Description
Differential Gene Expression Analysis with R.
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>.
README.md
DGEAR
The goal of DGEAR is to help the researchers find differentially expressed gene from microarray gene expression data or from the RNA seq count data the easiest way possible.
Installation
You can install the development version of DGEAR like so:
# Simple installation:
install.packages("DGEAR")
Library calling
library(DGEAR)
Data format
DGEAR has it's own example data that can be accessible like so:
# Data will be loaded with lazy loading and can be accessible when needed.
data("gene_exp_data")
head(gene_exp_data)
How it works
Here's an example code to run the example dataset.
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)
Try to find the DEGs from the example dataset with all the default arguments and understand it's working.