Title:	Immune Oncology Biological Research
Version:	2.2.0
Description:	Provides six modules for tumor microenvironment (TME) analysis based on multi-omics data. These modules cover data preprocessing, TME estimation, TME infiltrating patterns, cellular interactions, genome and TME interaction, and visualization for TME relevant features, as well as modelling based on key features. It integrates multiple microenvironmental analysis algorithms and signature estimation methods, simplifying the analysis and downstream visualization of the TME. In addition to providing a quick and easy way to construct gene signatures from single-cell RNA-seq data, it also provides a way to construct a reference matrix for TME deconvolution from single-cell RNA-seq data. The analysis pipeline and feature visualization are user-friendly and provide a comprehensive description of the complex TME, offering insights into tumour-immune interactions (Zeng D, et al. (2024) <doi:10.1016/j.crmeth.2024.100910>. Fang Y, et al. (2025) <doi:10.1002/mdr2.70001>).
License:	GPL-3
URL:	https://doi.org/10.3389/fimmu.2021.687975 (paper), https://iobr.github.io/book/ (docs), https://iobr.github.io/IOBR/
BugReports:	https://github.com/IOBR/IOBR/issues
Depends:	R (≥ 3.6.0)
Imports:	cli, dplyr, ggplot2, glmnet, GSVA, methods, purrr, rlang, stringr, survival, survminer, tibble, tidyr
Suggests:	BiocParallel, biomaRt, circlize, clusterProfiler, ComplexHeatmap, corrplot, DESeq2, doParallel, DOSE, e1071, easier, enrichplot, factoextra, FactoMineR, foreach, ggdensity, ggpp, ggpubr, ggsci, gridExtra, Hmisc, knitr, limma, limSolve, maftools, MASS, Matrix, msigdbr, NbClust, org.Hs.eg.db, org.Mm.eg.db, patchwork, PMCMRplus, pracma, preprocessCore, prettydoc, pROC, psych, RColorBrewer, reshape2, rmarkdown, ROCR, sampling, scales, Seurat, SeuratObject, sva, testthat (≥ 3.0.0), tidyHeatmap, timeROC, webr, WGCNA
VignetteBuilder:	knitr
biocViews:	GeneExpression, DifferentialExpression, ImmunoOncology, Transcriptomics, Clustering, Survival, Visualization
Config/testthat/edition:	3
Encoding:	UTF-8
LazyData:	FALSE
RoxygenNote:	7.3.3
NeedsCompilation:	no
Packaged:	2026-04-22 13:01:08 UTC; wsx
Author:	Dongqiang Zeng [aut], Yiran Fang [aut], Shixiang Wang [aut, cre], Qingcong Luo [aut], Hongqian Qian [aut]
Maintainer:	Shixiang Wang <w_shixiang@163.com>
Repository:	CRAN
Date/Publication:	2026-04-22 14:00:18 UTC

Append Signatures to Expression Matrix

Description

Calculates mean expression for each marker feature set.

Usage

.appendSignatures(xp, markers)

Arguments

Value

Matrix with summarized expression values.

Build Mutation Matrices from MAF Data

Description

Build Mutation Matrices from MAF Data

Usage

.build_mut_matrices(mut, category)

Arguments

Value

List of mutation matrices.

Create Single Mutation Matrix

Description

Create Single Mutation Matrix

Usage

.create_mut_matrix(mut)

Arguments

Value

Mutation matrix.

Calculate AUC for Binomial Model

Description

Computes Area Under the ROC Curve (AUC) for model predictions using the ROCR package. Handles binary classification models from glmnet.

Usage

BinomialAUC(model, newx, s, acture.y)

Arguments

Value

Numeric AUC value between 0 and 1.

Examples

if (requireNamespace("glmnet", quietly = TRUE) &&
  requireNamespace("ROCR", quietly = TRUE)) {
  set.seed(123)
  train_data <- matrix(rnorm(100 * 5), ncol = 5)
  train_outcome <- rbinom(100, 1, 0.5)
  test_data <- matrix(rnorm(50 * 5), ncol = 5)
  test_outcome <- rbinom(50, 1, 0.5)
  fitted_model <- glmnet::cv.glmnet(train_data, train_outcome, family = "binomial", nfolds = 5)
  auc_value <- BinomialAUC(fitted_model, test_data, fitted_model$lambda.min, test_outcome)
  print(auc_value)
}

Binomial Model Construction

Description

Constructs and evaluates binomial logistic regression models using Lasso and Ridge regularization. Processes input data, scales features if specified, splits data into training/testing sets, and fits both Lasso and Ridge models. Optionally generates AUC plots for model evaluation.

Usage

BinomialModel(
  x,
  y,
  seed = 123456,
  scale = TRUE,
  train_ratio = 0.7,
  nfold = 10,
  plot = TRUE,
  palette = "jama",
  cols = NULL
)

Arguments

Value

List containing:

lasso_result

Lasso model results

ridge_result

Ridge model results

train.x

Training data with IDs

Author(s)

Dongqiang Zeng

Examples

set.seed(123)
x <- data.frame(
  ID = paste0("Sample", 1:50),
  Feature1 = rnorm(50),
  Feature2 = rnorm(50),
  Feature3 = rnorm(50)
)
y <- data.frame(
  ID = x$ID,
  Outcome = factor(rbinom(50, 1, 0.5))
)
result <- BinomialModel(x = x, y = y, plot = FALSE, nfold = 5)
str(result, max.level = 1)

CIBERSORT Deconvolution Algorithm

Description

An analytical tool to estimate cell type abundances in mixed cell populations using gene expression data.

Usage

CIBERSORT(
  sig_matrix = NULL,
  mixture_file,
  perm,
  QN = TRUE,
  absolute = FALSE,
  abs_method = "sig.score",
  parallel = FALSE,
  num_cores = 2,
  seed = NULL
)

Arguments

Value

A matrix object containing the estimated cibersort-cell fractions, p-values, correlation coefficients, and RMSE values.

Author(s)

Aaron M. Newman, Stanford University (amnewman@stanford.edu)

Examples

lm22 <- load_data("lm22")
common_genes <- rownames(lm22)[1:500]
sim_mixture <- as.data.frame(matrix(
  rnorm(length(common_genes) * 10, mean = 5, sd = 2),
  nrow = length(common_genes), ncol = 10
))
rownames(sim_mixture) <- common_genes
colnames(sim_mixture) <- paste0("Sample", 1:10)
result <- CIBERSORT(
  sig_matrix = lm22,
  mixture_file = sim_mixture,
  perm = 10, QN = FALSE, absolute = FALSE,
  parallel = FALSE
)
head(result)

Calculate Performance Metrics

Description

Computes True Positive Rate (TPR) and False Positive Rate (FPR) for ROC analysis using the ROCR package. Used internally for ROC curve generation.

Usage

CalculatePref(model, newx, s, acture.y)

Arguments

Value

ROCR performance object containing TPR and FPR values.

Examples

if (requireNamespace("glmnet", quietly = TRUE) &&
  requireNamespace("ROCR", quietly = TRUE)) {
  fitted_model <- glmnet::cv.glmnet(matrix(rnorm(100), ncol = 2), rbinom(50, 1, 0.5), nfolds = 3)
  perf <- CalculatePref(fitted_model, matrix(rnorm(20), ncol = 2), "lambda.min", rbinom(10, 1, 0.5))
}

Calculate Time-Dependent ROC Curve

Description

Computes time-dependent ROC curve for survival models using the 'timeROC' package. Evaluates predictive accuracy at a specified time quantile.

Usage

CalculateTimeROC(model, newx, s, acture.y, modelname, time_prob = 0.9)

Arguments

Value

An object of class 'timeROC' containing ROC curve information.

Author(s)

Dongqiang Zeng

Examples

if (requireNamespace("glmnet", quietly = TRUE) &&
  requireNamespace("survival", quietly = TRUE) &&
  requireNamespace("timeROC", quietly = TRUE)) {
  library(survival)
  dat <- na.omit(lung[, c("time", "status", "age", "sex", "ph.ecog")])
  dat$status <- dat$status - 1
  x <- as.matrix(dat[, c("age", "sex", "ph.ecog")])
  y <- Surv(dat$time, dat$status)
  fit <- glmnet::glmnet(x, y, family = "cox")
  actual_outcome <- data.frame(time = dat$time, status = dat$status)
  roc_info <- CalculateTimeROC(
    model = fit, newx = x, s = 0.01, acture.y = actual_outcome,
    modelname = "glmnet Cox Model", time_prob = 0.5
  )
  print(roc_info$AUC)
}

Construct Contrast Matrix

Description

Creates a contrast matrix for differential analysis, where each phenotype level is contrasted against all other levels combined.

Usage

Construct_con(pheno)

Arguments

Value

Square matrix with dimensions equal to the number of levels in 'pheno'. Each row represents a contrast where the corresponding level is compared against the average of others.

Examples

pheno <- factor(c("A", "B", "C", "D"))
contrast_matrix <- Construct_con(pheno)
print(contrast_matrix)

Convert Rowname To Loci

Description

Processes a gene expression data matrix by modifying its row names. Extracts the gene identifier from row names formatted as 'GENE|ID', simplifying them to 'GENE'.

Usage

ConvertRownameToLoci(cancerGeneExpression)

Arguments

Value

Matrix with modified gene expression data with updated row names. Rows without a valid identifier are removed.

Examples

example_data <- matrix(runif(20), ncol = 5)
rownames(example_data) <- c("LOC101", "LOC102", "LOC103", "LOC104")
processed_data <- ConvertRownameToLoci(example_data)
print(processed_data)

Core Algorithm for CIBERSORT Deconvolution

Description

Performs nu-regression using support vector machines (SVM) to estimate cell type proportions. This is the core computational engine of CIBERSORT, using nu-SVR with linear kernel to decompose mixed gene expression signals.

Usage

CoreAlg(X, y, absolute, abs_method)

Arguments

Value

List containing:

w

Estimated cell type weights/proportions

mix_rmse

Root mean squared error of the deconvolution

mix_r

Correlation coefficient between observed and predicted mixture

Examples

X <- matrix(rnorm(100), nrow = 10)
y <- rnorm(10)
result <- CoreAlg(X, y, absolute = FALSE, abs_method = "sig.score")

Constrained Least Squares Deconvolution

Description

Constrained Least Squares Deconvolution

Usage

DClsei(b, A, G, H, scaling)

Arguments

Value

Estimated cell fractions.

Robust Regression Deconvolution

Description

Robust Regression Deconvolution

Usage

DCrr(b, A, method, scaling)

Arguments

Value

Estimated cell fractions.

Draw QQ Plot Comparing Cancer and Immune Expression

Description

Creates a quantile-quantile (QQ) plot to compare gene expression distributions between cancer and immune samples. Points along the diagonal indicate similar distributions.

Usage

DrawQQPlot(cancer.exp, immune.exp, name = "")

Arguments

Value

Generates a QQ plot.

Examples

cancer_exp <- rnorm(100, mean = 5, sd = 1.5)
immune_exp <- rnorm(100, mean = 5, sd = 1.5)
DrawQQPlot(
  cancer.exp = cancer_exp,
  immune.exp = immune_exp,
  name = "Comparison of Gene Expression"
)

Estimate the proportion of immune and cancer cells.

Description

EPIC takes as input bulk gene expression data (RNA-seq) and returns the proportion of mRNA and cells composing the various samples.

Usage

EPIC(
  bulk,
  reference = NULL,
  mRNA_cell = NULL,
  mRNA_cell_sub = NULL,
  sigGenes = NULL,
  scaleExprs = TRUE,
  withOtherCells = TRUE,
  constrainedSum = TRUE,
  rangeBasedOptim = FALSE
)

Arguments

Details

This function uses a constrained least square minimization to estimate the proportion of each cell type with a reference profile and another uncharacterized cell type in bulk gene expression samples.

The names of the genes in the bulk samples, the reference samples and in the gene signature list need to be the same format (gene symbols are used in the predefined reference profiles). The full list of gene names don't need to be exactly the same between the reference and bulk samples: EPIC will use the intersection of the genes. In case of duplicate gene names, EPIC will use the median value per duplicate - if you want to consider these cases differently, you can remove the duplicates before calling EPIC.

Value

A list of 3 matrices: - 'mRNAProportions': (nSamples x (nCellTypes+1)) the proportion of mRNA coming from all cell types with a ref profile + the uncharacterized other cell - 'cellFractions': (nSamples x (nCellTypes+1)) this gives the proportion of cells from each cell type after accounting for the mRNA / cell value - 'fit.gof': (nSamples x 12) a matrix telling the quality for the fit of the signature genes in each sample. It tells if the minimization converged, and other info about this fit comparing the measured gene expression in the sigGenes vs predicted gene expression in the sigGenes

Examples

melanoma_data <- load_data("melanoma_data")
TRef <- load_data("TRef")
res1 <- EPIC(melanoma_data$counts)
res1$cellFractions
res2 <- EPIC(melanoma_data$counts, TRef)
res3 <- EPIC(bulk = melanoma_data$counts, reference = TRef)
res4 <- EPIC(melanoma_data$counts, reference = "TRef")
res5 <- EPIC(melanoma_data$counts, mRNA_cell_sub = c(
  Bcells = 1,
  otherCells = 5
))
# Various possible ways of calling EPIC function. res 1 to 4 should
# give exactly the same outputs, and the elements res1$cellFractions
# should be equal to the example predictions found in
# melanoma_data$cellFractions.pred for these first 4 results.
# The values of cellFraction for res5 will be different due to the use of
# other mRNA per cell values for the B and other cells.

Elastic Net Model Fitting

Description

Fits elastic net model with cross-validation to find optimal alpha and lambda. Searches across a grid of alpha values (0 to 1) and lambda values to minimize cross-validation error.

Usage

Enet(train.x, train.y, lambdamax, nfold = 10)

Arguments

Value

List containing:

chose_alpha

Optimal alpha value (0-1)

chose_lambda

Optimal lambda value

Examples

if (requireNamespace("glmnet", quietly = TRUE)) {
  set.seed(123)
  train_data <- matrix(rnorm(50 * 5), ncol = 5)
  train_outcome <- rbinom(50, 1, 0.5)
  result <- Enet(train.x = train_data, train.y = train_outcome, lambdamax = 1, nfold = 5)
}

Constrained Regression Method (Abbas et al., 2009)

Description

Implements a constrained regression approach described by Abbas et al. (2009). Estimates proportions of immune cell types within mixed cancer tissue samples based on gene expression data. Iteratively adjusts regression coefficients to ensure non-negative values.

Usage

GetFractions.Abbas(XX, YY, w = NA)

Arguments

Value

Vector with non-negative coefficients representing proportions of each cell type.

Examples

XX <- matrix(runif(100), nrow = 10, ncol = 10)
colnames(XX) <- paste("CellType", 1:10, sep = "")
YY <- runif(10)
results <- GetFractions.Abbas(XX, YY)
print(results)

Get Outlier Genes

Description

Identifies outlier genes from multiple cancer datasets. Treats the top 5 expressed genes in each sample as outliers and returns unique outlier genes.

Usage

GetOutlierGenes(cancers)

Arguments

Value

Vector of unique gene names identified as outliers.

Examples

tf1 <- tempfile(fileext = ".tsv")
tf2 <- tempfile(fileext = ".tsv")

expr1 <- data.frame(
  gene = c("GeneA", "GeneB", "GeneC", "GeneD", "GeneE", "GeneF"),
  Sample1 = c(10, 50, 30, 80, 60, 20),
  Sample2 = c(15, 40, 25, 90, 55, 10)
)

expr2 <- data.frame(
  gene = c("GeneA", "GeneB", "GeneC", "GeneD", "GeneE", "GeneF"),
  Sample3 = c(100, 20, 10, 60, 30, 80),
  Sample4 = c(95, 25, 15, 70, 35, 85)
)

write.table(expr1, tf1, sep = "\t", row.names = FALSE, quote = FALSE)
write.table(expr2, tf2, sep = "\t", row.names = FALSE, quote = FALSE)

cancers <- data.frame(ExpressionFiles = c(tf1, tf2))
outlier_genes <- GetOutlierGenes(cancers)

Calculate Immunophenoscore (IPS)

Description

Calculates Immunophenoscore (IPS) from gene expression data. IPS is a composite score measuring immunophenotype based on four major categories: MHC molecules, immunomodulators, effector cells, and suppressor cells.

Usage

IPS_calculation(project = NULL, eset, plot = FALSE)

Arguments

Value

Data frame containing:

MHC

MHC molecules score

EC

Effector cells score

SC

Suppressor cells score

CP

Checkpoints/Immunomodulators score

AZ

Aggregate score (sum of MHC, CP, EC, SC)

IPS

Immunophenoscore (0-10 scale)

Examples

# IPS requires gene symbols as rownames
# Create a simple example with gene symbols
example_genes <- c(
  "HLA-A", "HLA-B", "HLA-C", "CD274", "PDCD1", "CTLA4",
  "CD8A", "CD8B", "GZMB", "PRF1", "FOXP3", "IL10"
)
sim_eset <- as.data.frame(matrix(
  rnorm(length(example_genes) * 10, mean = 5, sd = 2),
  nrow = length(example_genes), ncol = 10
))
rownames(sim_eset) <- example_genes
colnames(sim_eset) <- paste0("Sample", 1:10)
ips_result <- IPS_calculation(eset = sim_eset, project = "Example", plot = FALSE)
head(ips_result)

Calculate Ligand-Receptor Interaction Scores

Description

Quantifies ligand-receptor interactions in the tumor microenvironment from bulk gene expression data using the easier package. This function processes raw counts or TPM data and computes interaction scores for each sample.

Usage

LR_cal(
  eset,
  data_type = c("count", "tpm"),
  id_type = "ensembl",
  cancer_type = "pancan"
)

Arguments

Value

Data frame containing ligand-receptor interaction scores with sample IDs as row names.

References

Lapuente-Santana, van Genderen, M., Hilbers, P., Finotello, F., & Eduati, F. (2021). Interpretable systems biomarkers predict response to immune-checkpoint inhibitors. Patterns (New York, N.Y.), 2(8), 100293. https://doi.org/10.1016/j.patter.2021.100293

Examples

# LR_cal requires HGNC gene symbols as rownames
# Create a simple example with gene symbols
example_genes <- c(
  "TGFB1", "EGFR", "VEGFA", "PDGFB", "FGF2", "CXCL12",
  "CXCR4", "IL6", "IL6R", "TNF", "TNFRSF1A", "IFNG"
)
sim_eset <- as.data.frame(matrix(
  rnorm(length(example_genes) * 10, mean = 5, sd = 2),
  nrow = length(example_genes), ncol = 10
))
rownames(sim_eset) <- example_genes
colnames(sim_eset) <- paste0("Sample", 1:10)

if (requireNamespace("easier", quietly = TRUE)) {
  lr <- LR_cal(eset = sim_eset, data_type = "tpm")
  head(lr)
}

MCP-counter Cell Population Abundance Estimation

Description

Estimates the abundance of different immune and stromal cell populations using the MCP-counter method. Works with various gene identifiers including Affymetrix probesets, HUGO gene symbols, Entrez IDs, and Ensembl IDs.

Usage

MCPcounter.estimate(
  expression,
  featuresType = c("affy133P2_probesets", "HUGO_symbols", "ENTREZ_ID", "ENSEMBL_ID"),
  probesets = read.table(system.file("extdata/probesets.txt", package = "IOBR"), sep =
    "\t", stringsAsFactors = FALSE, colClasses = "character"),
  genes = read.table(system.file("extdata/genes.txt", package = "IOBR"), sep = "\t",
    stringsAsFactors = FALSE, header = TRUE, colClasses = "character", check.names =
    FALSE)
)

Arguments

Value

Matrix with cell populations in rows and samples in columns.

Author(s)

Etienne Becht

Examples

expr <- matrix(runif(1000), nrow = 100, ncol = 10)
rownames(expr) <- paste0("Gene", 1:100)
estimates <- MCPcounter.estimate(expr, featuresType = "HUGO_symbols")

Parse Input Gene Expression Data

Description

Reads gene expression data from a tab-delimited text file, using the first column as row names. Converts data into a numeric matrix for analysis.

Usage

ParseInputExpression(path)

Arguments

Value

Numeric matrix of gene expression values with genes as rows and samples as columns.

Examples

tf <- tempfile(fileext = ".tsv")
expr <- data.frame(
  gene = c("GeneA", "GeneB", "GeneC"),
  Sample1 = c(10, 20, 30),
  Sample2 = c(15, 25, 35)
)
write.table(expr, tf, sep = "\t", row.names = FALSE, quote = FALSE)
gene_expression_data <- ParseInputExpression(tf)
print(gene_expression_data)

Plot AUC ROC Curves

Description

Generates ROC curves for model evaluation comparing training and testing performance at both lambda.min and lambda.1se. Creates a ggplot visualization with AUC values in the legend.

Usage

PlotAUC(
  train.x,
  train.y,
  test.x,
  test.y,
  model,
  modelname,
  cols = NULL,
  palette = "jama"
)

Arguments

Value

ggplot object of ROC curves.

Examples

if (requireNamespace("glmnet", quietly = TRUE)) {
  set.seed(123)
  train_data <- matrix(rnorm(100 * 5), ncol = 5)
  train_outcome <- rbinom(100, 1, 0.5)
  test_data <- matrix(rnorm(50 * 5), ncol = 5)
  test_outcome <- rbinom(50, 1, 0.5)
  fitted_model <- glmnet::cv.glmnet(train_data, train_outcome, family = "binomial", nfolds = 5)
  p <- PlotAUC(train_data, train_outcome, test_data, test_outcome, fitted_model, "MyModel")
  print(p)
}

Plot Time-Dependent ROC Curves

Description

Generates time-dependent ROC curves for evaluating prognostic accuracy of survival models. Plots training and testing ROC curves at the 90th percentile survival time.

Usage

PlotTimeROC(
  train.x,
  train.y,
  test.x,
  test.y,
  model,
  modelname,
  cols = NULL,
  palette = "jama"
)

Arguments

Value

A 'ggplot' object representing the ROC curve plot.

Author(s)

Dongqiang Zeng

Examples

if (requireNamespace("glmnet", quietly = TRUE) &&
  requireNamespace("survival", quietly = TRUE) &&
  requireNamespace("timeROC", quietly = TRUE)) {
  library(survival)
  set.seed(123)
  train_x <- matrix(rnorm(100 * 5), ncol = 5)
  train_y <- data.frame(time = rexp(100), status = rbinom(100, 1, 0.5))
  test_x <- matrix(rnorm(50 * 5), ncol = 5)
  test_y <- data.frame(time = rexp(50), status = rbinom(50, 1, 0.5))
  fit <- glmnet::cv.glmnet(train_x, Surv(train_y$time, train_y$status), family = "cox")
  p <- PlotTimeROC(train_x, train_y, test_x, test_y, fit, "Cox Model")
  print(p)
}

Process Data for Model Construction

Description

Preprocesses data for binomial or survival analysis. Aligns and filters data based on sample IDs, optionally scales data, and ensures appropriate data types. Handles missing values by removing columns with NA values.

Usage

ProcessingData(x, y, scale, type = c("binomial", "survival"))

Arguments

Value

List containing:

x_scale

Processed predictor matrix

y

Processed outcome variable

x_ID

Sample IDs

Examples

x <- data.frame(ID = 1:10, predictor1 = rnorm(10), predictor2 = rnorm(10))
y <- data.frame(ID = 1:10, outcome = sample(c(0, 1), 10, replace = TRUE))
result <- ProcessingData(x, y, scale = TRUE, type = "binomial")

Calculate Time-Dependent AUC for Survival Models

Description

Evaluates prognostic ability of a survival model by calculating time-dependent AUC at the 30th and 90th percentiles of survival time. These thresholds assess short-term and long-term predictive accuracy.

Usage

PrognosticAUC(model, newx, s, acture.y)

Arguments

Value

A data frame with AUC values at 30th ('probs.3') and 90th ('probs.9') percentiles.

Author(s)

Dongqiang Zeng

Examples

if (requireNamespace("glmnet", quietly = TRUE) &&
  requireNamespace("survival", quietly = TRUE) &&
  requireNamespace("timeROC", quietly = TRUE)) {
  library(survival)
  set.seed(123)
  x <- matrix(rnorm(100 * 5), ncol = 5)
  y <- Surv(rexp(100), rbinom(100, 1, 0.5))
  fit <- glmnet::cv.glmnet(x, y, family = "cox")
  acture_y <- data.frame(time = y[, 1], status = y[, 2])
  auc_results <- PrognosticAUC(fit, newx = x, s = "lambda.min", acture.y = acture_y)
}

Build Prognostic Models Using LASSO and Ridge Regression

Description

Prepares data, splits it into training and testing sets, and fits LASSO and Ridge regression models for survival analysis. Evaluates model performance using cross-validation and optionally generates time-dependent ROC curves for visual assessment of predictive accuracy.

Usage

PrognosticModel(
  x,
  y,
  scale = FALSE,
  seed = 123456,
  train_ratio = 0.7,
  nfold = 10,
  plot = TRUE,
  palette = "jama",
  cols = NULL
)

Arguments

Value

A list containing:

lasso_result

Results from LASSO model including coefficients and AUC

ridge_result

Results from Ridge model including coefficients and AUC

train.x

Training data with sample IDs

Author(s)

Dongqiang Zeng

Examples

if (requireNamespace("glmnet", quietly = TRUE) &&
  requireNamespace("survival", quietly = TRUE)) {
  library(survival)
  imvigor210_sig <- load_data("imvigor210_sig")
  imvigor210_pdata <- load_data("imvigor210_pdata")
  pdata_prog <- data.frame(
    ID = imvigor210_pdata$ID,
    OS_days = as.numeric(imvigor210_pdata$OS_days),
    OS_status = as.numeric(imvigor210_pdata$OS_status)
  )
  prognostic_result <- PrognosticModel(
    x = imvigor210_sig, y = pdata_prog,
    scale = TRUE, seed = 123456,
    train_ratio = 0.7, nfold = 10, plot = FALSE
  )
}

Compute Prognostic Results for Survival Models

Description

Computes and compiles prognostic results from a survival model fitted with 'glmnet'. Extracts model coefficients at optimal lambda values ('lambda.min' and 'lambda.1se') and calculates time-dependent AUC metrics for both training and testing datasets.

Usage

PrognosticResult(model, train.x, train.y, test.x, test.y)

Arguments

Value

A list containing:

model

The fitted model object

coefs

Data frame of coefficients at 'lambda.min' and 'lambda.1se'

AUC

Data frame with AUC values for train/test at both lambda values

Author(s)

Dongqiang Zeng

Examples

if (requireNamespace("glmnet", quietly = TRUE) &&
  requireNamespace("survival", quietly = TRUE) &&
  requireNamespace("timeROC", quietly = TRUE)) {
  library(survival)
  set.seed(123)
  train_x <- matrix(rnorm(100 * 10), ncol = 10)
  train_y <- data.frame(time = rexp(100), status = rbinom(100, 1, 0.5))
  test_x <- matrix(rnorm(50 * 10), ncol = 10)
  test_y <- data.frame(time = rexp(50), status = rbinom(50, 1, 0.5))
  fit <- glmnet::cv.glmnet(train_x, Surv(train_y$time, train_y$status), family = "cox")
  results <- PrognosticResult(
    model = fit, train.x = train_x, train.y = train_y,
    test.x = test_x, test.y = test_y
  )
}

Regression Result Computation

Description

Computes regression results with coefficients at lambda.min and lambda.1se, and evaluates AUC for binomial outcomes. Returns a comprehensive summary of model performance on both training and testing datasets.

Usage

RegressionResult(train.x, train.y, test.x, test.y, model)

Arguments

Value

List containing:

model

The fitted cv.glmnet model

coefs

Data frame with feature names and coefficients at lambda.min and lambda.1se

AUC

Matrix of AUC values for train/test sets at both lambda values

Examples

if (requireNamespace("glmnet", quietly = TRUE)) {
  set.seed(123)
  train_data <- matrix(rnorm(100 * 10), ncol = 10)
  train_outcome <- rbinom(100, 1, 0.5)
  test_data <- matrix(rnorm(50 * 10), ncol = 10)
  test_outcome <- rbinom(50, 1, 0.5)
  fitted_model <- glmnet::cv.glmnet(train_data, train_outcome, family = "binomial", nfolds = 5)
  results <- RegressionResult(
    train.x = train_data, train.y = train_outcome,
    test.x = test_data, test.y = test_outcome, model = fitted_model
  )
}

Remove Batch Effect of Expression Set

Description

Removes batch effects between two gene expression datasets, typically representing different sample types such as cancer cells and immune cells. Uses ComBat from the sva package for batch correction.

Usage

RemoveBatchEffect(cancer.exp, immune.exp, immune.cellType)

Arguments

Value

A list containing:

1

Batch effect corrected cancer expression data

2

Batch effect corrected immune expression data

3

Aggregated immune expression data (median per cell type)

Author(s)

Bo Li

Examples

set.seed(123)
gene_names <- paste0("Gene", 1:100)
sample_names_cancer <- paste0("CancerSample", 1:10)
cancer.exp <- matrix(runif(1000, 1, 1000),
  nrow = 100, ncol = 10,
  dimnames = list(gene_names, sample_names_cancer)
)

sample_names_immune <- paste0("ImmuneSample", 1:5)
immune.exp <- matrix(runif(500, 1, 1000),
  nrow = 100, ncol = 5,
  dimnames = list(gene_names, sample_names_immune)
)

immune.cellType <- c("T-cell", "B-cell", "T-cell", "NK-cell", "B-cell")
names(immune.cellType) <- sample_names_immune

result <- RemoveBatchEffect(cancer.exp, immune.exp, immune.cellType)

Split Data into Training and Testing Sets

Description

Divides dataset into training and testing sets using random sampling. Maintains data integrity for both binomial and survival analysis types.

Usage

SplitTrainTest(x, y, train_ratio, type = c("binomial", "survival"), seed)

Arguments

Value

List containing:

train.x

Training predictors matrix

train.y

Training outcomes

test.x

Testing predictors matrix

test.y

Testing outcomes

train_sample

Indices of training samples

Examples

data_matrix <- matrix(rnorm(200), ncol = 2)
outcome_vector <- rbinom(100, 1, 0.5)
split_data <- SplitTrainTest(
  data_matrix, outcome_vector,
  train_ratio = 0.7,
  type = "binomial", seed = 123
)

Top Probe Selector

Description

Extracts the top 'i' probes based on their ordering in the provided data frame. If the number of rows is less than or equal to 'i', returns all probes.

Usage

Top_probe(dat, i)

Arguments

Value

Character vector containing the names of the top 'i' probes.

Examples

dat <- data.frame(
  probe = c("Probe1", "Probe2", "Probe3", "Probe4", "Probe5"),
  value = c(5, 3, 2, 4, 1)
)
top_probes <- Top_probe(dat, 3)
print(top_probes)

Add Custom Download Mirror

Description

Adds a custom mirror URL to the default mirrors for the current session. The mirror URL should be a base URL that will be prepended to GitHub paths.

Usage

add_iobr_mirror(url, position = c("first", "last", "before_github"))

Arguments

Value

Invisibly returns the updated mirror list.

Examples

# Add a custom mirror to try first
add_iobr_mirror("https://my-mirror.com/https://github.com")

# Add mirror to try before default GitHub
add_iobr_mirror("https://fast-mirror.org", position = "before_github")

# Download with the new mirror
data <- download_iobr_data("BRef")

Add Risk Score to Dataset

Description

Computes a risk score for each observation based on Cox proportional hazards regression or binary logistic regression. The function fits the specified model and returns the dataset with an added risk score column.

Usage

add_riskscore(
  input,
  family = c("cox", "binary"),
  target = NULL,
  time = NULL,
  status = NULL,
  vars,
  new_var_name = "riskscore"
)

Arguments

Value

Data frame identical to 'input' with an additional column containing risk scores (linear predictors for Cox models or predicted probabilities for logistic models).

Author(s)

Dongqiang Zeng

Examples

set.seed(123)
input_data <- data.frame(
  time = rexp(100),
  status = rbinom(100, 1, 0.5),
  age = rnorm(100, 60, 10),
  score1 = rnorm(100),
  score2 = rnorm(100)
)
result <- add_riskscore(
  input_data,
  time = "time", status = "status",
  vars = c("age", "score1", "score2")
)
head(result$riskscore)

Annotate Gene Expression Matrix and Remove Duplicated Genes

Description

Annotates an expression matrix with gene symbols using provided annotation data, filters out missing or invalid symbols, handles duplicate gene entries, and removes uninformative rows. The function supports multiple aggregation methods for resolving duplicate gene symbols.

Usage

anno_eset(
  eset,
  annotation,
  symbol = "symbol",
  probe = "probe_id",
  method = "mean"
)

Arguments

Details

The function performs the following operations:

1. Filters probes with missing symbols or labeled as '"NA_NA"'

2. Matches probes between expression set and annotation data

3. Merges annotation with expression data

4. Handles duplicate gene symbols using specified aggregation method

5. Removes rows with all zeros, all NAs, or missing values in the first column

Value

Annotated and cleaned gene expression matrix with gene symbols as row names.

Author(s)

Dongqiang Zeng

Examples

# Annotate Affymetrix microarray data
eset_gse62254 <- load_data("eset_gse62254")
anno_hug133plus2 <- load_data("anno_hug133plus2")
eset <- anno_eset(eset = eset_gse62254, annotation = anno_hug133plus2)
head(eset)

# Annotate RNA-seq data with Ensembl IDs
eset_stad <- load_data("eset_stad")
anno_grch38 <- load_data("anno_grch38")
eset <- anno_eset(eset = eset_stad, annotation = anno_grch38, probe = "id")
head(eset)

Harmonize Two Data Frames by Column Structure

Description

Adds missing columns (filled with 'NA') to a secondary data frame so that its column set and order match a reference data frame. This is useful when combining data frames from different sources that should have the same structure but may be missing some columns.

Usage

assimilate_data(data_a, data_b)

Arguments

Value

Data frame 'data_b' with added missing columns (NA-filled) and reordered to match 'data_a'.

Examples

# Create reference data frame
pdata_a <- data.frame(
  A = 1:5, B = 2:6, C = 3:7, D = 4:8, E = 5:9
)

# Create data frame with subset of columns
pdata_b <- data.frame(A = 1:3, C = 4:6, E = 7:9)

# Harmonize pdata_b to match pdata_a structure
pdata_b_harmonized <- assimilate_data(data_a = pdata_a, data_b = pdata_b)
print(names(pdata_b_harmonized)) # Now has A, B, C, D, E

Batch Correlation Analysis

Description

Performs correlation analysis between a target variable and multiple feature variables. Computes correlation coefficients, p-values, and adjusts for multiple testing using the Benjamini-Hochberg method.

Usage

batch_cor(data, target, feature, method = c("spearman", "pearson", "kendall"))

Arguments

Value

Tibble containing the following columns for each feature:

sig_names

Feature name

p.value

Raw p-value

statistic

Correlation coefficient

p.adj

Adjusted p-value (Benjamini-Hochberg)

log10pvalue

Negative log10-transformed p-value

stars

Significance stars: **** p<0.0001, *** p<0.001, ** p<0.01, * p<0.05, + p<0.5

Author(s)

Dongqiang Zeng

Examples

# Load TCGA-STAD signature data
sig_stad <- load_data("sig_stad")

# Perform batch correlation
results <- batch_cor(
  data = sig_stad,
  target = "CD_8_T_effector",
  feature = colnames(sig_stad)[69:ncol(sig_stad)]
)
head(results)

Batch Kruskal-Wallis Test

Description

Performs Kruskal-Wallis rank sum tests on multiple continuous features across different groups. Computes p-values, adjusts for multiple testing, and ranks features by significance.

Usage

batch_kruskal(data, group, feature = NULL, feature_manipulation = FALSE)

Arguments

Value

Tibble containing:

sig_names

Feature name

p.value

Raw p-value from Kruskal-Wallis test

statistic

Test statistic (chi-squared)

p.adj

Adjusted p-value (Benjamini-Hochberg)

log10pvalue

Negative log10-transformed p-value

stars

Significance stars: **** p<0.0001, *** p<0.001, ** p<0.01, * p<0.05, + p<0.5

group columns

Mean-centered values for each group

Author(s)

Dongqiang Zeng

Examples

# Load TCGA-STAD signature data
sig_stad <- load_data("sig_stad")

# Test features by gender (if available in your dataset)
if ("Gender" %in% colnames(sig_stad)) {
  res <- batch_kruskal(
    data = sig_stad,
    group = "Gender",
    feature = colnames(sig_stad)[69:ncol(sig_stad)]
  )
  head(res)
}

Batch Calculation of Partial Correlation Coefficients

Description

Computes partial correlation coefficients between multiple features and a target variable while controlling for an interference (confounding) variable. Adjusts p-values for multiple testing using the Benjamini-Hochberg method.

Usage

batch_pcc(
  input,
  interferenceid,
  target,
  features,
  method = c("pearson", "spearman", "kendall")
)

Arguments

Value

Tibble containing the following columns for each feature:

sig_names

Feature name

p.value

Raw p-value

statistic

Partial correlation coefficient

p.adj

Adjusted p-value (Benjamini-Hochberg method)

log10pvalue

Negative log10-transformed p-value

stars

Significance stars: **** p.adj<0.0001, *** p.adj<0.001, ** p.adj<0.01, * p.adj<0.05, + p.adj<0.5

Author(s)

Rongfang Shen

Examples

# Load TCGA-STAD signature data
sig_stad <- load_data("sig_stad")

# Calculate partial correlations controlling for tumor purity
res <- batch_pcc(
  input = sig_stad,
  interferenceid = "TumorPurity_estimate",
  target = "Pan_F_TBRs",
  method = "pearson",
  features = colnames(sig_stad)[70:ncol(sig_stad)]
)
head(res)

Batch Signature Survival Plot

Description

Generates Kaplan-Meier survival plots for multiple projects or cohorts based on signature scores. Automatically determines optimal cutpoints for signature stratification and creates publication-ready survival curves.

Usage

batch_sig_surv_plot(
  input_pdata,
  signature,
  id = "ID",
  column_of_project = "ProjectID",
  project = NULL,
  time = "time",
  status = "status",
  time_type = "day",
  break_month = "auto",
  palette = "jama",
  cols = NULL,
  mini_sig = "score",
  save_path = NULL,
  show_col = TRUE,
  fig_type = "pdf"
)

Arguments

Value

Data frame containing combined survival analysis results from all projects.

Author(s)

Dongqiang Zeng

Examples

sig_stad <- load_data("sig_stad")
result <- batch_sig_surv_plot(
  input_pdata = sig_stad,
  signature = "T.cells.CD8",
  id = "ID",
  column_of_project = "ProjectID",
  project = NULL,
  time = "OS_time",
  status = "OS_status",
  time_type = "month",
  break_month = "auto",
  palette = "jama",
  cols = NULL,
  mini_sig = "score",
  show_col = TRUE,
  fig_type = "pdf"
)

Batch Survival Analysis

Description

Performs Cox proportional hazards regression analysis on multiple variables. Optionally determines optimal cutoffs to dichotomize continuous predictors before modeling. Returns hazard ratios, confidence intervals, and p-values for each variable.

Usage

batch_surv(
  pdata,
  variable,
  time = "time",
  status = "status",
  best_cutoff = FALSE
)

Arguments

Value

Data frame containing hazard ratios (HR), 95 and p-values for each variable, sorted by p-value.

Author(s)

Dongqiang Zeng

Examples

sig_stad <- load_data("sig_stad")
batch_surv(
  pdata = sig_stad,
  variable = colnames(sig_stad)[69:ncol(sig_stad)],
  time = "OS_time",
  status = "OS_status"
)

Batch Wilcoxon Rank-Sum Test Between Two Groups

Description

Performs Wilcoxon rank-sum tests (Mann-Whitney U tests) to compare the distribution of specified features between two groups. Computes p-values, adjusts for multiple testing, and ranks features by significance.

Usage

batch_wilcoxon(
  data,
  target = "group",
  feature = NULL,
  feature_manipulation = FALSE
)

Arguments

Value

Tibble with columns:

sig_names

Feature name

p.value

Raw p-value

statistic

Difference in means between groups

p.adj

Adjusted p-value (Benjamini-Hochberg)

log10pvalue

Negative log10-transformed p-value

stars

Significance stars: **** p<0.0001, *** p<0.001, ** p<0.01, * p<0.05, + p<0.5

group1, group2

Mean values for each group

Author(s)

Dongqiang Zeng

Examples

# Load TCGA-STAD signature data
sig_stad <- load_data("sig_stad")

# Compare features by gender
res <- batch_wilcoxon(
  data = sig_stad,
  target = "Gender",
  feature = colnames(sig_stad)[69:ncol(sig_stad)]
)
head(res)

Extract Best Cutoff and Add Binary Variable to Data Frame

Description

Determines the optimal cutoff point for a continuous variable in survival analysis using the maximally selected rank statistics method. Creates a binary variable based on the identified cutoff and adds it to the input data frame.

Usage

best_cutoff(
  pdata,
  variable,
  time = "time",
  status = "status",
  print_result = TRUE
)

Arguments

Value

Data frame identical to 'pdata' with an additional binary column named '<variable>_binary' containing "High" and "Low" categories based on the optimal cutoff.

Author(s)

Dongqiang Zeng

Examples

set.seed(123)
pdata <- data.frame(
  time = rexp(100),
  status = rbinom(100, 1, 0.5),
  score = rnorm(100, mean = 50, sd = 10)
)
result <- best_cutoff(pdata, variable = "score", print_result = FALSE)
table(result$score_binary)

Extract Best Cutoff and Add Binary Variable to Data Frame

Description

Finds the best cutoff point for a continuous variable in survival analysis. Takes input data containing a continuous variable and survival information (time and status). Returns the modified input data with a new binary variable created based on the best cutoff point.

Usage

best_cutoff2(
  pdata,
  variable,
  time = "time",
  status = "status",
  print_result = TRUE
)

Arguments

Value

List containing:

pdata

Data frame with binary variable added

best_cutoff

Numeric cutoff value

cox_continuous_object

Cox model summary for continuous variable

summary_binary_variable

Summary of binary variable

cox_binary_object

Cox model summary for binary variable

Author(s)

Dongqiang Zeng

Examples

set.seed(123)
pdata <- data.frame(
  time = rexp(100),
  status = rbinom(100, 1, 0.5),
  score = rnorm(100, mean = 50, sd = 10)
)
result <- best_cutoff2(pdata, variable = "score", print_result = FALSE)
result$best_cutoff

Break Time Into Blocks

Description

Divides time duration into specified blocks for analysis.

Usage

calculate_break_month(input, block = 6, time_type = c("month", "day"))

Arguments

Value

Numeric vector of breakpoints, rounded to nearest multiple of 5.

Author(s)

Dongqiang Zeng

Examples

time_data <- c(24, 36, 12, 48)
blocks <- calculate_break_month(input = time_data)

Calculate Signature Score

Description

Main interface for calculating signature scores from gene expression data. Supports PCA, z-score, ssGSEA, and integration methods.

Usage

calculate_sig_score(
  pdata = NULL,
  eset,
  signature = NULL,
  method = c("pca", "ssgsea", "zscore", "integration"),
  mini_gene_count = 3,
  column_of_sample = "ID",
  print_gene_proportion = FALSE,
  print_filtered_signatures = FALSE,
  adjust_eset = FALSE,
  parallel.size = 1L,
  ...
)

Arguments

Value

Tibble with phenotype data and signature scores.

Author(s)

Dongqiang Zeng

References

1. Hänzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis. BMC Bioinformatics. 2013;14:7.

2. Mariathasan S, et al. TGF\beta attenuates tumour response to PD-L1 blockade. Nature. 2018;554:544-548.

Examples

set.seed(123)
eset <- matrix(rnorm(1000), nrow = 100, ncol = 10)
rownames(eset) <- paste0("Gene", 1:100)
colnames(eset) <- paste0("Sample", 1:10)
signature <- list(
  Signature1 = paste0("Gene", 1:10),
  Signature2 = paste0("Gene", 11:20)
)

result <- calculate_sig_score(eset = eset, signature = signature, method = "pca")

Calculate Signature Score Using Integration Method

Description

Computes signature scores using PCA, z-score, and ssGSEA methods combined.

Usage

calculate_sig_score_integration(
  pdata = NULL,
  eset,
  signature,
  mini_gene_count = 2,
  column_of_sample = "ID",
  adjust_eset = FALSE,
  parallel.size = 1L
)

Arguments

Value

Tibble with signature scores from all three methods.

Author(s)

Dongqiang Zeng

Examples

set.seed(123)
eset <- matrix(rnorm(1000), nrow = 100, ncol = 10)
rownames(eset) <- paste0("Gene", 1:100)
colnames(eset) <- paste0("Sample", 1:10)
signature <- list(
  Signature1 = paste0("Gene", 1:15),
  Signature2 = paste0("Gene", 16:30)
)

result <- calculate_sig_score_integration(eset = eset, signature = signature)

Calculate Signature Score Using PCA Method

Description

Computes signature scores using Principal Component Analysis. The first principal component is used as the signature score.

Usage

calculate_sig_score_pca(
  pdata = NULL,
  eset,
  signature,
  mini_gene_count = 3,
  column_of_sample = "ID",
  adjust_eset = FALSE
)

Arguments

Value

Tibble with signature scores.

Author(s)

Dongqiang Zeng

Examples

set.seed(123)
eset <- matrix(rnorm(1000), nrow = 100, ncol = 10)
rownames(eset) <- paste0("Gene", 1:100)
colnames(eset) <- paste0("Sample", 1:10)
signature <- list(
  Signature1 = paste0("Gene", 1:10),
  Signature2 = paste0("Gene", 11:20)
)

result <- calculate_sig_score_pca(eset = eset, signature = signature)

Calculate Signature Score Using ssGSEA Method

Description

Computes signature scores using single-sample Gene Set Enrichment Analysis.

Usage

calculate_sig_score_ssgsea(
  pdata = NULL,
  eset,
  signature,
  mini_gene_count = 5,
  column_of_sample = "ID",
  adjust_eset = FALSE,
  parallel.size = 1L
)

Arguments

Value

Tibble with signature scores.

Author(s)

Dongqiang Zeng

Examples

set.seed(123)
eset <- matrix(rnorm(1000), nrow = 100, ncol = 10)
rownames(eset) <- paste0("Gene", 1:100)
colnames(eset) <- paste0("Sample", 1:10)
signature <- list(
  Signature1 = paste0("Gene", 1:15),
  Signature2 = paste0("Gene", 16:30)
)

result <- calculate_sig_score_ssgsea(eset = eset, signature = signature)

Calculate Signature Score Using Z-Score Method

Description

Computes signature scores using z-score transformation.

Usage

calculate_sig_score_zscore(
  pdata = NULL,
  eset,
  signature,
  mini_gene_count = 3,
  column_of_sample = "ID",
  adjust_eset = FALSE
)

Arguments

Value

Tibble with signature scores.

Author(s)

Dongqiang Zeng

Examples

set.seed(123)
eset <- matrix(rnorm(1000), nrow = 100, ncol = 10)
rownames(eset) <- paste0("Gene", 1:100)
colnames(eset) <- paste0("Sample", 1:10)
signature <- list(
  Signature1 = paste0("Gene", 1:10),
  Signature2 = paste0("Gene", 11:20)
)

result <- calculate_sig_score_zscore(eset = eset, signature = signature)

Visualize Cell Fractions as Stacked Bar Chart

Description

Creates stacked bar charts to visualize tumor microenvironment (TME) cell fractions. Supports batch visualization of deconvolution results from methods such as CIBERSORT, EPIC, and quanTIseq.

Usage

cell_bar_plot(
  input,
  id = "ID",
  title = "Cell Fraction",
  features = NULL,
  pattern = NULL,
  legend.position = "bottom",
  coord_flip = TRUE,
  palette = 3,
  show_col = FALSE,
  cols = NULL
)

Arguments

Value

A ggplot2 object representing the stacked bar chart.

Author(s)

Dongqiang Zeng

Examples

set.seed(123)
input_data <- data.frame(
  ID = paste0("Sample", 1:10),
  Cell_A = runif(10, 0, 0.4),
  Cell_B = runif(10, 0, 0.3),
  Cell_C = runif(10, 0, 0.3)
)
cell_bar_plot(input = input_data, id = "ID", features = c("Cell_A", "Cell_B", "Cell_C"))

Process Batch Table and Validate Cancer Types

Description

Processes input data containing cancer types and validates each category against a predefined list of supported cancer types ('timer_available_cancers').

Usage

check_cancer_types(args)

Arguments

Value

A character matrix with two columns:

Column 1

Expression identifiers

Column 2

Cancer categories

Examples

args <- list(
  expression = c("exp1", "exp2"),
  category = c("luad", "brca"),
  batch = NULL
)
result <- check_cancer_types(args)

Check Integrity and Outliers of Expression Set

Description

Performs quality checks on an expression matrix to identify missing values, infinite values, and features with zero variance. Issues warnings when potential problems are detected that may affect downstream analyses.

Usage

check_eset(eset, print_result = FALSE, estimate_sd = FALSE)

Arguments

Value

Invisibly returns 'NULL'. Function is called for its side effects (printing messages and issuing warnings).

Author(s)

Dongqiang Zeng

Examples

set.seed(123)
eset <- matrix(rnorm(1000), nrow = 100, ncol = 10)
rownames(eset) <- paste0("Gene", 1:100)
colnames(eset) <- paste0("Sample", 1:10)
check_eset(eset, print_result = TRUE)

Clear IOBR Data Cache

Description

Removes all cached data files downloaded from GitHub.

Usage

clear_iobr_cache()

Value

Invisible NULL. Called for side effects of clearing the cache.

Combine Phenotype Data and Expression Set

Description

Merges phenotype data with an expression matrix by matching sample IDs. Optionally filters features, applies feature manipulation, and scales expression data before combining.

Usage

combine_pd_eset(
  eset,
  pdata,
  id_pdata = "ID",
  feas = NULL,
  feature_manipulation = TRUE,
  scale = TRUE,
  choose_who_when_duplicate = c("eset", "pdata")
)

Arguments

Value

Data frame combining phenotype data and (transposed) expression data, with samples in rows and features/phenotypes in columns.

Author(s)

Dongqiang Zeng

Examples

set.seed(123)
eset <- matrix(rnorm(1000), nrow = 100, ncol = 10)
rownames(eset) <- paste0("Gene", 1:100)
colnames(eset) <- paste0("Sample", 1:10)
pdata <- data.frame(
  ID = colnames(eset),
  group = rep(c("A", "B"), each = 5),
  age = rnorm(10, 50, 10)
)
result <- combine_pd_eset(eset = eset, pdata = pdata, scale = FALSE)
dim(result)

Convert Read Counts to Transcripts Per Million (TPM)

Description

Transforms gene expression count data into Transcripts Per Million (TPM) values, normalizing for gene length and library size. Supports multiple gene ID types and can retrieve gene length information from BioMart or use local datasets.

Usage

count2tpm(
  countMat,
  idType = "Ensembl",
  org = c("hsa", "mmus"),
  source = c("local", "biomart"),
  effLength = NULL,
  id = "id",
  gene_symbol = "symbol",
  length = "eff_length",
  check_data = FALSE
)

Arguments

Value

Data frame of TPM-normalized expression values with genes in rows and samples in columns. Gene identifiers are converted to gene symbols in the output, regardless of the input 'idType'.

Author(s)

Wubing Zhang, Dongqiang Zeng, Yiran Fang

Examples

# Load TCGA count data
eset_stad <- load_data("eset_stad")

# Transform to TPM using local gene annotation
eset <- count2tpm(countMat = eset_stad, source = "local", idType = "ensembl")
head(eset)

Create Nested Output Folders

Description

Creates one to three nested folders (if not existing) under the current working directory and returns their names and absolute paths.

Usage

creat_folder(f1, f2 = NULL, f3 = NULL, return = NULL)

Arguments

Value

List with elements:

folder_name

Relative path to the created folder

abspath

Absolute path ending with '/'

Examples

creat_folder(file.path(tempdir(), "1-result"))
creat_folder(file.path(tempdir(), "1-result"), "figures", "correlation")

Deconvolve Using CIBERSORT

Description

CIBERSORT is freely available to academic users. License and binary can be obtained from https://cibersortx.stanford.edu.

Usage

deconvo_cibersort(
  eset,
  project = NULL,
  arrays = FALSE,
  perm = 1000,
  absolute = FALSE,
  abs_method = "sig.score",
  parallel = FALSE,
  num_cores = 2,
  seed = NULL
)

Arguments

Value

Data frame with CIBERSORT cell fractions. Columns suffixed with '_CIBERSORT'.

Author(s)

Dongqiang Zeng

Examples

eset_tme_stad <- load_data("eset_tme_stad")
lm22 <- load_data("lm22")

cibersort_result <- deconvo_cibersort(
  eset = eset_tme_stad,
  project = "TCGA-STAD",
  perm = 100
)

Deconvolve Immune Microenvironment Using EPIC

Description

Estimates immune cell fractions using EPIC algorithm.

Usage

deconvo_epic(eset, project = NULL, tumor = TRUE)

Arguments

Value

Data frame with EPIC cell fraction estimates. Columns suffixed with '_EPIC'.

Author(s)

Dongqiang Zeng

Examples

eset_stad <- load_data("eset_stad")
anno_grch38 <- load_data("anno_grch38")
eset <- anno_eset(eset = eset_stad, annotation = anno_grch38, probe = "id")
eset <- eset[1:500, 1:5]
epic_result <- deconvo_epic(eset = eset, project = "Example", tumor = TRUE)
head(epic_result)

Calculate ESTIMATE Scores

Description

Calculates stromal, immune, and ESTIMATE scores from gene expression.

Usage

deconvo_estimate(eset, project = NULL, platform = "affymetrix")

Arguments

Value

Data frame with ESTIMATE scores. Columns suffixed with '_estimate'.

Author(s)

Dongqiang Zeng

Examples

eset_stad <- load_data("eset_stad")
anno_grch38 <- load_data("anno_grch38")
eset <- anno_eset(eset = eset_stad, annotation = anno_grch38, probe = "id")

estimate_result <- deconvo_estimate(eset, project = "TCGA-STAD")

Calculate Immunophenoscore (IPS)

Description

Calculates immune phenotype scores from gene expression data.

Usage

deconvo_ips(eset, project = NULL, plot = FALSE)

Arguments

Value

Data frame with IPS scores. Columns suffixed with '_IPS'.

Author(s)

Dongqiang Zeng

Examples

eset_stad <- load_data("eset_stad")
anno_grch38 <- load_data("anno_grch38")
eset <- anno_eset(eset = eset_stad, annotation = anno_grch38, probe = "id")

ips_result <- deconvo_ips(eset = eset, project = "TCGA-STAD")

Deconvolve Immune Microenvironment Using MCP-Counter

Description

Estimates immune cell abundances using MCP-counter.

Usage

deconvo_mcpcounter(eset, project = NULL)

Arguments

Value

Data frame with MCP-counter scores. Columns suffixed with '_MCPcounter'.

Author(s)

Dongqiang Zeng

Examples

eset_stad <- load_data("eset_stad")
anno_grch38 <- load_data("anno_grch38")
eset <- anno_eset(eset = eset_stad, annotation = anno_grch38, probe = "id")
eset <- eset[1:500, 1:3]

mcp_result <- deconvo_mcpcounter(eset = eset, project = "TCGA-STAD")
head(mcp_result)

Deconvolve Using quanTIseq

Description

quanTIseq deconvolution for RNA-seq immune cell fractions.

Usage

deconvo_quantiseq(eset, project = NULL, tumor, arrays, scale_mrna)

Arguments

Value

Data frame with quanTIseq cell fractions. Columns suffixed with '_quantiseq'.

Author(s)

Dongqiang Zeng

Examples

eset_stad <- load_data("eset_stad")
anno_grch38 <- load_data("anno_grch38")
eset <- anno_eset(eset = eset_stad, annotation = anno_grch38, probe = "id")
eset <- eset[1:500, 1:3]

res <- deconvo_quantiseq(
  eset = eset, project = "stad", tumor = TRUE,
  arrays = FALSE, scale_mrna = FALSE
)
head(res)

Deconvolve Using Custom Reference

Description

Cell fraction estimation using SVR or lsei methods with custom reference.

Usage

deconvo_ref(
  eset,
  project = NULL,
  arrays = TRUE,
  method = c("svr", "lsei"),
  perm = 100,
  reference,
  scale_reference = TRUE,
  absolute.mode = FALSE,
  abs.method = "sig.score"
)

Arguments

Value

Data frame with cell fractions. Columns suffixed with '_CIBERSORT'.

Author(s)

Dongqiang Zeng, Rongfang Shen

Examples

lm22 <- load_data("lm22")
common_genes <- rownames(lm22)[1:500]
sim_eset <- as.data.frame(matrix(
  rnorm(length(common_genes) * 5, mean = 5, sd = 2),
  nrow = length(common_genes), ncol = 5
))
rownames(sim_eset) <- common_genes
colnames(sim_eset) <- paste0("Sample", 1:5)

deconvo_ref(eset = sim_eset, reference = lm22, method = "lsei")

Deconvolve Using TIMER

Description

TIMER deconvolution for cancer-specific immune estimation.

Usage

deconvo_timer(eset, project = NULL, indications = NULL)

Arguments

Value

Data frame with TIMER cell fractions. Columns suffixed with '_TIMER'.

Author(s)

Dongqiang Zeng

Examples

eset_stad <- load_data("eset_stad")
anno_grch38 <- load_data("anno_grch38")
eset <- anno_eset(eset = eset_stad, annotation = anno_grch38, probe = "id")

res <- deconvo_timer(
  eset = eset, project = "stad",
  indications = rep("stad", ncol(eset))
)
head(res)

Main TME Deconvolution Function

Description

Unified interface for multiple TME deconvolution methods.

Usage

deconvo_tme(
  eset,
  project = NULL,
  method = tme_deconvolution_methods,
  arrays = FALSE,
  tumor = TRUE,
  perm = 1000,
  reference,
  scale_reference = TRUE,
  plot = FALSE,
  scale_mrna = TRUE,
  group_list = NULL,
  platform = "affymetrix",
  absolute.mode = FALSE,
  abs.method = "sig.score",
  ...
)

Arguments

Value

Tibble with cell fractions and 'ID' column.

Author(s)

Dongqiang Zeng, Rongfang Shen

References

1. Newman et al. (2015). Robust enumeration of cell subsets from tissue expression profiles. Nature Methods.

2. Vegesna et al. (2013). Inferring tumour purity and stromal/immune cell admixture. Nature Communications.

3. Finotello et al. (2019). Molecular and pharmacological modulators of the tumor immune contexture. Genome Medicine.

4. Li et al. (2016). Comprehensive analyses of tumor immunity. Genome Biology.

5. Charoentong et al. (2017). Pan-cancer Immunogenomic Analyses. Cell Reports.

6. Becht et al. (2016). Estimating population abundance of tissue-infiltrating immune cells. Genome Biology.

7. Aran et al. (2017). xCell: digitally portraying tissue cellular heterogeneity. Genome Biology.

8. Racle et al. (2017). Simultaneous enumeration of cancer and immune cell types. ELife.

Examples

lm22 <- load_data("lm22")
common_genes <- rownames(lm22)[1:500]
sim_eset <- as.data.frame(matrix(
  rnorm(length(common_genes) * 5, mean = 5, sd = 2),
  nrow = length(common_genes), ncol = 5
))
rownames(sim_eset) <- common_genes
colnames(sim_eset) <- paste0("Sample", 1:5)

res <- deconvo_tme(eset = sim_eset, method = "cibersort", perm = 10)

Deconvolve Immune Microenvironment Using xCell

Description

Estimates immune cell fractions using the xCell algorithm. xCell provides cell type enrichment scores for 64 immune and stromal cell types from gene expression data.

Usage

deconvo_xcell(eset, project = NULL, arrays = FALSE)

Arguments

Value

Data frame with xCell enrichment scores. Cell type columns are suffixed with '_xCell'.

Author(s)

Dongqiang Zeng

Examples

eset_stad <- load_data("eset_stad")
anno_grch38 <- load_data("anno_grch38")
eset <- anno_eset(eset = eset_stad, annotation = anno_grch38, probe = "id")
xcell_result <- deconvo_xcell(eset = eset[, 1:3], project = "TCGA-STAD")
head(xcell_result)[, 1:5]

Use quanTIseq to Deconvolute a Gene Expression Matrix

Description

Deconvolutes gene expression data to estimate immune cell fractions using the quanTIseq method. Source code from https://github.com/FFinotello/quanTIseq.

Usage

deconvolute_quantiseq.default(
  mix.mat,
  arrays = FALSE,
  signame = "TIL10",
  tumor = FALSE,
  mRNAscale = TRUE,
  method = c("lsei", "hampel", "huber", "bisquare"),
  rmgenes = "unassigned"
)

Arguments

Value

Data frame with cell fractions for each sample.

Author(s)

Finotello F, et al. (adapted for IOBR)

References

F. Finotello, C. Mayer, C. Plattner, G. Laschober, D. Rieder, H. Hackl, A. Krogsdam, W. Posch, D. Wilflingseder, S. Sopper, M. Jsselsteijn, D. Johnsons, Y. Xu, Y. Wang, M. E. Sanders, M. V. Estrada, P. Ericsson-Gonzalez, J. Balko, N. F. de Miranda, Z. Trajanoski. "quanTIseq: quantifying immune contexture of human tumors". bioRxiv 223180. https://doi.org/10.1101/223180.

Examples

lm22 <- load_data("lm22")
common_genes <- rownames(lm22)[1:500]
tpm_matrix <- as.data.frame(matrix(
  rnorm(length(common_genes) * 5, mean = 5, sd = 2),
  nrow = length(common_genes), ncol = 5
))
rownames(tpm_matrix) <- common_genes
colnames(tpm_matrix) <- paste0("Sample", 1:5)
results <- deconvolute_quantiseq.default(mix.mat = tpm_matrix)

Deconvolute Tumor Microenvironment Using TIMER

Description

Performs deconvolution of the tumor microenvironment using the TIMER algorithm. Processes multiple cancer datasets, removes batch effects, and estimates immune cell type abundances.

Usage

deconvolute_timer.default(args)

Arguments

Value

Matrix of abundance scores for different immune cell types across multiple cancer samples.

Examples

## Not run: 
# This example requires actual expression data files
# Create a batch file with paths to expression data and cancer types
batch_file <- "batch.csv"
# batch.csv format: each row contains expression_file_path,cancer_type
# Example content:
# /path/to/exp1.txt,luad
# /path/to/exp2.txt,brca
outdir <- tempdir()
args <- list(outdir = outdir, batch = batch_file)
results <- deconvolute_timer.default(args)

## End(Not run)

Design Custom Theme for ggplot2 Plots

Description

Creates a customized ggplot2 theme based on user-specified parameters for plot elements such as title size, axis sizes, legend settings, and theme style. Supports various base themes and allows fine-tuning of visual aspects.

Usage

design_mytheme(
  theme = c("light", "bw", "classic", "classic2"),
  plot_title_size = 2,
  axis_title_size = 2,
  axis_text_size = 12,
  axis_angle = 60,
  hjust = 1,
  legend.position = "bottom",
  legend.direction = "horizontal",
  legend.size = 0.25,
  legend.key.height = 0.5,
  legend.key.width = 0.5,
  legend.size.text = 10,
  legend.box = "horizontal"
)

Arguments

Value

A ggplot2 theme object.

Author(s)

Dongqiang Zeng

Examples

library(ggplot2)
p <- ggplot(mtcars, aes(wt, mpg)) +
  geom_point()
mytheme <- design_mytheme(theme = "bw", plot_title_size = 1.5, axis_text_size = 14)
p + mytheme + ggtitle("Example Plot")

Permutation Test for CIBERSORT

Description

Performs permutation-based sampling to generate an empirical null distribution of correlation coefficients for p-value calculation in CIBERSORT analysis. Randomly samples from the mixture data to create null distributions.

Usage

doPerm(perm, X, Y, absolute, abs_method, seed = NULL)

Arguments

Value

List containing:

dist

Numeric vector of correlation coefficients from permutations

Examples

X <- matrix(rnorm(100), nrow = 10)
Y <- rnorm(10)
result <- doPerm(100, X, Y, absolute = FALSE, abs_method = "sig.score")

Download IOBR Data from GitHub with Mirror Support

Description

Downloads large datasets from GitHub releases to avoid CRAN size limits. Supports multiple download mirrors for users in different regions. Data is cached locally after first download.

Usage

download_iobr_data(
  name,
  force = FALSE,
  verbose = TRUE,
  mirrors = get_default_mirrors()
)

Arguments

Value

The requested dataset.

Examples

# Download TCGA STAD signature data
tcga_sig <- download_iobr_data("tcga_stad_sig")

# Download with custom mirrors
eset <- download_iobr_data("eset_stad",
  mirrors = c(
    "https://ghproxy.vip/https://github.com",
    "https://gh-proxy.org/https://github.com"
  )
)

Enrichment Bar Plot with Two Directions

Description

Creates a bar plot visualizing enrichment results for up-regulated and down-regulated terms, using -log10(p-values) to indicate significance.

Usage

enrichment_barplot(
  up_terms,
  down_terms,
  terms = "Description",
  pvalue = "pvalue",
  group = "group",
  palette = "jama",
  cols = NULL,
  title = "Gene Ontology Enrichment",
  width_wrap = 30,
  font_terms = 15
)

Arguments

Value

A ggplot object of the enrichment bar plot.

Author(s)

Dongqiang Zeng

Examples

up_terms <- data.frame(
  Description = c("Pathway1", "Pathway2"),
  pvalue = c(0.001, 0.01)
)
down_terms <- data.frame(
  Description = c("Pathway4", "Pathway5"),
  pvalue = c(0.005, 0.02)
)
p <- enrichment_barplot(
  up_terms = up_terms,
  down_terms = down_terms,
  title = "Custom Enrichment Plot"
)
p

Visualize Expression Set Distribution

Description

Generates boxplots and density plots to analyze the distribution of expression values in an expression set. Useful for quality control and assessing data normalization.

Usage

eset_distribution(eset, quantile = 3, log = TRUE, project = NULL)

Arguments

Value

Invisibly returns 'NULL'. If 'project' is provided, saves PNG files to disk.

Examples

eset_stad <- load_data("eset_stad")
anno_rnaseq <- load_data("anno_rnaseq")
eset <- anno_eset(eset = eset_stad, annotation = anno_rnaseq)
eset_distribution(eset)
eset_distribution(eset, project = file.path(tempdir(), "ESET"))

estimateScore

Description

This function reads a gene expression dataset in GCT format, calculates enrichment scores for specific gene sets, and writes the computed scores to an output file. It supports multiple platform types and performs platform-specific calculations if necessary.

Usage

estimateScore(
  input.ds,
  output.ds,
  platform = c("affymetrix", "agilent", "illumina")
)

Arguments

Value

This function does not return a value but writes the computed scores to the specified output file in GCT format.

Examples

eset_stad <- load_data("eset_stad")
anno_grch38 <- load_data("anno_grch38")
eset <- anno_eset(eset = eset_stad, annotation = anno_grch38, probe = "id")
eset <- as.data.frame(eset)
eset <- tibble::rownames_to_column(eset, var = "symbol")
input_file <- tempfile(pattern = "estimate_", fileext = ".gct")
output_file <- tempfile(pattern = "estimate_score_", fileext = ".gct")
writeLines(c("#1.2", paste(nrow(eset), ncol(eset) - 1, sep = "\t")), input_file)
utils::write.table(
  eset,
  input_file,
  sep = "\t", row.names = FALSE, col.names = TRUE, append = TRUE, quote = FALSE
)
estimateScore(input.ds = input_file, output.ds = output_file, platform = "affymetrix")

Calculate Exact P-Value for Correlation

Description

Computes the exact p-value for the correlation between two numeric variables using a specified correlation method.

Usage

exact_pvalue(x, y, method)

Arguments

Value

Numeric value representing the exact p-value.

Author(s)

Dongqiang Zeng

Examples

sig_stad <- load_data("sig_stad")
p_val <- exact_pvalue(
  x = sig_stad$CD8.T.cells,
  y = sig_stad$CD_8_T_effector,
  method = "spearman"
)
print(p_val)

Extract Data Frame from Seurat Object

Description

Extracts and combines a data frame with cells as rows and features as columns from Seurat assay data. Supports multiple assays and optional metadata integration.

Usage

extract_sc_data(
  sce,
  vars = NULL,
  assay,
  slot = "scale.data",
  combine_meta_data = TRUE
)

Arguments

Value

Data frame with cells as rows and features as columns.

Author(s)

Dongqiang Zeng

Examples

if (requireNamespace("Seurat", quietly = TRUE)) {
  pbmc <- SeuratObject::pbmc_small
  vars <- c("PPBP", "IGLL5", "VDAC3", "CD1C", "AKR1C3")
  eset <- extract_sc_data(sce = pbmc, vars = vars, assay = "RNA")
}

Feature Quality Control and Filtering

Description

Filters features (variables) in a matrix or data frame by removing those with missing values, non-numeric types, infinite values, or zero variance. This is useful for preparing data for downstream statistical analyses.

Usage

feature_manipulation(
  data,
  feature = NULL,
  is_matrix = FALSE,
  print_result = FALSE
)

Arguments

Value

Character vector of feature names that pass all quality checks.

Author(s)

Dongqiang Zeng

Examples

eset_stad <- load_data("eset_stad")
feas <- feature_manipulation(
  data = eset_stad,
  feature = rownames(eset_stad),
  is_matrix = TRUE,
  print_result = TRUE
)

Feature Selection via Correlation or Differential Expression

Description

Selects informative features using either correlation with a quantitative response or differential expression (limma) for binary/continuous responses.

Usage

feature_select(
  x,
  y,
  method = c("cor", "dif"),
  family = c("spearman", "pearson"),
  cutoff = NULL,
  padjcut = NULL
)

Arguments

Value

Character vector of selected feature names.

Examples

imvigor210_eset <- load_data("imvigor210_eset")
mad <- apply(imvigor210_eset, 1, mad)
imvigor210_eset <- imvigor210_eset[mad > 0.5, ]
pd1 <- as.numeric(imvigor210_eset["PDCD1", ])
group <- ifelse(pd1 > mean(pd1), "high", "low")
pd1_cor <- feature_select(
  x = imvigor210_eset, y = pd1, method = "cor",
  family = "pearson", padjcut = 0.05, cutoff = 0.5
)
pd1_dif <- feature_select(
  x = imvigor210_eset, y = pd1, method = "dif",
  padjcut = 0.05, cutoff = 2
)
pd1_dif_2 <- feature_select(
  x = imvigor210_eset, y = group,
  method = "dif", padjcut = 0.05, cutoff = 2
)

filterCommonGenes

Description

This function filters and merges a dataset with a set of common genes.

Usage

filterCommonGenes(input.f, output.f, id = c("GeneSymbol", "EntrezID"))

Arguments

Value

No return value. The function writes the merged dataset to the specified output file.

Examples

# Create a sample input dataframe
input_data <- data.frame(
  GeneSymbol = c("BRCA1", "TP53", "EGFR", "NOTCH1"),
  Value = c(10, 15, 8, 12),
  stringsAsFactors = FALSE
)

# Write the input data to temporary file
input_file <- tempfile(fileext = ".txt")
output_file <- tempfile(fileext = ".txt")
write.table(input_data,
  file = input_file, sep = "\t", row.names = TRUE,
  quote = FALSE
)

# Call the filterCommonGenes function
filterCommonGenes(input_file, output_file, id = "GeneSymbol")

Identify Marker Features in Bulk Expression Data

Description

Identifies informative marker features across groups from bulk gene expression or signature score matrices using Seurat workflows. Performs feature selection, scaling, PCA, clustering, and marker discovery.

Usage

find_markers_in_bulk(
  pdata,
  eset,
  group,
  id_pdata = "ID",
  nfeatures = 2000,
  top_n = 20,
  thresh.use = 0.25,
  only.pos = TRUE,
  min.pct = 0.25,
  npcs = 30
)

Arguments

Value

List with components: 'sce' (Seurat object), 'markers' (all markers), 'top_markers' (top markers per group).

Examples

eset_tme_stad <- load_data("eset_tme_stad")
colnames(eset_tme_stad) <- substring(colnames(eset_tme_stad), 1, 12)
pdata_sig_tme <- load_data("pdata_sig_tme")

res <- find_markers_in_bulk(
  pdata = pdata_sig_tme, eset = eset_tme_stad,
  group = "TMEcluster"
)
# Extract top markers per cluster using base R
top_markers <- res$top_markers

Analyze Mutations Related to Signature Scores

Description

This function identifies mutations associated with a specific signature score, performs statistical tests for significance, and generates oncoprints and box plots to visualize relationships.

Usage

find_mutations(
  mutation_matrix,
  signature_matrix,
  id_signature_matrix = "ID",
  signature,
  min_mut_freq = 0.05,
  plot = TRUE,
  method = "multi",
  point_alpha = 0.1,
  save_path = NULL,
  palette = "jco",
  cols = NULL,
  show_plot = TRUE,
  show_col = FALSE,
  width = 8,
  height = 4,
  oncoprint_group_by = "mean",
  oncoprint_col = "#224444",
  gene_counts = 10,
  jitter = FALSE,
  genes = NULL,
  point_size = 4.5
)

Arguments

Value

A list containing statistical test results, oncoprint plots, and box plots.

Author(s)

Dongqiang Zeng

Examples

## Not run: 
# This example requires a MAF file from TCGA or maftools
# See maftools or TCGAbiolinks documentation for obtaining MAF files
mut_list <- make_mut_matrix(
  maf = "path_to_maf_file", isTCGA = TRUE,
  category = "multi"
)
mut <- mut_list$snp
results <- find_mutations(
  mutation_matrix = mut, signature_matrix = signature_data,
  id_signature_matrix = "ID", signature = "CD_8_T_effector",
  min_mut_freq = 0.01, plot = TRUE, method = "multi"
)

## End(Not run)

Identify Outlier Samples in Gene Expression Data

Description

Analyzes gene expression data to identify potential outlier samples using connectivity analysis via the WGCNA package. Calculates normalized adjacency and connectivity z-scores for each sample, generates connectivity plots, and optionally performs hierarchical clustering.

Usage

find_outlier_samples(
  eset,
  yinter = -3,
  project = NULL,
  plot_hculst = FALSE,
  show_plot = TRUE,
  index = NULL,
  save = FALSE
)

Arguments

Value

Character vector of sample names identified as potential outliers.

Author(s)

Dongqiang Zeng

Examples

eset_tme_stad <- load_data("eset_tme_stad")
outs <- find_outlier_samples(eset = eset_tme_stad)
print(outs)

Identify Variable Genes in Expression Data

Description

Identifies variable genes from a gene expression dataset using specified selection criteria. Supports multiple methods, including expression thresholding and variability estimation via median absolute deviation (MAD).

Usage

find_variable_genes(
  eset,
  data_type = c("count", "normalized"),
  methods = c("low", "mad"),
  prop = 0.7,
  quantile = c(0.75, 0.5, 0.25),
  min.mad = 0.1,
  feas = NULL
)

Arguments

Value

Matrix subset of 'eset' containing variable genes.

Author(s)

Dongqiang Zeng

Examples

eset_tme_stad <- load_data("eset_tme_stad")
eset <- find_variable_genes(
  eset = eset_tme_stad,
  data_type = "normalized",
  methods = "mad",
  quantile = 0.25
)

Fix Expression Mixture Matrix

Description

Processes expression matrix by mapping genes, converting log values, quantile normalization (if arrays), and TPM normalization.

Usage

fixMixture(mix.mat, arrays = FALSE)

Arguments

Value

Processed expression matrix.

Format Input Signatures from MSigDB

Description

Reads a GMT file from MSigDB and converts it into a named list of gene sets suitable for IOBR functions.

Usage

format_msigdb(gmt, ont = "term", gene = "gene")

Arguments

Value

Named list of character vectors, where each element contains the genes belonging to one signature.

Examples

tf <- tempfile(fileext = ".gmt")
writeLines(
  c(
    "HALLMARK_TNFA_SIGNALING_VIA_NFKB\tNA\tTNF\tNFKB1\tNFKB2",
    "HALLMARK_P53_PATHWAY\tNA\tTP53\tMDM2\tCDKN1A"
  ),
  con = tf
)

sig_list <- format_msigdb(tf, ont = "term", gene = "gene")
names(sig_list)
sig_list[[1]]

Transform Signature Data into List Format

Description

Converts signature data from a data frame (with signatures as columns and genes as rows) into a list format suitable for IOBR functions. Handles NA values appropriately.

Usage

format_signatures(sig_data, save_signature = FALSE, output_path = NULL)

Arguments

Value

List of signatures.

Author(s)

Dongqiang Zeng

Examples

sig_data <- data.frame(
  Signature1 = c("Gene1", "Gene2", "Gene3", NA),
  Signature2 = c("Gene4", "Gene5", NA, NA)
)
format_signatures(sig_data)

Generate Reference Signature Matrix

Description

Generates a reference signature matrix for cell types based on differential expression analysis. Supports both limma for normalized data and DESeq2 for raw count data.

Usage

generateRef(dds, pheno, FDR = 0.05, dat, method = "limma")

Arguments

Value

List containing: - 'reference_matrix': Data frame of median expression for significant genes across cell types. - 'G': Optimal number of probes minimizing condition number. - 'condition_number': Minimum condition number. - 'whole_matrix': Full median expression matrix.

Examples

expressionData <- matrix(runif(1000 * 4, min = 0, max = 10), ncol = 4)
rownames(expressionData) <- paste("Gene", 1:1000, sep = "_")
colnames(expressionData) <- paste("Sample", 1:4, sep = "_")

phenotype <- c("celltype1", "celltype2", "celltype1", "celltype2")

rawCountData <- matrix(sample(1:100, 1000 * 4, replace = TRUE), ncol = 4)
rownames(rawCountData) <- paste("Gene", 1:1000, sep = "_")
colnames(rawCountData) <- paste("Sample", 1:4, sep = "_")


result <- generateRef(
  dds = rawCountData, pheno = phenotype,
  FDR = 0.05, dat = expressionData, method = "DESeq2"
)

Generate Reference Signature Matrix Using DESeq2

Description

Uses DESeq2 to perform differential expression analysis across cell types, identifies significantly expressed genes, and creates a reference signature matrix from median expression levels.

Usage

generateRef_DEseq2(dds, pheno, FDR = 0.05, dat)

Arguments

Value

List containing: - 'reference_matrix': Data frame of median expression for significant genes across cell types. - 'G': Optimal number of probes minimizing condition number. - 'condition_number': Minimum condition number. - 'whole_matrix': Full median expression matrix.

Examples

set.seed(123)
dds <- matrix(sample(0:1000, 2000, replace = TRUE), nrow = 100, ncol = 20)
colnames(dds) <- paste("Sample", 1:20, sep = "_")
rownames(dds) <- paste("Gene", 1:100, sep = "_")
pheno <- rep(c("Type1", "Type2"), each = 10)
dat <- matrix(runif(2000), nrow = 100, ncol = 20)
rownames(dat) <- rownames(dds)
colnames(dat) <- colnames(dds)

result <- generateRef_DEseq2(dds = dds, pheno = pheno, FDR = 0.05, dat = dat)
print(result$reference_matrix)

Generate Reference Signature Matrix Using Limma

Description

Performs differential expression analysis using the limma package to identify significantly expressed genes across different cell types. Computes median expression levels of these significant genes to create a reference signature matrix.

Usage

generateRef_limma(dat, pheno, FDR = 0.05)

Arguments

Value

List containing: - 'reference_matrix': Data frame of median expression values for significantly expressed genes. - 'G': Number of probes used that resulted in the lowest condition number. - 'condition_number': Minimum condition number obtained. - 'whole_matrix': Matrix of median values across all samples.

Examples

dat <- matrix(rnorm(2000), nrow = 100)
rownames(dat) <- paste("Gene", 1:100, sep = "_")
colnames(dat) <- paste("Sample", 1:20, sep = "_")
pheno <- sample(c("Type1", "Type2", "Type3"), 20, replace = TRUE)
results <- generateRef_limma(dat, pheno)
print(results)

Generate Reference Gene Matrix from RNA-seq DEGs

Description

Uses DESeq2 to identify differentially expressed genes and create a reference matrix from median expression levels across cell types.

Usage

generateRef_rnaseq(dds, pheno, mode = "oneVSothers", FDR = 0.05, dat)

Arguments

Value

List containing: - 'reference_matrix': Data frame of median expression for significant genes across cell types. - 'G': Optimal number of probes minimizing condition number. - 'condition_number': Minimum condition number. - 'whole_matrix': Full median expression matrix.

Author(s)

Rongfang Shen

Examples

dds <- matrix(rpois(200 * 10, lambda = 10), ncol = 10)
rownames(dds) <- paste("Gene", 1:200, sep = "_")
colnames(dds) <- paste("Sample", 1:10, sep = "_")
pheno <- sample(c("Type1", "Type2", "Type3"), 10, replace = TRUE)
dat <- matrix(rnorm(200 * 10), ncol = 10)
rownames(dat) <- rownames(dds)
colnames(dat) <- colnames(dds)

results <- generateRef_rnaseq(dds = dds, pheno = pheno, FDR = 0.05, dat = dat)

Generate Reference Matrix from Seurat Object

Description

Generates reference gene expression data from a Seurat object by identifying marker genes for each cell type and aggregating expression data.

Usage

generateRef_seurat(
  sce,
  celltype = NULL,
  proportion = NULL,
  assay_deg = "RNA",
  slot_deg = "data",
  adjust_assay = FALSE,
  assay_out = "RNA",
  slot_out = "data",
  verbose = FALSE,
  only.pos = TRUE,
  n_ref_genes = 50,
  logfc.threshold = 0.15,
  test.use = "wilcox"
)

Arguments

Value

Matrix containing aggregated expression data for reference genes.

Author(s)

Dongqiang Zeng

Examples

## Not run: 
if (requireNamespace("Seurat", quietly = TRUE)) {
  # Requires a Seurat object with sufficient cells and markers
  sm <- generateRef_seurat(sce = seurat_obj, celltype = "cell_type", slot_out = "data")
}

## End(Not run)

Extract Hazard Ratio and Confidence Intervals from Cox Model

Description

Extracts hazard ratio (HR) and 95 Cox proportional hazards model.

Usage

getHRandCIfromCoxph(coxphData)

Arguments

Value

Data frame with p-values, HR, and confidence intervals.

Author(s)

Dorothee Nickles

Dongqiang Zeng

Examples

library(survival)
set.seed(123)
df <- data.frame(
  TTE = rexp(200, rate = 0.1),
  Cens = rbinom(200, size = 1, prob = 0.7),
  group = sample(c("Treatment", "Control"), 200, replace = TRUE)
)
coxphData <- survival::coxph(survival::Surv(TTE, Cens) ~ group, data = df)
results <- getHRandCIfromCoxph(coxphData)
print(results)

Set and View Color Palettes

Description

Retrieves color palettes from the IOBR package with options for randomization and visualization. Users can specify predefined palettes or provide custom colors.

Usage

get_cols(cols = "normal", palette = 1, show_col = TRUE, seed = 123)

Arguments

Value

Character vector of colors.

Examples

# Get default palette
mycols <- get_cols()

# Get random palette
mycols <- get_cols(cols = "random", seed = 456)

# Use custom colors
mycols <- get_cols(cols = c("red", "blue", "green"))

Calculate and Visualize Correlation Between Two Variables

Description

Calculates and visualizes the correlation between two variables with options for scaling, handling missing values, and incorporating grouping data.

Usage

get_cor(
  eset,
  pdata = NULL,
  var1,
  var2,
  is.matrix = FALSE,
  id_eset = "ID",
  id_pdata = "ID",
  scale = TRUE,
  subtype = NULL,
  na.subtype.rm = FALSE,
  color_subtype = NULL,
  palette = "jama",
  index = NULL,
  method = c("spearman", "pearson", "kendall"),
  show_cor_result = TRUE,
  col_line = NULL,
  id = NULL,
  show_label = FALSE,
  point_size = 4,
  title = NULL,
  alpha = 0.5,
  title_size = 1.5,
  text_size = 10,
  axis_angle = 0,
  hjust = 0,
  show_plot = TRUE,
  save_plot = FALSE,
  path = NULL,
  fig.format = "png",
  fig.width = 7,
  fig.height = 7.3,
  add.hdr.line = FALSE
)

Arguments

Value

A ggplot object of the correlation plot.

Author(s)

Dongqiang Zeng

Examples

eset_tme_stad <- load_data("eset_tme_stad")
get_cor(
  eset = eset_tme_stad,
  is.matrix = TRUE,
  var1 = "GZMB",
  var2 = "CD274"
)

Calculate and Visualize Correlation Matrix Between Two Variable Sets

Description

Calculates and visualizes the correlation matrix between two sets of variables. Supports Pearson, Spearman, and Kendall correlation methods. The function generates a customizable heatmap with significance stars.

Usage

get_cor_matrix(
  data,
  feas1,
  feas2,
  method = c("pearson", "spearman", "kendall"),
  path = NULL,
  index = 1,
  fig.type = "pdf",
  width = NULL,
  height = NULL,
  project = NULL,
  is.matrix = FALSE,
  scale = TRUE,
  font.size = 15,
  fill_by_cor = FALSE,
  round.num = 1,
  font.size.star = 8,
  cols = NULL
)

Arguments

Value

ggplot object displaying the correlation matrix heatmap.

Author(s)

Dongqiang Zeng

Examples

set.seed(123)
data <- as.data.frame(matrix(rnorm(1000), nrow = 100, ncol = 10))
colnames(data) <- paste0("Gene_", 1:10)

feas1 <- c("Gene_1", "Gene_2", "Gene_3")
feas2 <- c("Gene_4", "Gene_5", "Gene_6")

cor_plot <- get_cor_matrix(
  data = data,
  feas1 = feas1,
  feas2 = feas2,
  method = "spearman",
  project = "Example Correlation"
)

Get Default Download Mirrors

Description

Returns the default list of download mirrors.

Usage

get_default_mirrors()

Value

Character vector of mirror URLs.

Extract Top Marker Genes from Single-Cell Differential Results

Description

Selects the top N marker genes per cluster from a ranked differential expression result table.

Usage

get_sig_sc(
  deg,
  cluster = "cluster",
  gene = "gene",
  avg_log2FC = "avg_log2FC",
  n = 100
)

Arguments

Value

List of character vectors; each element contains the top N genes for a cluster.

Examples

deg <- load_data("deg")
get_sig_sc(deg, cluster = "cluster", gene = "gene", avg_log2FC = "avg_log2FC", n = 100)

GSVA API Version Detection

Description

Detects whether the installed GSVA package supports the new parameter-based API (gsvaParam/ssgseaParam) or the old direct argument API. This function is used internally to ensure compatibility across different GSVA package versions (1.50.0+ vs older versions).

Usage

gsva_use_new_api()

Value

A list with two elements: - 'use_new_api': Logical indicating whether to use the new API ('TRUE') or old API ('FALSE') - 'gsva_version': Character string of the installed GSVA version, or '"not installed"' if not available

Identify High-Variance Features from Statistical Results

Description

Selects top variable (up- and down-regulated) features based on adjusted p-value and log fold-change thresholds.

Usage

high_var_fea(
  result,
  target,
  name_padj = "padj",
  padj_cutoff = 1,
  name_logfc,
  logfc_cutoff = 0,
  n = 10,
  data_type = NULL
)

Arguments

Value

Character vector of selected feature names (combined up and down sets).

Author(s)

Dongqiang Zeng

Examples

result_data <- data.frame(
  gene = c("Gene1", "Gene2", "Gene3", "Gene4", "Gene5"),
  padj = c(0.01, 0.02, 0.05, 0.001, 0.03),
  logfc = c(-2, 1.5, -3, 2.5, 0.5)
)
high_var_fea(
  result = result_data,
  target = "gene",
  name_padj = "padj",
  name_logfc = "logfc",
  n = 2,
  padj_cutoff = 0.05,
  logfc_cutoff = 1.5
)

IMvigor210 Bladder Cancer Immunotherapy Cohort Data

Description

Clinical and biomarker data from the IMvigor210 clinical trial cohort. Includes treatment response, survival outcomes, and immune biomarker measurements for bladder cancer patients treated with atezolizumab.

Usage

data(imvigor210_pdata)

Format

A data frame with patients as rows and variables as columns:

ID

Patient sample identifier

BOR

Best overall response (CR, PR, SD, PD, NA)

BOR_binary

Binary response classification (R=responder, NR=non-responder)

OS_days

Overall survival time in days

OS_status

Overall survival status (0=alive, 1=dead)

Mutation_Load

Tumor mutation burden

Neo_antigen_Load

Neoantigen load

CD_8_T_effector

CD8+ T effector signature score

Immune_Checkpoint

Immune checkpoint signature score

Pan_F_TBRs

Pan-fibroblast TGF-\beta response signature

Mismatch_Repair

Mismatch repair status or signature

TumorPurity

Estimated tumor purity

Source

IMvigor210 clinical trial (NCT02108652)

References

Mariathasan S et al. TGF\beta attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells. Nature 554, 544-548 (2018). doi:10.1038/nature25501

Examples

data(imvigor210_pdata)
head(imvigor210_pdata)

Integrative Correlation Analysis Between Phenotype and Features

Description

Performs comprehensive correlation analysis between phenotype data and feature data, supporting both continuous and categorical phenotypes. Filters features based on statistical significance and generates publication-ready visualizations including box plots, heatmaps, and correlation plots.

Usage

iobr_cor_plot(
  pdata_group,
  id1 = "ID",
  feature_data,
  id2 = "ID",
  target = NULL,
  group = "group3",
  is_target_continuous = TRUE,
  padj_cutoff = 1,
  index = 1,
  category = "signature",
  signature_group = NULL,
  ProjectID = "TCGA",
  palette_box = "nrc",
  cols_box = NULL,
  palette_corplot = "pheatmap",
  palette_heatmap = 2,
  feature_limit = 26,
  character_limit = 60,
  show_heatmap_col_name = FALSE,
  show_col = FALSE,
  show_plot = FALSE,
  path = NULL,
  discrete_x = 20,
  discrete_width = 20,
  show_palettes = FALSE,
  fig.type = "pdf"
)

Arguments

Value

Depending on configuration, returns ggplot2 objects (box plots, heatmaps, correlation plots) and/or a data frame containing statistical analysis results.

Author(s)

Dongqiang Zeng

Examples

set.seed(123)

pdata_group <- data.frame(
  ID = 1:100,
  phenotype_score = rnorm(100)
)

feature_data <- data.frame(
  ID = 1:100,
  Feature1 = rnorm(100),
  Feature2 = rnorm(100),
  Feature3 = rnorm(100)
)

sig_group_example <- list(
  signature = c("Feature1", "Feature2", "Feature3")
)

results <- iobr_cor_plot(
  pdata_group = pdata_group,
  feature_data = feature_data,
  id1 = "ID",
  id2 = "ID",
  target = "phenotype_score",
  is_target_continuous = TRUE,
  category = "signature",
  signature_group = sig_group_example,
  show_plot = FALSE,
  path = tempdir()
)

print(results)

Tumor Microenvironment (TME) Deconvolution Pipeline

Description

Executes an integrated TME analysis on a gene expression matrix: performs immune/stromal cell deconvolution using multiple algorithms, computes signature scores, and aggregates results. Designed for exploratory immunogenomic profiling.

Usage

iobr_deconvo_pipeline(
  eset,
  project,
  array,
  tumor_type,
  path = NULL,
  permutation = 1000
)

Arguments

Value

Data frame integrating cell fractions and signature scores (also writes intermediate outputs to disk).

Author(s)

Dongqiang Zeng

Examples

eset_stad <- load_data("eset_stad")
anno_grch38 <- load_data("anno_grch38")
eset <- anno_eset(eset = eset_stad, annotation = anno_grch38, probe = "id")

res <- iobr_deconvo_pipeline(
  eset = eset, project = "STAD",
  array = FALSE, tumor_type = "stad",
  path = tempdir(), permutation = 10
)

Differential Expression Analysis

Description

Performs differential expression analysis on gene expression data using either DESeq2 or limma. Includes pre-processing steps like filtering low count data, and calculates fold changes and adjusted p-values. Optionally generates volcano plots and heatmaps.

Usage

iobr_deg(
  eset,
  annotation = NULL,
  id_anno = NULL,
  pdata,
  group_id = "group",
  pdata_id = "ID",
  array = FALSE,
  method = c("DESeq2", "limma"),
  contrast = c("High", "Low"),
  path = NULL,
  padj_cutoff = 0.01,
  logfc_cutoff = 0.5,
  volcano_plot = FALSE,
  col_volcano = 1,
  heatmap = TRUE,
  col_heatmap = 1,
  parallel = FALSE
)

Arguments

Value

Data frame containing differentially expressed genes with statistics including log2 fold changes and adjusted p-values.

Author(s)

Dongqiang Zeng

Examples

eset_stad <- load_data("eset_stad")
stad_group <- load_data("stad_group")
deg <- iobr_deg(
  eset = eset_stad, pdata = stad_group,
  group_id = "subtype", pdata_id = "ID", array = FALSE,
  method = "DESeq2", contrast = c("EBV", "GS"),
  path = file.path(tempdir(), "STAD")
)
head(deg)

Principal Component Analysis (PCA) Visualization

Description

This function performs Principal Component Analysis (PCA) on gene expression data, reduces dimensionality while preserving variance, and generates a scatter plot visualization.

Usage

iobr_pca(
  data,
  is.matrix = TRUE,
  scale = TRUE,
  is.log = FALSE,
  pdata,
  id_pdata = "ID",
  group = NULL,
  geom.ind = "point",
  cols = "normal",
  palette = "jama",
  repel = FALSE,
  ncp = 5,
  axes = c(1, 2),
  addEllipses = TRUE
)

Arguments

Value

A ggplot object of the PCA plot.

Author(s)

Dongqiang Zeng

Examples

if (requireNamespace("FactoMineR", quietly = TRUE) &&
  requireNamespace("factoextra", quietly = TRUE)) {
  set.seed(123)
  eset <- matrix(rnorm(1000), nrow = 100, ncol = 10)
  rownames(eset) <- paste0("Gene", 1:100)
  colnames(eset) <- paste0("Sample", 1:10)
  pdata <- data.frame(
    ID = colnames(eset),
    group = rep(c("A", "B"), each = 5)
  )
  iobr_pca(eset, pdata = pdata, id_pdata = "ID", group = "group", addEllipses = FALSE)
}

Map Score to Immunophenoscore

Description

Maps input score to Immunophenoscore (IPS) on a 0-10 scale. Scores \le0 map to 0, scores \ge3 map to 10, and intermediate scores are linearly scaled.

Usage

ipsmap(x)

Arguments

Value

Integer value between 0 and 10 representing the Immunophenoscore.

Examples

ips <- ipsmap(2.5)
ips <- ipsmap(-1)
ips <- ipsmap(5)

Feature Selection for Predictive or Prognostic Models Using LASSO Regression

Description

Applies LASSO (Least Absolute Shrinkage and Selection Operator) regression to construct predictive or prognostic models. Supports both binary and survival response variables, utilizing cross-validation for optimal model selection.

Usage

lasso_select(
  x,
  y,
  type = c("binary", "survival"),
  nfold = 10,
  lambda = c("lambda.min", "lambda.1se")
)

Arguments

Value

Character vector of selected feature names with non-zero coefficients in the optimal LASSO model.

Author(s)

Dongqiang Zeng

Examples

set.seed(123)
gene_expression <- matrix(rnorm(100 * 20), nrow = 100, ncol = 20)
rownames(gene_expression) <- paste0("Gene", 1:100)
colnames(gene_expression) <- paste0("Sample", 1:20)

# Binary response example
binary_outcome <- sample(c(0, 1), 20, replace = TRUE)
lasso_select(
  x = gene_expression,
  y = binary_outcome,
  type = "binary",
  nfold = 5
)

Differential Expression Analysis Using Limma

Description

Performs differential expression analysis using the limma package on a given gene expression dataset. Constructs a design matrix from phenotype data, fits a linear model, applies contrasts, and computes statistics for differential expression.

Usage

limma.dif(exprdata, pdata, contrastfml)

Arguments

Value

Returns a dataframe from limma::topTable, which includes genes as rows and columns like genelist, logFC, AveExpr, etc.

Examples

# Toy example with 100 genes and 6 samples
set.seed(123)
exprdata <- matrix(
  rnorm(100 * 6),
  nrow = 100,
  ncol = 6,
  dimnames = list(
    paste0("gene", 1:100),
    paste0("sample", 1:6)
  )
)

# Phenotype data: 3 vs 3
pdata <- data.frame(
  sample = colnames(exprdata),
  group = rep(c("group1", "group2"), each = 3),
  stringsAsFactors = FALSE
)

# Differential expression: group1 vs group2
res <- limma.dif(
  exprdata = exprdata,
  pdata = pdata,
  contrastfml = "group1 - group2"
)
head(res)

List Available GitHub Datasets

Description

List Available GitHub Datasets

Usage

list_github_datasets()

Value

Character vector of available dataset names.

Examples

list_github_datasets()

List Current Download Mirrors

Description

Returns the current list of download mirrors.

Usage

list_iobr_mirrors()

Value

Character vector of mirror URLs.

Examples

list_iobr_mirrors()

Load IOBR Datasets

Description

Loads internal datasets from the IOBR package. Supports both sysdata (internal) and exported data files included in the package.

Usage

load_data(name)

Arguments

Value

Dataset object, typically a 'list', 'data.frame', or 'matrix'. The exact type depends on the requested dataset.

Examples

# Load signature collection
sig_tme <- load_data("signature_tme")

# Load expression data
eset <- load_data("eset_stad")

# Load color palette
colors <- load_data("palette1")

# Error handling with suggestions for similar names
try(load_data("sign_tme")) # Will suggest "signature_tme"

Log2 Transformation of Gene Expression Matrix

Description

Determines whether a gene expression matrix requires log2 transformation based on the distribution of values, and applies it if necessary. This is useful for automatically detecting raw counts or linear-scale data that should be log-transformed for downstream analysis.

Usage

log2eset(eset)

Arguments

Value

Numeric matrix. Log2-transformed gene expression data (if transformation was needed), or the original data otherwise.

Examples

set.seed(123)
eset <- matrix(rnorm(1000, mean = 10, sd = 2), nrow = 100, ncol = 10)
rownames(eset) <- paste0("Gene", 1:100)
colnames(eset) <- paste0("Sample", 1:10)
eset_transformed <- log2eset(eset)

Quantile Normalization

Description

Quantile Normalization

Usage

makeQN(mix.mat)

Arguments

Value

Quantile normalized matrix.

Construct Mutation Matrices from MAF Data

Description

Builds mutation presence/absence matrices from MAF input (file path or MAF object). Supports multiple categories: all mutations, SNPs, indels, and frameshift mutations. When category = "multi", returns a list of matrices for each category. Compatible with TCGA-formatted data.

Usage

make_mut_matrix(
  maf = NULL,
  mut_data = NULL,
  isTCGA = TRUE,
  category = c("multi", "all", "snp", "indel", "frameshift"),
  Tumor_Sample_Barcode = "Tumor_Sample_Barcode",
  Hugo_Symbol = "Hugo_Symbol",
  Variant_Classification = "Variant_Classification",
  Variant_Type = "Variant_Type"
)

Arguments

Value

List of mutation matrices (if category = "multi") or a single matrix for the specified category.

Note

Some users may encounter errors from upstream data import (e.g. "Can't combine ..$Tumor_Seq_Allele2" when using TCGAbiolinks or TCGAmutations). This is due to inconsistent column types in the source MAF tables, not an issue of this function. Please ensure your MAF or merged data frame uses consistent column types (e.g. convert allele columns to character before input).

Author(s)

Dongqian Zeng

Shixiang Huang

Examples

## Not run: 
# See maftools or TCGAbiolinks documentation for obtaining MAF input
# Example: Download MAF file from TCGA portal
mut_list <- make_mut_matrix(maf = "path_to_maf_file.maf", isTCGA = TRUE, category = "multi")

## End(Not run)

Map Gene Names to Approved Symbols

Description

Maps gene symbols to approved HGNC symbols, handling withdrawn and previous symbols.

Usage

mapGenes(mydata)

Arguments

Value

Matrix with mapped gene symbols.

Map Score to Black and White Color

Description

Maps a numeric input value to a color from a black-white gradient palette. Values are mapped to a 1001-color palette where -2 maps to black and +2 maps to white.

Usage

mapbw(x, my_palette2 = NULL)

Arguments

Value

A color from the black-white palette as a hex code.

Examples

my_palette2 <- grDevices::colorRampPalette(c("black", "white"))(1001)
color <- mapbw(1.5, my_palette2)
color <- mapbw(-1, my_palette2)

Map Score to Color

Description

Maps a numeric input value to a color from a blue-white-red gradient palette. Values are mapped to a 1001-color palette where -3 maps to blue, 0 maps to white, and +3 maps to red.

Usage

mapcolors(x, my_palette = NULL)

Arguments

Value

A color from the palette as a hex code.

Examples

my_palette <- grDevices::colorRampPalette(c("blue", "white", "red"))(1001)
color <- mapcolors(2, my_palette)
color <- mapcolors(-2, my_palette)

Merge Data Frames with Duplicated Column Names

Description

Merges two data frames, resolving duplicated column names according to user preference. Allows selection of which data frame's duplicated columns to retain, ensuring data integrity during merging.

Usage

merge_duplicate(
  x,
  y,
  by.x,
  by.y,
  all.x = FALSE,
  all.y = FALSE,
  all = NULL,
  choose = c("x", "y")
)

Arguments

Value

Data frame resulting from merging 'x' and 'y' according to specified parameters.

Examples

df1 <- data.frame(ID = 1:3, Name = c("A", "B", "C"), Value = 1:3)
df2 <- data.frame(ID = 1:3, Name = c("X", "Y", "Z"), Score = 4:6)

# Merge keeping duplicated columns from x
merged_df <- merge_duplicate(df1, df2,
  by.x = "ID", by.y = "ID",
  all.x = TRUE, choose = "x"
)
print(merged_df)

# Merge keeping duplicated columns from y
merged_df2 <- merge_duplicate(df1, df2,
  by.x = "ID", by.y = "ID",
  all = TRUE, choose = "y"
)

Merging the duplicates from the input matrix.

Description

In case there are some duplicate rownames in the input matrix (mat), this function will return a similar matrix with unique rownames and the duplicate cases will be merged together based on the median values. When warn is true a warning message will be written in case there are duplicates, this warning message will include the in_type string to indicate the current type of the input matrix.

Usage

merge_duplicates(mat, warn = TRUE, in_type = NULL)

Arguments

Merge Expression Sets by Row Names

Description

Merges two or three expression sets (matrices or data frames) by row names (gene symbols), removing duplicates. The function ensures common genes across all input expression sets are retained.

Usage

merge_eset(eset1, eset2, eset3 = NULL)

Arguments

Value

Merged expression set (data frame) with duplicates removed. Row names correspond to gene symbols.

Author(s)

Dongqiang Zeng

Examples

# Load example data
eset_stad <- load_data("eset_stad")

# Create mock expression sets with common genes
common_genes <- c("TP53", "BRCA1", "EGFR", "MYC")
eset1 <- matrix(rnorm(12),
  nrow = 4,
  dimnames = list(common_genes, paste0("S", 1:3))
)
eset2 <- matrix(rnorm(16),
  nrow = 4,
  dimnames = list(common_genes, paste0("S", 4:7))
)

# Merge two expression sets
merged_eset <- merge_eset(eset1, eset2)
print(dim(merged_eset))

Calculate Microenvironment Scores

Description

Calculates combined immune and stroma scores from adjusted xCell scores.

Usage

microenvironmentScores(adjustedScores)

Arguments

Value

Matrix with additional microenvironment score rows.

Convert Mouse Gene Symbols to Human Gene Symbols

Description

Converts mouse gene symbols to human gene symbols in an expression dataset. Supports using either an online resource (Ensembl) or a local dataset for conversion.

Usage

mouse2human_eset(
  eset,
  source = c("local", "ensembl"),
  is_matrix = TRUE,
  column_of_symbol = NULL,
  verbose = FALSE
)

Arguments

Value

Expression set with human gene symbols.

Author(s)

Dongqiang Zeng

Examples

# Create example mouse expression data
anno_gc_vm32 <- load_data("anno_gc_vm32")
num_rows <- 200
num_cols <- 10
sample_names <- paste0("Sample", 1:num_cols)
data <- matrix(runif(num_rows * num_cols), nrow = num_rows, ncol = num_cols)
rownames(data) <- anno_gc_vm32$symbol[1:200]
colnames(data) <- sample_names

# Convert using local database
human_data <- mouse2human_eset(data, source = "local", is_matrix = TRUE)

NULL Model Coefficients for MCPcounter

Description

NULL Model Coefficients for MCPcounter

Usage

data(null_models)

Format

A 'data.frame' with cell types in rows and coefficients in columns.

Examples

data(null_models)
head(null_models)

outputGCT

Description

This function converts a gene expression dataset to a GCT format file.

Usage

outputGCT(input.f, output.f)

Arguments

Value

No return value. The function writes the dataset to the specified output file in GCT format.

Examples

# Create a sample input data frame
sample_data <- data.frame(
  Gene = c("BRCA1", "TP53", "EGFR"),
  Sample1 = c(10, 15, 8),
  Sample2 = c(12, 18, 7),
  stringsAsFactors = FALSE
)
rownames(sample_data) <- sample_data$Gene
sample_data <- sample_data[, -1]

# Convert the input data frame to GCT format and save to temporary file
output_file <- tempfile(fileext = ".gct")
outputGCT(sample_data, output_file)

Save Signature Data to File

Description

Saves signature data to a specified file format, supporting CSV or RData. Handles single signatures or lists of signatures, converting them to a data frame for storage.

Usage

output_sig(signatures, format = c("csv", "rdata"), file.name)

Arguments

Value

Data frame containing the processed signature data, also saved to the specified file.

Author(s)

Dongqiang Zeng

Examples

signature_collection <- load_data("signature_collection")
tmpfile <- tempfile(fileext = ".csv")
output_sig(
  signatures = signature_collection, format = "csv",
  file.name = tools::file_path_sans_ext(tmpfile)
)

Select Color Palettes for Visualization

Description

Provides curated qualitative, sequential, and diverging palettes for multiple plot types. Supports intensity adjustment and preview.

Usage

palettes(
  category = "box",
  palette = "nrc",
  alpha = 1,
  counts = 50,
  show_col = TRUE,
  show_message = FALSE
)

Arguments

Value

Character vector of hex color codes.

Author(s)

Dongqiang Zeng

Examples

colors <- palettes(category = "box", palette = "nrc", show_col = FALSE)
heatmap_colors <- palettes(
  category = "heatmap", palette = 1, counts = 10, show_col = FALSE
)

Parallel Permutation Test for CIBERSORT

Description

Parallel version of doPerm. Performs permutation-based sampling and runs the CoreAlg function iteratively using multiple CPU cores to accelerate computation. This function generates an empirical null distribution of correlation coefficients for p-value calculation in CIBERSORT analysis.

Usage

parallel_doperm(
  perm1,
  X1,
  Y1,
  absolute1,
  abs_method1,
  num_cores1 = 2,
  seed = NULL
)

Arguments

Details

This function utilizes the foreach and doParallel packages to distribute permutation iterations across multiple cores. It automatically handles cluster setup/teardown via on.exit() to prevent resource leaks.

Note: Windows users may experience slower performance due to socket-based parallelization (PSOCK) versus forking on Unix systems.

Value

List containing:

dist

Numeric vector of correlation coefficients from permutations, representing the empirical null distribution.

See Also

doPerm for the sequential version, CoreAlg, CIBERSORT

Examples

X <- matrix(rnorm(1000), nrow = 100)
Y <- matrix(rnorm(500), nrow = 100)
rownames(X) <- rownames(Y) <- paste0("Gene", 1:100)

result <- parallel_doperm(
  perm1 = 100, X1 = X, Y1 = Y,
  absolute1 = FALSE, abs_method1 = "sig.score", num_cores1 = 2
)
str(result$dist)

Default Pattern List for Name Cleaning

Description

A character vector of common substrings to remove from feature names. Used in [remove_names()] and other helper functions.

Usage

patterns_to_na

Format

A character vector of length 12.

Value

Character vector of patterns to remove.

Examples

# View default patterns
patterns_to_na

Toy STAD Phenotype Data

Description

A data frame containing clinical and pathological annotations for the TCGA stomach adenocarcinoma (STAD) cohort. Each row corresponds to one tumour sample and can be matched to the columns of eset_stad via the ID column. This dataset is typically used together with eset_stad in examples of survival analysis, subgroup comparison and immune deconvolution in the IOBR package.

Usage

data(pdata_stad)

Format

A data frame with one row per TCGA-STAD sample and 8 variables:

ID

Character. TCGA sample barcode, matching the column names of eset_stad.

stage

Factor. Pathological stage (e.g. "Stage_I", "Stage_II", "Stage_III", "Stage_IV").

status

Factor. Vital status at last follow-up ("Alive" or "Dead").

Lauren

Factor. Lauren classification of gastric cancer ("Intestinal", "Diffuse", "Mixed" or NA).

subtype

Factor. Molecular subtype (e.g. "CIN", "EBV", "GS", "MSI").

EBV

Factor. EBV status of the tumour ("Positive" or "Negative").

time

Numeric. Overall survival or follow-up time, typically measured in months.

OS_status

Integer/binary. Overall survival status indicator. (1 = death, 0 = censored)

Examples

data(pdata_stad)
head(pdata_stad)

Create a Percent Bar Plot

Description

Generates a bar plot visualizing the percentage distribution of a variable grouped by another variable.

Usage

percent_bar_plot(
  input,
  x,
  y,
  subset.x = NULL,
  color = NULL,
  palette = NULL,
  title = NULL,
  axis_angle = 0,
  coord_flip = FALSE,
  add_Freq = TRUE,
  Freq = NULL,
  size_freq = 8,
  legend.size = 0.5,
  legend.size.text = 10,
  add_sum = TRUE,
  print_result = TRUE,
  round.num = 2
)

Arguments

Value

A ggplot object.

Author(s)

Dongqiang Zeng

Examples

sig_stad <- load_data("sig_stad")
percent_bar_plot(
  input = sig_stad, x = "Subtype", y = "Lauren",
  axis_angle = 60
)

Create Pie or Donut Charts

Description

Generates a pie chart or donut chart from input data.

Usage

pie_chart(
  input,
  var,
  var2 = NULL,
  type = 2,
  show_freq = FALSE,
  color = NULL,
  palette = "jama",
  title = NULL,
  text_size = 10,
  title_size = 20,
  add_sum = FALSE
)

Arguments

Value

A ggplot object.

Author(s)

Dongqiang Zeng

Examples

sig_stad <- load_data("sig_stad")
pie_chart(input = sig_stad, var = "Subtype", palette = "jama")
pie_chart(input = sig_stad, var = "Subtype", type = 2)

plotPurity

Description

This function generates scatterplots of tumor purity based on ESTIMATE scores for given samples.

Usage

plotPurity(
  scores,
  samples = "all_samples",
  platform = c("affymetrix", "agilent", "illumina"),
  output.dir = NULL
)

Arguments

Value

No return value. The function generates and saves scatterplots in the specified output directory.

Examples

# Create a sample ESTIMATE score matrix
scores_data <- data.frame(
  Sample1 = c(100, 200, 500, 0.80),
  Sample2 = c(120, 220, 450, 0.70),
  Sample3 = c(140, 240, 600, 0.90),
  row.names = c(
    "StromalScore", "ImmuneScore", "ESTIMATEScore",
    "TumorPurity"
  ),
  check.names = FALSE
)

# Write to a temporary GCT file
scores_file <- tempfile(fileext = ".gct")
outputGCT(scores_data, scores_file)

Run quanTIseq Deconvolution

Description

Run quanTIseq Deconvolution

Usage

quanTIseq(currsig, currmix, scaling, method)

Arguments

Value

Deconvolution results matrix.

Helper Functions for quanTIseq

Description

Helper functions for quanTIseq deconvolution method. Source code adapted from https://github.com/FFinotello/quanTIseq.

Value

Internal functions return processed data for quanTIseq deconvolution.

Stratified Random Sampling of Cells

Description

Performs stratified random sampling of cells from single-cell data, ensuring proportional representation of each cell type while respecting minimum and maximum count constraints.

Usage

random_strata_cells(
  input,
  group,
  proportion = 0.1,
  minimum_count_include = 300,
  minimum_count = 200,
  maximum_count = 1000,
  sub_cluster = NULL,
  cell_type = NULL
)

Arguments

Value

A data frame containing the sampled cells with preserved cell type proportions.

Examples

## Not run: 
# Sample cells from a data frame
sampled_cells <- random_strata_cells(
  input = cell_annotations,
  group = "cell_type",
  proportion = 0.1,
  minimum_count_include = 300,
  minimum_count = 200,
  maximum_count = 1000
)

# Sample cells from a Seurat object
sampled_cells <- random_strata_cells(
  input = seurat_object,
  group = "seurat_clusters",
  proportion = 0.2
)

## End(Not run)

Calculate Raw xCell Enrichment Scores

Description

Returns the raw xCell cell types enrichment scores using ssGSEA.

Usage

rawEnrichmentAnalysis(expr, signatures, genes, file.name = NULL)

Arguments

Value

Matrix of raw xCell scores.

Row Bind Multiple Data Sets

Description

Combines two or three data frames or matrices vertically using 'rbind'. Ensures compatibility of input data before binding by aligning columns.

Usage

rbind_iobr(data1, data2, data3 = NULL)

Arguments

Value

Combined data frame resulting from row binding the input datasets.

Author(s)

Dongqiang Zeng

Examples

data1 <- data.frame(A = 1:5, B = letters[1:5])
data2 <- data.frame(A = 6:10, B = letters[6:10])
combined_data <- rbind_iobr(data1, data2)

# With three datasets
data3 <- data.frame(A = 11:15, B = letters[11:15])
combined_data <- rbind_iobr(data1, data2, data3)

Removing Batch Effect from Expression Sets

Description

Removes batch effects from expression datasets using sva::ComBat (for microarray/TPM data) or sva::ComBat_seq (for RNA-seq count data). Generates PCA plots to visualize data before and after correction.

Usage

remove_batcheffect(
  eset1,
  eset2,
  eset3 = NULL,
  id_type,
  data_type = c("array", "count", "tpm"),
  cols = "normal",
  palette = "jama",
  log2 = TRUE,
  check_eset = TRUE,
  adjust_eset = TRUE,
  repel = FALSE,
  path = NULL
)

Arguments

Value

Expression matrix after batch correction.

Author(s)

Dongqiang Zeng

References

Zhang Y, et al. ComBat-seq: batch effect adjustment for RNA-seq count data. NAR Genomics and Bioinformatics. 2020;2(3):lqaa078. doi:10.1093/nargab/lqaa078

Leek JT, et al. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics. 2012;28(6):882-883.

Examples

eset_stad <- load_data("eset_stad")
eset_blca <- load_data("eset_blca")

eset_corrected <- remove_batcheffect(
  eset_stad[1:1000, 1:5], eset_blca[1:1000, 1:5],
  id_type = "ensembl",
  data_type = "count"
)

Remove Duplicate Gene Symbols in Gene Expression Data

Description

This function addresses duplicate gene symbols in a gene expression dataset by selecting the highest-expressing instance among duplicates. Users can choose between mean, standard deviation, or sum as the ranking criterion for selection. This is useful for preparing data where duplicates can lead to issues in downstream analyses.

Usage

remove_duplicate_genes(eset, column_of_symbol, method = c("mean", "sd", "sum"))

Arguments

Value

A modified version of 'eset' where duplicate gene symbols have been reduced to a single entry (the highest-ranking one). The gene symbols are set as row names in the returned data frame.

Note

Important: This function performs selection, not aggregation. For duplicate genes, it retains only the highest-ranking instance (based on the specified method) and discards others.

Author(s)

Dongqiang Zeng

Examples

# Load and annotate expression data
eset_stad <- load_data("eset_stad")
anno_rnaseq <- load_data("anno_rnaseq")
eset_stad <- anno_eset(eset = eset_stad, annotation = anno_rnaseq)
eset_stad <- tibble::rownames_to_column(as.data.frame(eset_stad), var = "id")

# Create duplicate gene names for demonstration
eset_stad[2:3, "id"] <- "MT-CO1"

# Check duplicates before
sum(duplicated(eset_stad$id))

# Remove duplicates using mean expression as ranking criterion
eset_stad <- remove_duplicate_genes(
  eset = eset_stad,
  column_of_symbol = "id",
  method = "mean"
)

# Check duplicates after
sum(duplicated(rownames(eset_stad)))

Remove Patterns from Column Names or Variables

Description

Modifies column names or specified variables in a data frame by replacing specified patterns with empty strings or spaces.

Usage

remove_names(
  input_df,
  variable = "colnames",
  patterns_to_na = patterns_to_na,
  patterns_space = NULL
)

Arguments

Value

Modified data frame with patterns replaced.

Author(s)

Dongqiang Zeng

Examples

df <- data.frame(
  "CellA_cibersort" = 1:5,
  "CellB_xCell" = 6:10,
  "CellC_TIMER" = 11:15
)
result <- remove_names(df, variable = "colnames", patterns_to_na = patterns_to_na)
colnames(result)

Reset Download Mirrors to Default

Description

Resets the download mirror list to the default values.

Usage

reset_iobr_mirrors()

Value

Invisibly returns the default mirror list.

Examples

reset_iobr_mirrors()

Time-dependent ROC Curve for Survival Analysis

Description

Generates a time-dependent Receiver Operating Characteristic (ROC) plot to evaluate the predictive performance of one or more variables in survival analysis. Calculates the Area Under the Curve (AUC) for each specified time point and variable, and creates a multi-line ROC plot with annotated AUC values.

Usage

roc_time(
  input,
  vars,
  time = "time",
  status = "status",
  time_point = 12,
  time_type = "month",
  palette = "jama",
  cols = "normal",
  seed = 1234,
  show_col = FALSE,
  path = NULL,
  main = "PFS",
  index = 1,
  fig.type = "pdf",
  width = 5,
  height = 5.2
)

Arguments

Value

A ggplot object representing the time-dependent ROC plot.

Author(s)

Dongqiang Zeng

Examples

tcga_stad_sig <- load_data("tcga_stad_sig")
pdata_stad <- load_data("pdata_stad")
input <- merge(pdata_stad, tcga_stad_sig, by = "ID")
roc_time(
  input = input, vars = c("Pan_F_TBRs", "CD_8_T_effector", "Immune_Checkpoint"),
  time = "time", status = "OS_status", time_point = 12, path = NULL, main = "OS"
)

Scaling raw counts from each sample.

Description

Normalizing the sum of counts from each sample to 1e6.

Usage

scaleCounts(counts, sigGenes = NULL, renormGenes = NULL, normFact = NULL)

Details

Function taking a matrix (genes x samples) of counts as input and returning the scaled counts for the subset signature genes (or all genes if it sigGenes is NULL), with the scaled counts computed based on all the 'renormGenes' (or all genes if it is NULL). The renormalization is made independently for each sample, to have the sum of each columns over the renormGenes equal to 1e6.

normFact, if not null, is used as the normalization factor instead of the colSums (used to renormalize the refProfiles.var by the same amount than the refProfiles).

Scale and Manipulate a Matrix

Description

Scales a gene expression matrix, optionally applies logarithmic transformation, and performs feature manipulation to remove problematic variables (e.g., genes with zero variance or missing values).

Usage

scale_matrix(matrix, log2matrix = TRUE, manipulate = TRUE)

Arguments

Value

A scaled matrix (genes as rows, samples as columns).

Author(s)

Dongqiang Zeng

Examples

eset_gse62254 <- load_data("eset_gse62254")
eset2 <- scale_matrix(eset_gse62254, log2matrix = FALSE, manipulate = TRUE)

Select a Signature Scoring Method Subset

Description

Filters an integrated signature score matrix to retain results from a specified method (PCA, ssGSEA, or zscore) and strips method suffixes from column names.

Usage

select_method(data, method = c("ssGSEA", "PCA", "zscore"))

Arguments

Value

Matrix or data frame containing only the selected method's scores.

Author(s)

Dongqiang Zeng

Examples

eset_stad <- load_data("eset_stad")
anno_grch38 <- load_data("anno_grch38")
hallmark <- load_data("hallmark")
eset <- anno_eset(eset = eset_stad, annotation = anno_grch38, probe = "id")
eset <- eset[1:5000, 1:10]
res <- calculate_sig_score(
  eset = eset,
  signature = hallmark[1:4],
  method = "integration"
)
select_method(res, method = "PCA")

Calculate Signature Score Using PCA, Mean, or Z-score Methods

Description

Computes signature scores from gene expression data using Principal Component Analysis (PCA), mean-based, or z-score approaches.

Usage

sigScore(eset, methods = c("PCA", "mean", "zscore"))

Arguments

Value

Numeric vector of length 'ncol(eset)'; a score summarizing the rows of 'eset'.

Author(s)

Dorothee Nickles, Dongqiang Zeng

Examples

# Load example data
eset_stad <- load_data("eset_stad")
eset <- count2tpm(eset_stad, idType = "ensembl")

# Get signature genes
signature_tme <- load_data("signature_tme")
genes <- signature_tme[["CD_8_T_effector"]]
genes <- genes[genes %in% rownames(eset)]

# Calculate scores (only if enough genes are available)
if (length(genes) >= 2) {
  score_pca <- sigScore(eset = eset[genes, ], methods = "PCA")
  score_mean <- sigScore(eset = eset[genes, ], methods = "mean")
  score_zscore <- sigScore(eset = eset[genes, ], methods = "zscore")
}

Signature Box Plot with Statistical Comparisons

Description

Creates box plots to visualize signature distributions across groups with optional statistical pairwise comparisons. Supports both data frames and Seurat objects for single-cell data visualization.

Usage

sig_box(
  data,
  signature,
  variable,
  palette = "nrc",
  cols = NULL,
  jitter = FALSE,
  point_size = 5,
  angle_x_text = 0,
  hjust = 0.5,
  show_pairwise_p = TRUE,
  show_overall_p = FALSE,
  return_stat_res = FALSE,
  size_of_pvalue = 6,
  size_of_font = 10,
  assay = NULL,
  slot = "scale.data",
  scale = FALSE
)

Arguments

Value

If 'return_stat_res = FALSE', returns a ggplot2 object. If 'return_stat_res = TRUE', returns a data frame containing statistical test results.

Author(s)

Dongqiang Zeng

Examples

tcga_stad_pdata <- load_data("tcga_stad_pdata")
sig_box(
  data = tcga_stad_pdata,
  signature = "TMEscore_plus",
  variable = "subtype",
  jitter = TRUE,
  palette = "jco"
)

Batch Signature Box Plots for Group Comparisons

Description

Generates multiple box plots for specified features (signatures) across groups in the input data. Supports customization of plot appearance, output path, statistical annotation, and compatibility with Seurat objects. Plots are saved to the specified directory or a default folder.

Usage

sig_box_batch(
  input,
  vars,
  groups,
  pattern_vars = FALSE,
  path = NULL,
  index = 0,
  angle_x_text = 0,
  hjust = 0.5,
  palette = "jama",
  cols = NULL,
  jitter = FALSE,
  point_size = 5,
  size_of_font = 8,
  size_of_pvalue = 4.5,
  show_pvalue = TRUE,
  return_stat_res = FALSE,
  assay = NULL,
  slot = "scale.data",
  scale = FALSE,
  height = 5,
  width = 3.5,
  fig_type = "pdf",
  max_count_feas = 30
)

Arguments

Value

If 'return_stat_res = TRUE', returns a data frame of statistical results; otherwise, invisibly returns the path to saved plots.

Author(s)

Dongqiang Zeng

Examples

tcga_stad_pdata <- load_data("tcga_stad_pdata")
sig_box_batch(
  input = tcga_stad_pdata,
  vars = c("TMEscore_plus", "GZMB"),
  groups = "subtype",
  jitter = TRUE,
  palette = "jco",
  path = tempdir()
)

Forest Plot for Survival Analysis Results

Description

Generates a forest plot to visualize hazard ratios, confidence intervals, and p-values for gene signatures or features from survival analysis.

Usage

sig_forest(
  data,
  signature,
  pvalue = "P",
  HR = "HR",
  CI_low_0.95 = "CI_low_0.95",
  CI_up_0.95 = "CI_up_0.95",
  n = 10,
  max_character = 25,
  discrete_width = 35,
  color_option = 1,
  cols = NULL,
  text.size = 13
)

Arguments

Value

A ggplot2 object of the forest plot.

Author(s)

Dongqiang Zeng

Examples

# Example with sample survival results
sample_results <- data.frame(
  ID = c("Sig1", "Sig2", "Sig3"),
  HR = c(1.5, 0.8, 2.0),
  P = c(0.01, 0.05, 0.001),
  CI_low_0.95 = c(1.1, 0.6, 1.5),
  CI_up_0.95 = c(2.0, 1.0, 2.8)
)
sig_forest(data = sample_results, signature = "ID")

Grouped gene signatures for IOBR analysis

Description

A named list that organizes gene signatures into functional or biological categories. Each element of the list is a character vector containing the names of gene signatures defined in signature_collection. A total of 43 signature groups are included, covering tumour intrinsic pathways, immune-related processes, stromal activity, TME characteristics and immuno-oncology biomarkers. These groups are used in IOBR to conveniently select sets of signatures for scoring and visualization.

Usage

data(sig_group)

Format

A named list of length 43. Each element is a character vector of signature names. Representative groups include:

tumor_signature

Signatures related to intrinsic tumour biology such as cell cycle, DNA damage repair and histone regulation.

EMT

Epithelial–mesenchymal transition (EMT)–associated signatures.

io_biomarkers

Immuno-oncology biomarker–related signatures.

immu_microenvironment

Immune microenvironment–related signatures.

immu_suppression

Immune suppression–related signatures.

immu_exclusion

Signatures associated with immune exclusion and stromal barriers.

TCR_BCR

T-cell and B-cell receptor pathway signatures.

tme_signatures1

Tumour microenvironment signature panel (set 1).

tme_signatures2

Tumour microenvironment signature panel (set 2).

...

Additional groups are included (43 total), but not listed individually here; all groups follow the same structure.

Examples

data(sig_group)
head(sig_group)

Perform Gene Set Enrichment Analysis (GSEA)

Description

Conducts Gene Set Enrichment Analysis to identify significantly enriched gene sets from differential gene expression data. Supports MSigDB gene sets or custom gene signatures, and generates comprehensive visualizations and statistical results.

Usage

sig_gsea(
  deg,
  genesets = NULL,
  path = NULL,
  gene_symbol = "symbol",
  logfc = "log2FoldChange",
  org = c("hsa", "mus"),
  msigdb = TRUE,
  category = "H",
  subcategory = NULL,
  palette_bar = "jama",
  palette_gsea = 2,
  cols_gsea = NULL,
  cols_bar = NULL,
  show_bar = 10,
  show_col = FALSE,
  show_plot = FALSE,
  show_gsea = 8,
  show_path_n = 20,
  plot_single_sig = FALSE,
  project = "custom_sig",
  minGSSize = 10,
  maxGSSize = 500,
  verbose = TRUE,
  seed = FALSE,
  fig.type = "pdf",
  print_bar = TRUE
)

Arguments

Value

List containing:

up

Data frame of up-regulated enriched gene sets

down

Data frame of down-regulated enriched gene sets

all

Complete GSEA results

plot_top

GSEA enrichment plot for top gene sets

Author(s)

Dongqiang Zeng

Examples

set.seed(123)
genes <- c(
  "TP53", "BRCA1", "EGFR", "MYC", "KRAS", "PTEN", "APC", "RB1",
  "CDKN2A", "VHL", "ATM", "ATR", "CHEK2", "PALB2", "RAD51", "MDM2",
  "CDK4", "CDK6", "CCND1", "CCNE1", "CDK2", "E2F1", "E2F2", "E2F3",
  "ARF1", "ARF3", "ARF4", "ARF5", "ARF6", "GSK3B", "AKT1", "AKT2",
  "PIK3CA", "PIK3CB", "PIK3CD", "PIK3CG", "PIK3R1", "PIK3R2", "PIK3R3"
)
deg <- data.frame(
  symbol = genes,
  log2FoldChange = rnorm(length(genes), mean = 0, sd = 2),
  padj = runif(length(genes), 0, 0.1)
)
signature <- list(
  DNA_Repair = c(
    "TP53", "BRCA1", "ATM",
    "ATR", "CHEK2", "PALB2", "RAD51"
  ),
  Cell_Cycle = c(
    "TP53", "MYC",
    "RB1", "CDKN2A", "CDK4",
    "CDK6", "CCND1", "CCNE1",
    "CDK2", "E2F1", "E2F2", "E2F3"
  ),
  PI3K_AKT = c(
    "AKT1", "AKT2",
    "PIK3CA", "PIK3CB", "PIK3CD",
    "PIK3CG", "PIK3R1", "PIK3R2", "PIK3R3"
  )
)

res <- sig_gsea(
  deg = deg,
  genesets = signature,
  path = tempdir(),
  show_plot = FALSE,
  print_bar = FALSE
)
print(names(res))

Signature Heatmap with Optional Annotations

Description

Generates a heatmap of selected features grouped by a categorical variable, with optional conditional (annotation) bars. Supports palette customization, scaling, size controls, and output saving.

Usage

sig_heatmap(
  input,
  id = "ID",
  features,
  group,
  condition = NULL,
  id_condition = "vars",
  col_condition = "condition",
  cols_condition = NULL,
  scale = FALSE,
  palette = 2,
  cols_heatmap = NULL,
  palette_group = "jama",
  show_col = FALSE,
  show_palettes = FALSE,
  cols_group = NULL,
  show_plot = TRUE,
  width = 8,
  height = NULL,
  size_col = 10,
  size_row = 8,
  angle_col = 90,
  column_title = NULL,
  row_title = NULL,
  show_heatmap_col_name = FALSE,
  path = NULL,
  index = NULL
)

Arguments

Value

A tidyHeatmap object. Saves PDF only when 'path' is provided.

Author(s)

Dongqiang Zeng

Examples

tcga_stad_sig <- load_data("tcga_stad_sig")
tcga_stad_pdata <- load_data("tcga_stad_pdata")
input <- merge(tcga_stad_pdata, tcga_stad_sig, by = "ID")
feas <- grep("MCPcounter", colnames(input), value = TRUE)
sig_heatmap(input = input, features = feas, group = "subtype", scale = TRUE)

Generate Heatmap for Signature Data

Description

Creates a heatmap from signature data with grouping variables, offering flexible options for colors, clustering, and output formats using ComplexHeatmap.

Usage

sig_pheatmap(
  input,
  feas,
  group,
  group2 = NULL,
  group3 = NULL,
  ID = "ID",
  path = NULL,
  cols1 = "random",
  cols2 = "random",
  cols3 = "random",
  seed = 54321,
  show_col = FALSE,
  palette1 = 1,
  palette2 = 2,
  palette3 = 3,
  cluster_cols = TRUE,
  palette_for_heatmape = 6,
  scale.matrix = TRUE,
  cellwidth = 1,
  cellheight = 9,
  show_colnames = FALSE,
  fig.type = "pdf",
  width = 6,
  height = NULL,
  file_name_prefix = 1
)

Arguments

Value

A list containing:

p_anno

Annotation data frame

p_cols

List of cluster colors

plot

ComplexHeatmap object

eset

Transformed expression matrix

Author(s)

Dongqiang Zeng

Examples

tcga_stad_sig <- load_data("tcga_stad_sig")
tcga_stad_pdata <- load_data("tcga_stad_pdata")
input <- merge(tcga_stad_pdata, tcga_stad_sig, by = "ID")
feas <- grep("MCPcounter", colnames(input), value = TRUE)
sig_pheatmap(
  input = input, feas = feas, group = "subtype",
  scale.matrix = TRUE, path = tempdir()
)

Plot ROC Curves and Compare Them

Description

Generates Receiver Operating Characteristic (ROC) curves for multiple predictors and optionally performs statistical comparisons between them.

Usage

sig_roc(
  data,
  response,
  variables,
  fig.path = NULL,
  main = NULL,
  file.name = NULL,
  palette = "jama",
  cols = NULL,
  alpha = 1,
  compare = FALSE,
  smooth = TRUE,
  compare_method = "bootstrap",
  boot.n = 100
)

Arguments

Value

A list containing:

auc.out

Data frame with AUC values and confidence intervals

legend.name

Vector of legend entries for the plot

p.out

If 'compare = TRUE', data frame with p-values from comparisons

Author(s)

Dongqiang Zeng

Examples

tcga_stad_pdata <- load_data("tcga_stad_pdata")
sig_roc(
  data = tcga_stad_pdata, response = "OS_status",
  variables = c("TMEscore_plus", "GZMB", "GNLY")
)

Generate Kaplan-Meier Survival Plot for Signature

Description

Creates Kaplan-Meier survival plots for a given signature or gene, with automatic cutoff determination. Generates three types of plots: optimal cutoff (best cutoff), tertile-based (3 groups), and median split (2 groups).

Usage

sig_surv_plot(
  input_pdata,
  signature,
  project = "KM",
  ID = "ID",
  time = "time",
  status = "status",
  time_type = "month",
  break_month = "auto",
  cols = NULL,
  palette = "jama",
  show_col = TRUE,
  mini_sig = "score",
  fig.type = "png",
  save_path = NULL,
  index = 1
)

Arguments

Value

A list containing:

data

Processed input data with group assignments

plots

Combined survival plots

Author(s)

Dongqiang Zeng

Examples

tcga_stad_pdata <- load_data("tcga_stad_pdata")
sig_surv_plot(
  input_pdata = tcga_stad_pdata,
  signature = "TMEscore_plus",
  time = "time",
  status = "OS_status"
)

Gene signature collection for pathway and immune analysis

Description

A named list of gene signatures used in the IOBR package for immune deconvolution, pathway scoring, functional annotation, and tumour microenvironment (TME) characterization. Each element corresponds to a predefined biological signature and contains a character vector of HGNC gene symbols.

Usage

data(signature_collection)

Format

A named list of length 323. Each element is a character vector of gene symbols. Representative entries include:

CD_8_T_effector

Markers of CD8^{+} effector T cells.

DDR

DNA damage response and repair genes.

Immune_Checkpoint

Immune checkpoint molecules.

CellCycle_Reg

Core regulators of cell-cycle progression.

Mismatch_Repair

Mismatch-repair pathway genes.

TMEsocreA_CIR

TME-related signature used in TMEscore analysis.

...

Additional signatures are included in the list but are not individually listed here; all follow the same structure.

Examples

data(signature_collection)
head(signature_collection)

Signature Score Calculation Methods

Description

A named vector of supported methods for calculating signature scores.

Usage

signature_score_calculation_methods

Format

Named character vector:

PCA

Principal Component Analysis method ("pca")

ssGSEA

Single-sample Gene Set Enrichment Analysis ("ssgsea")

z-score

Z-score transformation method ("zscore")

Integration

Integration of multiple methods ("integration")

Examples

signature_score_calculation_methods
signature_score_calculation_methods["PCA"]

Adjust Scores Using Spillover Compensation

Description

Adjusts xCell scores using spillover compensation matrix.

Usage

spillOver(transformedScores, K, alpha = 0.5, file.name = NULL)

Arguments