Package index • IOBR

Data Input & Validation

Read, validate, and format expression data.

check_eset(): Check Integrity and Outliers of Expression Set
anno_eset(): Annotate Gene Expression Matrix and Remove Duplicated Genes
combine_pd_eset(): Combine Phenotype Data and Expression Set
merge_eset(): Merge Expression Sets by Row Names
rbind_iobr(): Row Bind Multiple Data Sets
filterCommonGenes(): filterCommonGenes
remove_duplicate_genes(): Remove Duplicate Gene Symbols in Gene Expression Data
merge_duplicate(): Merge Data Frames with Duplicated Column Names
assimilate_data(): Harmonize Two Data Frames by Column Structure

Data Preprocessing & Transformation

Normalize, transform, and batch-correct expression data.

transform_data(): Transform NA, Inf, or Zero Values in Data
count2tpm(): Convert Read Counts to Transcripts Per Million (TPM)
log2eset(): Log2 Transformation of Gene Expression Matrix
remove_batcheffect(): Removing Batch Effect from Expression Sets
RemoveBatchEffect(): Remove Batch Effect of Expression Set
scale_matrix(): Scale and Manipulate a Matrix
eset_distribution(): Visualize Expression Set Distribution
mouse2human_eset(): Convert Mouse Gene Symbols to Human Gene Symbols
patterns_to_na: Default Pattern List for Name Cleaning
tcga_rna_preps(): Preprocess TCGA RNA-seq Data

TME Deconvolution — Main Interface

Estimate immune and stromal cell fractions from bulk expression data using 11 integrated deconvolution algorithms.

deconvo_tme(): Main TME Deconvolution Function
tme_deconvolution_methods: TME Deconvolution Methods
select_method(): Select a Signature Scoring Method Subset
iobr_deconvo_pipeline(): Tumor Microenvironment (TME) Deconvolution Pipeline

TME Deconvolution — Individual Methods

Standalone wrappers for each supported deconvolution method.

deconvo_cibersort(): Deconvolve Using CIBERSORT
deconvo_timer(): Deconvolve Using TIMER
deconvo_xcell(): Deconvolve Immune Microenvironment Using xCell
deconvo_mcpcounter(): Deconvolve Immune Microenvironment Using MCP-Counter
deconvo_estimate(): Calculate ESTIMATE Scores
deconvo_epic(): Deconvolve Immune Microenvironment Using EPIC
deconvo_ips(): Calculate Immunophenoscore (IPS)
deconvo_quantiseq(): Deconvolve Using quanTIseq
deconvolute_quantiseq.default(): Use quanTIseq to Deconvolute a Gene Expression Matrix
deconvolute_timer.default(): Deconvolute Tumor Microenvironment Using TIMER
deconvo_ref(): Deconvolve Using Custom Reference

TME Deconvolution — CIBERSORT Internals

Core CIBERSORT algorithm implementation.

CIBERSORT(): CIBERSORT Deconvolution Algorithm
CoreAlg(): Core Algorithm for CIBERSORT Deconvolution
GetFractions.Abbas(): Constrained Regression Method (Abbas et al., 2009)
ParseInputExpression(): Parse Input Gene Expression Data
doPerm(): Permutation Test for CIBERSORT
parallel_doperm(): Parallel Permutation Test for CIBERSORT

TME Deconvolution — TIMER Internals

TIMER algorithm utilities and cancer-type support.

timer_available_cancers: TIMER Available Cancer Types
timer_info(): Source code for the TIMER deconvolution method.
check_cancer_types(): Process Batch Table and Validate Cancer Types
GetOutlierGenes(): Get Outlier Genes
Top_probe(): Top Probe Selector

TME Deconvolution — Other Algorithm Internals

Internal helpers for EPIC, IPS, MCPcounter, and ESTIMATE.

MCPcounter.estimate(): MCP-counter Cell Population Abundance Estimation
EPIC(): Estimate the proportion of immune and cancer cells.
IPS_calculation(): Calculate Immunophenoscore (IPS)
ipsmap(): Map Score to Immunophenoscore
estimateScore(): estimateScore
plotPurity(): plotPurity

Signature Scoring

Calculate gene signature scores using PCA, z-score, and ssGSEA methods for 300+ curated TME signatures.

calculate_sig_score(): Calculate Signature Score
sigScore(): Calculate Signature Score Using PCA, Mean, or Z-score Methods
signature_score_calculation_methods: Signature Score Calculation Methods
calculate_sig_score_pca(): Calculate Signature Score Using PCA Method
calculate_sig_score_zscore(): Calculate Signature Score Using Z-Score Method
calculate_sig_score_ssgsea(): Calculate Signature Score Using ssGSEA Method
calculate_sig_score_integration(): Calculate Signature Score Using Integration Method
test_for_infiltration(): Test for Cell Population Infiltration

Statistical Analysis — Correlation

Batch correlation tests between features or signature scores.

batch_cor(): Batch Correlation Analysis
batch_pcc(): Batch Calculation of Partial Correlation Coefficients
get_cor(): Calculate and Visualize Correlation Between Two Variables
get_cor_matrix(): Calculate and Visualize Correlation Matrix Between Two Variable Sets

Statistical Analysis — Group Comparison

Statistical tests comparing groups (Wilcoxon, Kruskal-Wallis).

batch_wilcoxon(): Batch Wilcoxon Rank-Sum Test Between Two Groups
batch_kruskal(): Batch Kruskal-Wallis Test
exact_pvalue(): Calculate Exact P-Value for Correlation

Statistical Analysis — Survival & Time-to-Event

Optimal cutoff identification and time-dependent ROC analysis.

best_cutoff(): Extract Best Cutoff and Add Binary Variable to Data Frame
best_cutoff2(): Extract Best Cutoff and Add Binary Variable to Data Frame
calculate_break_month(): Break Time Into Blocks
CalculateTimeROC(): Calculate Time-Dependent ROC Curve
PlotTimeROC(): Plot Time-Dependent ROC Curves
roc_time(): Time-dependent ROC Curve for Survival Analysis

Visualization — Heatmaps & Correlation

Heatmaps and correlation plots for TME features.

sig_heatmap(): Signature Heatmap with Optional Annotations
sig_pheatmap(): Generate Heatmap for Signature Data
iobr_cor_plot(): Integrative Correlation Analysis Between Phenotype and Features

Visualization — Survival & ROC

Kaplan-Meier survival curves and ROC plots.

sig_surv_plot(): Generate Kaplan-Meier Survival Plot for Signature
batch_surv(): Batch Survival Analysis
batch_sig_surv_plot(): Batch Signature Survival Plot
surv_group(): Generate Kaplan-Meier Survival Plots for Categorical Groups
subgroup_survival(): Subgroup Survival Analysis Using Cox Proportional Hazards Models
sig_roc(): Plot ROC Curves and Compare Them

Visualization — Comparison Plots

Box plots, violin plots, and forest plots.

sig_box(): Signature Box Plot with Statistical Comparisons
sig_box_batch(): Batch Signature Box Plots for Group Comparisons
sig_forest(): Forest Plot for Survival Analysis Results

Visualization — Distribution & Enrichment

Bar charts, pie charts, and enrichment visualizations.

cell_bar_plot(): Visualize Cell Fractions as Stacked Bar Chart
percent_bar_plot(): Create a Percent Bar Plot
pie_chart(): Create Pie or Donut Charts
enrichment_barplot(): Enrichment Bar Plot with Two Directions

Visualization — Themes & Color Utilities

Color palettes, themes, and plot formatting helpers.

get_cols(): Set and View Color Palettes
mapcolors(): Map Score to Color
mapbw(): Map Score to Black and White Color
design_mytheme(): Design Custom Theme for ggplot2 Plots
palettes(): Select Color Palettes for Visualization
DrawQQPlot(): Draw QQ Plot Comparing Cancer and Immune Expression

Prognostic Modeling

Build LASSO/Elastic-Net prognostic models and compute risk scores.

PrognosticModel(): Build Prognostic Models Using LASSO and Ridge Regression
BinomialModel(): Binomial Model Construction
add_riskscore(): Add Risk Score to Dataset
PrognosticAUC(): Calculate Time-Dependent AUC for Survival Models
BinomialAUC(): Calculate AUC for Binomial Model
SplitTrainTest(): Split Data into Training and Testing Sets
PrognosticResult(): Compute Prognostic Results for Survival Models
RegressionResult(): Regression Result Computation
PlotAUC(): Plot AUC ROC Curves
getHRandCIfromCoxph(): Extract Hazard Ratio and Confidence Intervals from Cox Model
ProcessingData(): Process Data for Model Construction
Enet(): Elastic Net Model Fitting
CalculatePref(): Calculate Performance Metrics

Clustering & Subgroup Analysis

Cluster TME profiles and identify patient subgroups.

tme_cluster(): Identification of TME Cluster

Feature Selection

Identify informative features and variable genes.

feature_select(): Feature Selection via Correlation or Differential Expression
feature_manipulation(): Feature Quality Control and Filtering
lasso_select(): Feature Selection for Predictive or Prognostic Models Using LASSO Regression
high_var_fea(): Identify High-Variance Features from Statistical Results
find_variable_genes(): Identify Variable Genes in Expression Data
find_outlier_samples(): Identify Outlier Samples in Gene Expression Data

Genomics & Differential Expression

Differential expression, mutation analysis, and PCA.

limma.dif(): Differential Expression Analysis Using Limma
iobr_deg(): Differential Expression Analysis
iobr_pca(): Principal Component Analysis (PCA) Visualization
find_mutations(): Analyze Mutations Related to Signature Scores
find_markers_in_bulk(): Identify Marker Features in Bulk Expression Data
make_mut_matrix(): Construct Mutation Matrices from MAF Data
ConvertRownameToLoci(): Convert Rowname To Loci

scRNA-seq Reference Construction

Build custom deconvolution reference matrices from scRNA-seq data.

generateRef(): Generate Reference Signature Matrix
generateRef_rnaseq(): Generate Reference Gene Matrix from RNA-seq DEGs
generateRef_seurat(): Generate Reference Matrix from Seurat Object
generateRef_limma(): Generate Reference Signature Matrix Using Limma
generateRef_DEseq2(): Generate Reference Signature Matrix Using DESeq2
extract_sc_data(): Extract Data Frame from Seurat Object
get_sig_sc(): Extract Top Marker Genes from Single-Cell Differential Results
Construct_con(): Construct Contrast Matrix
random_strata_cells(): Stratified Random Sampling of Cells

Signature Format & Export

Format, annotate, and export gene signatures.

format_signatures(): Transform Signature Data into List Format
format_msigdb(): Format Input Signatures from MSigDB
output_sig(): Save Signature Data to File
outputGCT(): outputGCT
sig_gsea(): Perform Gene Set Enrichment Analysis (GSEA)

Ligand-Receptor Interactions

Calculate ligand-receptor interaction scores.

LR_cal(): Calculate Ligand-Receptor Interaction Scores

General Utilities

Miscellaneous helper functions for data and file management.

load_data(): Load IOBR Datasets
creat_folder(): Create Nested Output Folders
remove_names(): Remove Patterns from Column Names or Variables

Datasets — Expression Sets

Example bulk RNA-seq expression matrices.

eset_stad: Toy STAD expression matrix
eset_blca: TCGA-BLCA Bladder Cancer Expression Data
eset_gse62254: GSE62254 Gastric Cancer Expression Data
eset_tme_stad: TCGA-STAD Tumor Microenvironment Signature Scores

Datasets — Deconvolution Reference Matrices

Reference signature matrices for CIBERSORT and EPIC.

lm22: Reference profiles for immune cell types using lm22 (EPIC/IOBR)
TRef: Reference profiles from tumor-infiltrating non-malignant cells
BRef: Reference profiles for B cell–related deconvolution (EPIC/IOBR)

Datasets — Gene Signatures

Curated gene signature collections for TME scoring.

signature_collection: Gene signature collection for pathway and immune analysis
imvigor210_sig: IMvigor210 Bladder Cancer Cohort Multi-omics Signatures
sig_stad: TCGA-STAD Gastric Cancer Cohort with Molecular and Clinical Data
tcga_stad_sig: TCGA-STAD Gastric Cancer Immune Infiltration Signatures

Datasets — Sample Metadata

Clinical and phenotypic data for example datasets.

pdata_stad: Toy STAD Phenotype Data
tcga_stad_pdata: TCGA-STAD Clinical and Molecular Annotation Data
imvigor210_pdata: IMvigor210 Bladder Cancer Immunotherapy Cohort Data
sig_group: Grouped gene signatures for IOBR analysis
stad_group: Example Clinical Data for TCGA-STAD Gastric Cancer Analysis
subgroup_data: Example Dataset for Subgroup Survival Analysis

Datasets — Genomic Annotation

Gene and probe annotation tables for multiple platforms.

anno_rnaseq: General RNA-seq Annotation
anno_grch38: GRCh38 Human Genome Annotation
anno_illumina: Illumina Microarray Annotation
anno_hug133plus2: Affymetrix Human Genome U133 Plus 2.0 Array Annotation
anno_gc_vm32: Mouse Genome Annotation (GC/VM32)

Datasets — Miscellaneous

Additional reference data used by package functions.

deg: Single-cell RNA-seq Differential Expression Analysis Results
null_models: NULL Model Coefficients for MCPcounter