Type:	Package
Title:	Differential Gene Expression Analysis with R
Version:	0.1.4
Author:	Koushik Bardhan [aut, cre, ctb], Chiranjib Sarkar [aut, ths]
Maintainer:	Koushik Bardhan <koushikbardhan2000@gmail.com>
Description:	Analyses gene expression data derived from experiments to detect differentially expressed genes by employing the concept of majority voting with five different statistical models. It includes functions for differential expression analysis, significance testing, etc. It simplifies the process of uncovering meaningful patterns and trends within gene expression data, aiding researchers in downstream analysis. Boyer, R.S., Moore, J.S. (1991) <doi:10.1007/978-94-011-3488-0_5>.
License:	MIT + file LICENSE
Encoding:	UTF-8
LazyData:	true
Imports:	DescTools
RoxygenNote:	7.3.1
Depends:	R (≥ 2.10)
Suggests:	spelling, testthat (≥ 3.0.0)
Config/testthat/edition:	3
NeedsCompilation:	no
Packaged:	2024-06-26 20:08:54 UTC; koush
Repository:	CRAN
Date/Publication:	2024-06-26 20:20:02 UTC
Language:	en-US

Differential Gene Expression Analysis with R

Description

Main function which incorporates results from five statistical models and detects DEGs through majority voting.

Usage

DGEAR(dataframe, con1, con2, exp1, exp2, alpha, votting_cutoff)

Arguments

Details

To use this tool the necessary parameters are con1 = Control start column, con2 = Control end column, exp1 = Experiment start column, exp2 = Experiment end column, alpha = Value of significance level, voting_cutoff = Majority voting value (not more than 5, since there are 5 statistical methods which take part in the majority voting)

Value

A matrix containing Differentially Expressed Genes(DEGs) detected

Examples

library(DGEAR)
data("gene_exp_data")
DGEAR(dataframe = gene_exp_data, con1 = 1, con2 = 10,
  exp1 = 11, exp2 = 20, alpha = 0.05, votting_cutoff = 2)

A dataset containing gene expression data

Description

This dataset contains statistically simulated gene expression data for ease of exercise.

Usage

gene_exp_data

Format

A data frame with 10 rows and 20 columns, the columns represents samples, say first 10 columns 1 to 10 being control and 11 to 20 being experiment. Whereas, the rows of the dataset contains genes. First 5 out of 10 genes, gene1-gene5 are the true DEGs as the expression values for the first 10 samples are ~13 times higher than the rest.

Examples

# Data will be loaded with lazy loading and can be accessible when needed.
data("gene_exp_data")
head(gene_exp_data)

Function for ANOVA One-Way Test

Description

Function for ANOVA One-Way Test

Usage

perform_anova(datafile, con, exp, alpha = 0.05)

Arguments

Value

A data frame containing values for statistic score, p-values etc for each gene being tested.

Examples

library(DGEAR)
data("gene_exp_data")
data = read_and_preprocess_data(datafile = gene_exp_data, con1=1,con2=10,exp1=11,exp2=20)
perform_anova(datafile = data$datafile, con= data$con, exp= data$exp)

Function for Dunnett's Test

Description

Function for Dunnett's Test

Usage

perform_dunnett_test(datafile, con, exp, alpha = 0.05)

Arguments

Value

A data frame containing values for statistic score, p-values etc for each gene being tested.

Examples

library(DGEAR)
data("gene_exp_data")
data = read_and_preprocess_data(datafile = gene_exp_data, con1=1,con2=10,exp1=11,exp2=20)
perform_dunnett_test(datafile = data$datafile, con= data$con, exp= data$exp)

Function for Half's-T-Test Analysis

Description

Function for Half's-T-Test Analysis

Usage

perform_h_test(con, exp, alpha = 0.05, FC)

Arguments

Value

A data frame containing values for statistic score, p-values etc for each gene being tested.

Examples

library(DGEAR)
data("gene_exp_data")
data = read_and_preprocess_data(datafile = gene_exp_data, con1=1,con2=10,exp1=11,exp2=20)
perform_h_test(con= data$con, exp= data$exp, FC = data$FC)

Function for t-Test Analysis

Description

Function for t-Test Analysis

Usage

perform_t_test(con, exp, alpha = 0.05)

Arguments

Value

A data frame containing values for statistic score, p-values etc for each gene being tested.

Examples

library(DGEAR)
data("gene_exp_data")
data = read_and_preprocess_data(datafile = gene_exp_data, con1=1,con2=10,exp1=11,exp2=20)
perform_t_test(con= data$con, exp= data$exp)

Function for Wilcoxon-Mann-Whitney U-Test

Description

Function for Wilcoxon-Mann-Whitney U-Test

Usage

perform_wilcox_test(con, exp, alpha = 0.05)

Arguments

Value

A data frame containing values for statistic score, p-values etc for each gene being tested.

Examples

library(DGEAR)
data("gene_exp_data")
data = read_and_preprocess_data(datafile = gene_exp_data, con1=1,con2=10,exp1=11,exp2=20)
perform_wilcox_test(con= data$con, exp= data$exp)

Function to read data and perform initial pre-processing

Description

Function to read data and perform initial pre-processing

Usage

read_and_preprocess_data(
  datafile,
  con1,
  con2,
  exp1,
  exp2,
  alpha = 0.05,
  votting_cutoff = 2
)

Arguments

Value

A large list containing the data file and the input values

Examples

data("gene_exp_data")
read_and_preprocess_data(datafile = gene_exp_data, con1=1,con2=10,exp1=11,exp2=20)