gene expression

performs gene expression analysis

read_gene_expression_file

reading gene expression file and converting it into dataframe for further analysis

Usage:

expression_data = ge.read_gene_expression_file('mouseretina.txt')
print(expression_data)

gene_expression_normalization

This function normalizes raw gene expression data to remove technical variations and biases, which can affect downstream analysis.

Usage:

ge.gene_expression_normalization(expression_data, method='quantile')

get_group_labels

Get Group Labels For differential_gene_expression_analysis

Usage:

group_labels = ge.get_group_labels(norm)

differential_gene_expression_analysis

This function performs differential gene expression analysis to identify genes that are differentially expressed between two or more conditions or group_labels

Usage:

ge.differential_gene_expression_analysis(expression_data, group_labels)

gene_expression_quantification

This function quantifies gene expression levels using various methods such as RPKM, TPM, or FPKM, which can be used as input for downstream analysis.

Usage:

ge.gene_expression_quantification(counts_matrix, lengths)

gene_expression_correlation

This function calculates the correlation between gene expression levels and various phenotypic or clinical variables, such as disease status, age, or gender, which can help identify potential biomarkers or therapeutic targets.

Usage:

ge.gene_expression_correlation(expression_data, clinical_data)