analysis_deps_sam: Differential expression analysis using SAM

In ecnuzdd/PhosMap: A Comprehensive R Package For Analyzing Quantitative Phosphoproteomics Data

Differential expression analysis using SAM

Description

Differential expression analysis using SAM

Usage

analysis_deps_sam(
  expr_data_frame,
  group,
  log2_label = FALSE,
  nperms = 100,
  rand = NULL,
  minFDR = 0.05,
  samr_plot = TRUE
)

Arguments

expr_data_frame	A data frame containing ID and quantification values.
group	A factor representing experimental groups.
log2_label	A boolean value for representing whether the value is logarithmic or not, the default is FALSE.
nperms	Number of permutations used to estimate false discovery rates.
rand	if specified, the random number generator will be put in a reproducible state.
minFDR	A numeric value for filtering significant genes, the default is 0.05.
samr_plot	A boolean value for representing whether samr graph is plotted or not.

Value

A list containing results from sam analysis.

Author(s)

Dongdong Zhan and Mengsha Tong

References

R. Tibshirani, G. Chu, T. Hastie and Balasubramanian Narasimhan (2010). samr: SAM: Significance Analysis of Microarrays.\ Rpackage version 1.28. https://CRAN.R-project.org/package=samr

Examples

## The process needs to load data from PhosMap datasets stored into FTP server and perform large computation.
## It may take a few minutes.
if(FALSE){
ftp_url <- "ftp://111.198.139.72:4000/pub/PhosMap_datasets/function_demo_data/analysis_deps_sam.RData"
load_data <- load_data_with_ftp(ftp_url, 'RData')
writeBin(load_data, "analysis_deps_sam.RData")
load("analysis_deps_sam.RData")

sam_results_list <- analysis_deps_sam(
  expr_data_frame, group, log2_label = FALSE,
  nperms = 100, rand = NULL, minFDR = 0.05,samr_plot = TRUE
)
head(sam_results_list)

}

ecnuzdd/PhosMap documentation built on Dec. 7, 2022, 4:09 a.m.