perform_main_ppi: Pipeline of PPI-based analysis.

In ys-amms/bionexr: Integrative network-based analysis of cancer somatic mutation and expression data

Description

This is the PPI-based analysis pipeline.

Usage

perform_main_ppi(df_user_mut, df_user_exp, sid_pattern = c("-01$", "-11$"),
  mcutoff = 2, fraction = 0.6, nei_order = 2, mweight = 0.6,
  padj = 0.01, log2_fold_change = 2, jobname = "tcga", raw_count = TRUE,
  use_cache = FALSE)

Arguments

df_user_mut	Mutation dataframe.
df_user_exp	Gene expression dataframe.
sid_pattern	A regex string to match case and control IDs.
mcutoff	A float number between 0 and 6, default is 2.
fraction	A float number between 0 and 1, default is 0.6.
nei_order	The order of neighborhood from the mutant gene, default is 2.
mweight	weight of mutation score for combined score, default is 0.6.
padj	Adjusted p value threshold for differential analysis, default is 0.01.
log2_fold_change	log2 transformed fold change threshold for differential analysis, default is 2.
jobname	unique job name.
raw_count	Logical value, set TRUE to process RSEM-based expression data.
use_cache	Logical value, set TRUE to save intermediate result.

Details

df_user_mut is a dataframe with 7 columns, which are "Hugo_Symbol", "Chromosome", "Start_position", "End_position", "Reference_Allele", "Tumor_Seq_Allele2", "Tumor_Sample_Barcode". df_user_exp is a dataframe, row names are gene symbols, and column names are sample IDs. sid_pattern is a regex string that can distingish case ID and control ID.

Value

A list.

Examples

## Not run: 
mut_data <- firehose_get("HNSC", "mutation", run_date = "2015_08_21", run_type = "stddata")
mut_data <- mut_data[[1]]
mut_sample_ids <- unique(mut_data[[7]])

exp_data <- firehose_get("HNSC", "expression", run_date = "2015_08_21", run_type = "stddata")
exp_data <- exp_data[[1]]
exp_sample_ids <- colnames(exp_data)

common_case <- intersect(mut_sample_ids, exp_sample_ids)
exp_control <- grepl("-11$", exp_sample_ids)
hnsc_mut <- mut_data[mut_data[[7]] %in% common_case, ]
hnsc_exp <- exp_data[, (exp_sample_ids %in% common_case) | exp_control]

prepare_ma()
res <- perform_main_ppi(hnsc_mut, hnsc_exp, jobname = "HNSC", use_cache = TRUE)

## End(Not run)

ys-amms/bionexr documentation built on May 4, 2019, 5:33 p.m.