Package 'MultiOmicsCancer' reference manual

Title:	Integrative Multi-Omics Analysis for Pan-Cancer Research
Description:	A modular and cancer-agnostic framework for integrative analysis of multi-omics data in cancer research. The package provides tools for differential methylation analysis, gene expression analysis, miRNA profiling, multi-omics integration, survival analysis, and biomarker discovery. It supports data from TCGA, cBioPortal, GEO, and user-provided datasets, and is designed to work across any cancer type.
Authors:	Maryam Emami [aut, cre] (ORCID: <https://orcid.org/0009-0008-7710-3274>)
Maintainer:	Maryam Emami <[email protected]>
License:	MIT + file LICENSE
Version:	0.99.2
Built:	2026-10-20 10:24:08 UTC
Source:	https://github.com/BiocStaging/MultiOmicsCancer

Differential Gene Expression Analysis for Pan-Cancer Studies

Description

Performs differential gene expression analysis between tumor and normal samples (or any two groups defined in the clinical data) for any cancer type. Supports both limma (for log-transformed or normalized data) and DESeq2 (for raw count data).

Usage

analyze_expression(
  mae,
  assay_name = "expression",
  group_column = "sample_type",
  group_levels = NULL,
  method = "limma",
  log_transform = TRUE,
  adj_method = "BH",
  p_cutoff = 0.05,
  logFC_cutoff = 1,
  verbose = TRUE
)
analyze_expression(
  mae,
  assay_name = "expression",
  group_column = "sample_type",
  group_levels = NULL,
  method = "limma",
  log_transform = TRUE,
  adj_method = "BH",
  p_cutoff = 0.05,
  logFC_cutoff = 1,
  verbose = TRUE
)

Arguments

mae

A MultiAssayExperiment object created by load_cancer_data.

assay_name

Character. Name of the expression assay in the MultiAssayExperiment. Default "expression".

group_column

Character. Name of the clinical column used to define groups. Default "sample_type".

group_levels

Character vector of length 2. The two groups to compare. The first is the reference (e.g., normal), the second is the comparison (e.g., tumor). If NULL, the function attempts to auto-detect common tumor/normal labels.

method

Character. Method for differential expression. One of "limma" (default) or "DESeq2". Use "DESeq2" only for raw count data.

log_transform

Logical. If TRUE and method = "limma", applies log2(x + 1) transformation to the data. Default TRUE.

adj_method

Character. Multiple testing correction method. Default "BH" (Benjamini-Hochberg).

p_cutoff

Numeric. Adjusted p-value threshold for significance. Default 0.05. Lower values increase specificity; higher values increase sensitivity.

logFC_cutoff

Numeric. Minimum absolute log2 fold change to call a gene differentially expressed. Default 1 (i.e., 2-fold). Lower values (e.g., 0.5) increase sensitivity and return more genes, but may include false positives. Higher values (e.g., 1.5) increase specificity and return fewer genes.

verbose

Logical. Whether to print progress messages. Default TRUE.

Value

A data frame with the following columns:

gene: Gene identifier
logFC: Log2 fold change (group2 - group1)
AveExpr: Average expression across all samples
t: Moderated t-statistic (limma) or Wald statistic (DESeq2)
P.Value: Raw p-value
adj.P.Val: Adjusted p-value
direction: "Up" or "Down" in group2 vs group1
significant: Logical, whether the gene passes both p-value and logFC cutoffs

Examples

if (interactive()) {
# Load data
brca <- load_cancer_data("brca_tcga")

# Differential expression: normal vs tumor with limma
deg <- analyze_expression(brca, method = "limma")

# With DESeq2 on raw counts
deg_deseq <- analyze_expression(brca, method = "DESeq2")

# Filter significant genes
sig_deg <- deg[deg$significant, ]

# Lower the logFC cutoff to get more genes (higher sensitivity)
deg_sensitive <- analyze_expression(brca, logFC_cutoff = 0.5)

# Raise the logFC cutoff for stricter results (higher specificity)
deg_strict <- analyze_expression(brca, logFC_cutoff = 1.5)
}

if (interactive()) {
# Load data
brca <- load_cancer_data("brca_tcga")

# Differential expression: normal vs tumor with limma
deg <- analyze_expression(brca, method = "limma")

# With DESeq2 on raw counts
deg_deseq <- analyze_expression(brca, method = "DESeq2")

# Filter significant genes
sig_deg <- deg[deg$significant, ]

# Lower the logFC cutoff to get more genes (higher sensitivity)
deg_sensitive <- analyze_expression(brca, logFC_cutoff = 0.5)

# Raise the logFC cutoff for stricter results (higher specificity)
deg_strict <- analyze_expression(brca, logFC_cutoff = 1.5)
}

Differential Methylation Analysis for Pan-Cancer Studies

Description

Performs differential methylation analysis between tumor and normal samples (or any two groups defined in the clinical data) for any cancer type. Uses the limma framework for linear modeling of methylation beta values.

Usage

analyze_methylation(
  mae,
  assay_name = "methylation",
  group_column = "sample_type",
  group_levels = NULL,
  method = "limma",
  adj_method = "BH",
  p_cutoff = 0.05,
  delta_beta = 0.1,
  verbose = TRUE
)
analyze_methylation(
  mae,
  assay_name = "methylation",
  group_column = "sample_type",
  group_levels = NULL,
  method = "limma",
  adj_method = "BH",
  p_cutoff = 0.05,
  delta_beta = 0.1,
  verbose = TRUE
)

Arguments

mae

A MultiAssayExperiment object created by load_cancer_data.

assay_name

Character. Name of the methylation assay in the MultiAssayExperiment. Default "methylation".

group_column

Character. Name of the clinical column used to define groups. Default "sample_type".

group_levels

method

Character. Differential methylation method. Currently supports "limma" (default).

adj_method

Character. Multiple testing correction method passed to limma::topTable. Default "BH" (Benjamini-Hochberg).

p_cutoff

Numeric. Adjusted p-value threshold for significance. Default 0.05. Lower values increase specificity; higher values increase sensitivity.

delta_beta

Numeric. Minimum absolute difference in mean beta value between groups to call a probe differentially methylated. Default 0.1 (10\ higher values (e.g., 0.2) return fewer, more robust probes.

verbose

Logical. Whether to print progress messages. Default TRUE.

Value

A data frame with the following columns:

probe: Probe or CpG identifier
logFC: Difference in mean methylation (group2 - group1)
AveExpr: Average methylation across all samples
t: Moderated t-statistic
P.Value: Raw p-value
adj.P.Val: Adjusted p-value
delta_beta: Absolute difference in mean beta between groups
direction: "Hyper" or "Hypo" methylation in group2 vs group1
significant: Logical, whether the probe passes both p-value and delta-beta cutoffs

Examples

if (interactive()) {
# Load data
brca <- load_cancer_data("brca_tcga")

# Differential methylation: normal vs tumor
dmr <- analyze_methylation(
  brca,
  group_column = "sample_type",
  group_levels = c("Solid Tissue Normal", "Primary Tumor")
)

# Filter significant probes
sig_dmr <- dmr[dmr$significant, ]

# More sensitive analysis with lower thresholds
dmr_sensitive <- analyze_methylation(brca, delta_beta = 0.05,
                                      p_cutoff = 0.01)
}

if (interactive()) {
# Load data
brca <- load_cancer_data("brca_tcga")

# Differential methylation: normal vs tumor
dmr <- analyze_methylation(
  brca,
  group_column = "sample_type",
  group_levels = c("Solid Tissue Normal", "Primary Tumor")
)

# Filter significant probes
sig_dmr <- dmr[dmr$significant, ]

# More sensitive analysis with lower thresholds
dmr_sensitive <- analyze_methylation(brca, delta_beta = 0.05,
                                      p_cutoff = 0.01)
}

Survival Analysis for Pan-Cancer Studies

Description

Performs survival analysis using clinical data and a molecular feature (gene expression, methylation, or a risk score). Supports Kaplan-Meier curves, log-rank tests, and univariate Cox proportional hazards models.

Usage

analyze_survival(
  mae,
  time_column = "OS.time",
  event_column = "OS",
  feature_name = NULL,
  assay_name = "expression",
  split_method = "median",
  verbose = TRUE
)
analyze_survival(
  mae,
  time_column = "OS.time",
  event_column = "OS",
  feature_name = NULL,
  assay_name = "expression",
  split_method = "median",
  verbose = TRUE
)

Arguments

mae

A MultiAssayExperiment object created by load_cancer_data, containing clinical survival data.

time_column

Character. Name of the clinical column containing follow-up time (in days or months). Default "OS.time".

event_column

Character. Name of the clinical column containing event status (1 = event/death, 0 = censored). Default "OS".

feature_name

Character. Name of the molecular feature to test (gene symbol or probe ID). If NULL, only clinical summary is returned.

assay_name

Character. Which assay to use for the feature. One of "expression" (default), "methylation", or "mirna".

split_method

Character. How to split samples into high/low groups. One of "median" (default), "tertile", or "quartile".

verbose

Logical. Whether to print progress messages. Default TRUE.

Value

A list with the following components:

km_fit: A survfit object for Kaplan-Meier curves
logrank_p: P-value from the log-rank test
cox_model: A coxph object for univariate Cox regression
cox_summary: Summary table of the Cox model
data: Data frame used for the analysis
group: Factor of high/low groups

Examples

if (interactive()) {
# Load data
brca <- load_cancer_data("brca_tcga")

# Survival analysis for TP53 expression
surv <- analyze_survival(brca, feature_name = "TP53")

# Survival analysis for a methylation probe
surv_meth <- analyze_survival(brca,
                              feature_name = "cg00000029",
                              assay_name = "methylation")
}

if (interactive()) {
# Load data
brca <- load_cancer_data("brca_tcga")

# Survival analysis for TP53 expression
surv <- analyze_survival(brca, feature_name = "TP53")

# Survival analysis for a methylation probe
surv_meth <- analyze_survival(brca,
                              feature_name = "cg00000029",
                              assay_name = "methylation")
}

Nested Bootstrap Validation for IMS

Description

Nested Bootstrap Validation for IMS

Usage

bootstrap_validate_ims(
  meth_data,
  surv_data,
  immune_genes = NULL,
  B = 100L,
  p_threshold = 0.05,
  min_cpgs = 3L,
  seed = 123
)
bootstrap_validate_ims(
  meth_data,
  surv_data,
  immune_genes = NULL,
  B = 100L,
  p_threshold = 0.05,
  min_cpgs = 3L,
  seed = 123
)

Arguments

meth_data

Matrix of methylation (CpGs x samples).

surv_data

Data frame with columns 'time' and 'status'.

immune_genes

Character vector of immune gene symbols.

B

Integer. Number of bootstrap replicates. Default 100.

p_threshold

Numeric. P-value cutoff. Default 0.05.

min_cpgs

Integer. Minimum CpGs required. Default 3.

seed

Integer. Random seed. Default 123.

Value

A list with apparent C-index, optimism, corrected C-index, and the bootstrap distribution.

Examples

# Source: Mock data simulated for demonstration purposes.
set.seed(123)
meth_data <- matrix(runif(100), nrow = 10, ncol = 10)
rownames(meth_data) <- paste0("CD274_cg", 1:10)
colnames(meth_data) <- paste0("Sample", 1:10)
surv_data <- data.frame(
  time = runif(10, 10, 100),
  status = sample(c(0, 1), 10, replace = TRUE)
)
rownames(surv_data) <- colnames(meth_data)
res <- bootstrap_validate_ims(
  meth_data = meth_data, surv_data = surv_data,
  immune_genes = c("CD274"), B = 5,
  p_threshold = 1, min_cpgs = 2
)
# Source: Mock data simulated for demonstration purposes.
set.seed(123)
meth_data <- matrix(runif(100), nrow = 10, ncol = 10)
rownames(meth_data) <- paste0("CD274_cg", 1:10)
colnames(meth_data) <- paste0("Sample", 1:10)
surv_data <- data.frame(
  time = runif(10, 10, 100),
  status = sample(c(0, 1), 10, replace = TRUE)
)
rownames(surv_data) <- colnames(meth_data)
res <- bootstrap_validate_ims(
  meth_data = meth_data, surv_data = surv_data,
  immune_genes = c("CD274"), B = 5,
  p_threshold = 1, min_cpgs = 2
)

Build an Immune Methylation Score (IMS)

Description

Build an Immune Methylation Score (IMS)

Usage

build_ims(
  meth_data,
  surv_data,
  immune_genes = NULL,
  p_threshold = 0.05,
  hr_range = c(0.1, 10),
  min_cpgs = 3L
)
build_ims(
  meth_data,
  surv_data,
  immune_genes = NULL,
  p_threshold = 0.05,
  hr_range = c(0.1, 10),
  min_cpgs = 3L
)

Arguments

meth_data

A matrix of methylation beta values (CpGs x samples).

surv_data

A data frame with columns 'time' and 'status'.

immune_genes

Character vector of immune gene symbols. If NULL, uses a default 35-gene immune panel.

p_threshold

Numeric. P-value threshold for CpG selection. Default 0.05.

hr_range

Numeric vector length 2. Allowed HR range. Default c(0.1, 10).

min_cpgs

Integer. Minimum CpGs required. Default 3.

Value

A list with IMS, selected_cpgs, coefs, model.

Examples

# Source: Mock data simulated for demonstration purposes.
set.seed(123)
meth_data <- matrix(runif(100), nrow = 10, ncol = 10)
rownames(meth_data) <- paste0("CD274_cg", 1:10)
colnames(meth_data) <- paste0("Sample", 1:10)
surv_data <- data.frame(
  time = runif(10, 10, 100),
  status = sample(c(0, 1), 10, replace = TRUE)
)
rownames(surv_data) <- colnames(meth_data)
res <- build_ims(
  meth_data = meth_data, surv_data = surv_data,
  immune_genes = c("CD274"), p_threshold = 1,
  hr_range = c(0, 100), min_cpgs = 2
)
# Source: Mock data simulated for demonstration purposes.
set.seed(123)
meth_data <- matrix(runif(100), nrow = 10, ncol = 10)
rownames(meth_data) <- paste0("CD274_cg", 1:10)
colnames(meth_data) <- paste0("Sample", 1:10)
surv_data <- data.frame(
  time = runif(10, 10, 100),
  status = sample(c(0, 1), 10, replace = TRUE)
)
rownames(surv_data) <- colnames(meth_data)
res <- build_ims(
  meth_data = meth_data, surv_data = surv_data,
  immune_genes = c("CD274"), p_threshold = 1,
  hr_range = c(0, 100), min_cpgs = 2
)

Compare IMS with Clinical Variables

Description

Compares prognostic performance of IMS vs a clinical variable (e.g., Stage) using Cox models and likelihood ratio test.

Usage

compare_ims_clinical(
  ims_values,
  surv_data,
  clinical_var,
  var_name = "Clinical"
)
compare_ims_clinical(
  ims_values,
  surv_data,
  clinical_var,
  var_name = "Clinical"
)

Arguments

ims_values

Named numeric vector of IMS values (from build_ims).

surv_data

Data frame with columns 'time' and 'status'.

clinical_var

Named vector or factor of clinical variable (e.g., Stage_binary) aligned with surv_data rownames.

var_name

Character. Name of clinical variable for output labels.

Value

A list with C-index for IMS alone, clinical alone, both, Delta C-index, and LRT p-value.

Examples

# Source: Mock data simulated for demonstration purposes.
set.seed(123)
n <- 60
ims_values <- setNames(rnorm(n), paste0("Sample", 1:n))
surv_data <- data.frame(
  time = runif(n, 10, 100),
  status = sample(c(0, 1), n, replace = TRUE),
  row.names = names(ims_values)
)
clinical_var <- setNames(
  sample(c("Low", "High"), n, replace = TRUE),
  names(ims_values)
)
res <- compare_ims_clinical(
  ims_values = ims_values, surv_data = surv_data,
  clinical_var = clinical_var, var_name = "Stage"
)
# Source: Mock data simulated for demonstration purposes.
set.seed(123)
n <- 60
ims_values <- setNames(rnorm(n), paste0("Sample", 1:n))
surv_data <- data.frame(
  time = runif(n, 10, 100),
  status = sample(c(0, 1), n, replace = TRUE),
  row.names = names(ims_values)
)
clinical_var <- setNames(
  sample(c("Low", "High"), n, replace = TRUE),
  names(ims_values)
)
res <- compare_ims_clinical(
  ims_values = ims_values, surv_data = surv_data,
  clinical_var = clinical_var, var_name = "Stage"
)

Estimate Tumor Purity and Immune Score

Description

Computes ESTIMATE scores (Stromal, Immune, ESTIMATE) and Tumor Purity from gene expression data using the ESTIMATE algorithm. Requires package 'tidyestimate'.

Usage

estimate_purity(expr_data, is_affymetrix = FALSE)
estimate_purity(expr_data, is_affymetrix = FALSE)

Arguments

expr_data

A matrix of gene expression (genes x samples).

is_affymetrix

Logical. TRUE if data is from Affymetrix arrays. Default FALSE (RNA-seq).

Value

A data frame with columns: sample, stromal, immune, estimate, purity.

Examples

# Source: Mock data simulated to demonstrate the ESTIMATE algorithm.
set.seed(123)
expr_mat <- matrix(rnorm(1000), nrow = 100, ncol = 10)
rownames(expr_mat) <- paste0("GENE", 1:100)
colnames(expr_mat) <- paste0("Sample", 1:10)
tryCatch({
  res <- estimate_purity(expr_mat)
  head(res)
}, error = function(e) {
  message("estimate_purity requires 'tidyestimate' package.")
})
# Source: Mock data simulated to demonstrate the ESTIMATE algorithm.
set.seed(123)
expr_mat <- matrix(rnorm(1000), nrow = 100, ncol = 10)
rownames(expr_mat) <- paste0("GENE", 1:100)
colnames(expr_mat) <- paste0("Sample", 1:10)
tryCatch({
  res <- estimate_purity(expr_mat)
  head(res)
}, error = function(e) {
  message("estimate_purity requires 'tidyestimate' package.")
})

Integrate Methylation and Gene Expression Data

Description

Integrates DNA methylation and gene expression data to identify genes whose expression is significantly correlated with their promoter methylation status. This is a key step in multi-omics analysis for discovering epigenetically regulated genes in any cancer type.

Usage

integrate_meth_expr(
  mae,
  meth_assay = "methylation",
  expr_assay = "expression",
  gene_map = NULL,
  correlation_method = "pearson",
  min_samples = 5,
  p_cutoff = 0.05,
  cor_cutoff = 0.3,
  verbose = TRUE
)
integrate_meth_expr(
  mae,
  meth_assay = "methylation",
  expr_assay = "expression",
  gene_map = NULL,
  correlation_method = "pearson",
  min_samples = 5,
  p_cutoff = 0.05,
  cor_cutoff = 0.3,
  verbose = TRUE
)

Arguments

mae

A MultiAssayExperiment object created by load_cancer_data, containing both methylation and expression assays.

meth_assay

Character. Name of the methylation assay. Default "methylation".

expr_assay

Character. Name of the expression assay. Default "expression".

gene_map

Optional. A data frame mapping methylation probes to genes. Must have columns probe and gene. If NULL, the function assumes probe and gene identifiers match (i.e., row names of both assays are gene symbols).

correlation_method

Character. Correlation method, one of "pearson" (default) or "spearman".

min_samples

Integer. Minimum number of samples required for correlation analysis. Default 5.

p_cutoff

Numeric. Adjusted p-value threshold for significance. Default 0.05.

cor_cutoff

Numeric. Minimum absolute correlation coefficient to consider a methylation-expression pair significant. Default 0.3.

verbose

Logical. Whether to print progress messages. Default TRUE.

Value

A data frame with the following columns:

gene: Gene identifier
probe: Methylation probe identifier
correlation: Correlation coefficient between methylation and expression
p_value: Raw p-value of the correlation test
adj_p_value: Adjusted p-value (Benjamini-Hochberg)
n_samples: Number of samples used in the correlation
regulation: "Negative" if methylation is inversely correlated with expression (expected for promoter methylation), "Positive" otherwise
significant: Logical, whether the pair passes both p-value and correlation cutoffs

Examples

if (interactive()) {
# Load multi-omics data
brca <- load_cancer_data("brca_tcga",
                         data_types = c("methylation", "expression"))

# Integrate methylation and expression
integrated <- integrate_meth_expr(brca)

# Filter significant negative correlations
epi_genes <- integrated[integrated$significant &
                         integrated$regulation == "Negative", ]
}

if (interactive()) {
# Load multi-omics data
brca <- load_cancer_data("brca_tcga",
                         data_types = c("methylation", "expression"))

# Integrate methylation and expression
integrated <- integrate_meth_expr(brca)

# Filter significant negative correlations
epi_genes <- integrated[integrated$significant &
                         integrated$regulation == "Negative", ]
}

Load Multi-Omics Cancer Data

Description

Loads multi-omics data for any cancer type from cBioPortal, TCGA, or user-provided datasets. The function returns a MultiAssayExperiment object that can be used by all downstream analysis functions in the package.

Usage

load_cancer_data(
  cancer_type,
  data_types = c("methylation", "expression", "mirna"),
  source = "cBioPortal",
  genes = NULL,
  custom_data = NULL,
  verbose = TRUE
)
load_cancer_data(
  cancer_type,
  data_types = c("methylation", "expression", "mirna"),
  source = "cBioPortal",
  genes = NULL,
  custom_data = NULL,
  verbose = TRUE
)

Arguments

cancer_type

Character. Cancer study identifier. For cBioPortal, use study IDs such as "brca_tcga", "luad_tcga", "coadread_tcga". For a full list, see cBioPortalData::getStudies().

data_types

Character vector. Omics layers to load. Options include "methylation", "expression", and "mirna". Default loads all three.

source

Character. Data source. One of "cBioPortal" (default), "custom", or "TCGA".

genes

Optional character vector of gene symbols to restrict the download (e.g., c("TP53", "BRCA1", "EGFR")). Only used when source = "cBioPortal". If NULL (default), all available genes are downloaded, which can be very slow for large studies.

custom_data

Optional. A named list of matrices for custom data. Names must match data_types. Only used when source = "custom".

verbose

Logical. Whether to print progress messages. Default TRUE.

Value

A MultiAssayExperiment object containing the requested omics layers and associated clinical data.

Examples

if (interactive()) {
# Load breast cancer data from cBioPortal (all genes)
brca <- load_cancer_data("brca_tcga")

# Load only selected genes (faster)
brca <- load_cancer_data(
  "brca_tcga",
  data_types = c("methylation", "expression"),
  genes = c("TP53", "BRCA1", "BRCA2", "EGFR")
)

# Load custom data
custom <- load_cancer_data("my_study",
                           source = "custom",
                           custom_data = list(methylation = meth_matrix,
                                              expression = expr_matrix))
}

if (interactive()) {
# Load breast cancer data from cBioPortal (all genes)
brca <- load_cancer_data("brca_tcga")

# Load only selected genes (faster)
brca <- load_cancer_data(
  "brca_tcga",
  data_types = c("methylation", "expression"),
  genes = c("TP53", "BRCA1", "BRCA2", "EGFR")
)

# Load custom data
custom <- load_cancer_data("my_study",
                           source = "custom",
                           custom_data = list(methylation = meth_matrix,
                                              expression = expr_matrix))
}

Visualization Functions for Pan-Cancer Multi-Omics Analysis

Description

A collection of publication-quality plotting functions for visualizing results from differential methylation, differential expression, multi-omics integration, and survival analysis. All plots are based on ggplot2 and return ggplot objects that can be further customized.

Usage

plot_cancer(
  data = NULL,
  plot_type = c("volcano", "heatmap", "correlation", "survival"),
  title = NULL,
  p_cutoff = 0.05,
  fc_cutoff = NULL,
  color_up = "firebrick",
  color_down = "steelblue",
  color_ns = "grey70",
  max_labels = 10,
  surv_result = NULL,
  verbose = TRUE
)
plot_cancer(
  data = NULL,
  plot_type = c("volcano", "heatmap", "correlation", "survival"),
  title = NULL,
  p_cutoff = 0.05,
  fc_cutoff = NULL,
  color_up = "firebrick",
  color_down = "steelblue",
  color_ns = "grey70",
  max_labels = 10,
  surv_result = NULL,
  verbose = TRUE
)

Arguments

data

A data frame returned by analyze_methylation, analyze_expression, or integrate_meth_expr.

plot_type

Character. Type of plot to generate. One of "volcano", "heatmap", "correlation", or "survival".

title

Optional. Plot title. If NULL, a default title is used.

p_cutoff

Numeric. Adjusted p-value threshold for highlighting significant features. Default 0.05.

fc_cutoff

Numeric. Log fold change or delta-beta threshold for highlighting significant features. Default 1 for expression and 0.1 for methylation. If NULL, uses the significant column in the data.

color_up

Character. Color for upregulated/hypermethylated features. Default "firebrick".

color_down

Character. Color for downregulated/hypomethylated features. Default "steelblue".

color_ns

Character. Color for non-significant features. Default "grey70".

max_labels

Integer. Maximum number of labels to display on volcano plots. Default 10.

surv_result

Optional. A list returned by analyze_survival for survival plots.

verbose

Logical. Whether to print progress messages. Default TRUE.

Value

A ggplot object.

Examples

if (interactive()) {
# Volcano plot of differential expression
deg <- analyze_expression(brca)
plot_cancer(deg, plot_type = "volcano")

# Volcano plot of differential methylation
dmr <- analyze_methylation(brca)
plot_cancer(dmr, plot_type = "volcano")

# Survival plot
surv <- analyze_survival(brca, feature_name = "TP53")
plot_cancer(plot_type = "survival", surv_result = surv)
}

if (interactive()) {
# Volcano plot of differential expression
deg <- analyze_expression(brca)
plot_cancer(deg, plot_type = "volcano")

# Volcano plot of differential methylation
dmr <- analyze_methylation(brca)
plot_cancer(dmr, plot_type = "volcano")

# Survival plot
surv <- analyze_survival(brca, feature_name = "TP53")
plot_cancer(plot_type = "survival", surv_result = surv)
}

Validate Genes at Protein Level

Description

Validates genes using UniProt (protein existence).

Usage

validate_proteins(genes, use_uniprot = TRUE, timeout = 30)
validate_proteins(genes, use_uniprot = TRUE, timeout = 30)

Arguments

genes

Character vector of gene symbols.

use_uniprot

Logical. Query UniProt? Default TRUE.

timeout

Numeric. HTTP timeout in seconds. Default 30.

Value

A data frame with columns: gene, protein_exists, protein_name.

Examples

# Source: Mock data for offline demonstration.
genes <- c("BRCA1", "TP53", "CD274")
res <- validate_proteins(genes, use_uniprot = FALSE)
head(res)
# Source: Mock data for offline demonstration.
genes <- c("BRCA1", "TP53", "CD274")
res <- validate_proteins(genes, use_uniprot = FALSE)
head(res)

Package 'MultiOmicsCancer'

Help Index

Differential Gene Expression Analysis for Pan-Cancer Studies

Description

Usage

Arguments

Value

Examples

Differential Methylation Analysis for Pan-Cancer Studies

Description

Usage

Arguments

Value

Examples

Survival Analysis for Pan-Cancer Studies

Description

Usage

Arguments

Value

Examples

Nested Bootstrap Validation for IMS

Description

Usage

Arguments

Value

Examples

Build an Immune Methylation Score (IMS)

Description

Usage

Arguments

Value

Examples

Compare IMS with Clinical Variables

Description

Usage

Arguments

Value

Examples

Estimate Tumor Purity and Immune Score

Description

Usage

Arguments

Value

Examples

Integrate Methylation and Gene Expression Data

Description

Usage

Arguments

Value

Examples

Load Multi-Omics Cancer Data

Description

Usage

Arguments

Value

Examples

Visualization Functions for Pan-Cancer Multi-Omics Analysis

Description

Usage

Arguments

Value

Examples

Validate Genes at Protein Level

Description

Usage

Arguments

Value

Examples