omicverse.bulk.pyDEG

omicverse.bulk.pyDEG#

class omicverse.bulk.pyDEG(raw_data)[source]#

Differential-expression analysis helper for bulk RNA-seq count tables.

Parameters:

raw_data (pd.DataFrame) – Raw count matrix with genes in rows and samples in columns.

__init__(raw_data)[source]#

Initialize the pyDEG class.

Parameters:

raw_data (DataFrame) – The raw data to be processed.

Returns:

None

Methods

__init__(raw_data)

Initialize the pyDEG class.

continuous_deg(trait[, covariates, alpha, ...])

Differential expression against a CONTINUOUS sample-level trait.

deg_analysis(group1, group2[, method, ...])

Differential expression analysis.

drop_duplicates_index()

Drop the duplicated index of data.

foldchange_set([fc_threshold, ...])

Set fold-change and p-value thresholds to classify differentially expressed genes as up-regulated, down-regulated, or not significant.

normalize()

Normalize the data using DESeq2 method.

plot_boxplot(genes, treatment_groups, ...[, ...])

Plot the boxplot of genes from dds data

plot_volcano([figsize, pval_name, fc_name, ...])

Generate a volcano plot for the differential gene expression analysis results.

ranking2gsea([rank_max, rank_min])

Ranking the result of dds data for gsea analysis

timecourse_deg(time[, group, block, ...])

Time-course / longitudinal differential-expression analysis.
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