fold_change: calculation of Fold-Change

In Drinchai/BloodGen3Module: This R package for performing module repertoire analyses and generating fingerprint representations

Description

A function to calculate fold-change between group comparison; "Test_group" vs "Ref_group"

Usage

fold_change(
  df_raw = df_raw,
  sample_info = sample_info,
  Group_column = Group_column,
  Test_group = Test_group,
  Ref_group = Ref_group
)

Arguments

df_raw	Matrix of normalized expression data (not Log2 transformed). Genes should be in rows and Sample ID in columns. Row names are required to be valid Gene Symbols
sample_info	A dataframe with sample annotation. Sample_info dataframe requires two columns: 1) a column specifying Sample ID (exactly matching Sample ID of data.matrix) and 2) a column specifying group names
Group_column	Character vector identical to the column name from sample_info dataframe that specifies group annotation used for the analysis
Test_group	Character vector specifying values within the group column (Group_column) that will be used as Test group (samples considered as cases or “intervention” group).
Ref_group	Character vector specifying value within the group column (Group_column) that will be used as Reference group

Value

A matrix of the fold change comparison between "Test_group" vs ""Ref_group"

Author(s)

Darawan Rinchai [e-mail]

Examples

## data could be downloaded from ExperimentHub("GSE13015")
library(ExperimentHub)
library(SummarizedExperiment)
dat = ExperimentHub()
res = query(dat , "GSE13015")
GSE13015 = res[["EH5429"]]
data_matrix = assay(GSE13015)
sample_ann = data.frame(colData(GSE13015))

FCgroup = fold_change(df_raw = data_matrix[c(1:5),],
                     sample_info = sample_ann,
                     Group_column = "Group_test",
                     Test_group="Sepsis",
                     Ref_group="Control")

Drinchai/BloodGen3Module documentation built on Sept. 20, 2021, 10:31 p.m.