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pyDESeq2 pairwise differential analysis

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Typing name :  TASK.gws_omix.pyDESeq2DifferentialAnalysis Brick :  gws_omix

Compute differential analysis using pyDESeq2 python package (pairwise comparison)

PyDESeq2, a Python implementation of the DESeq2 method originally developed in R (click here) , is a versatile tool for conducting differential expression analysis (DEA) with bulk RNA-seq data. This re-implementation yields similar, but not identical, results: it achieves higher model likelihood, allows speed improvements on large datasets. By implementing Wald tests, PyDESeq2 enables users to statistically evaluate the significance of these expression differences, providing a robust framework for unraveling the nuanced relationships between genes in RNA-seq studies.

Input

count table matrix
count table matrix
File
metadata file
tsv metadata file
File

Output

Differantial expression result
Differential expression results ,providing a summary of genes showing significant changes in expression levels between two conditions.
Table
Principal Component Analysis
Show the difference after dimensional reduction via principal component analysis.
Plotly resource
Interactive Average Hierarchical Clustering Heatmap
The average linkage method and the Euclidean distance metric was used for hierarchical clustering. Utilizing scipy's hierarchical clustering, the code groups genes and samples, and then rearranges the original DataFrame based on the clustering outcomes. The resulting heatmap, created using Plotly, visually represents the reordered data, making it easier to discern patterns and relationships within the gene expression dataset.
Plotly resource
Interactive Volcano plot
This plot permit to visualize the relationship between the log2 fold change and adjusted p-values for each gene. The color scale represents log2 fold change values, and the size of the points is controlled for better visibility. The resulting plot, titled 'Volcano Plot,' provides insights into gene expression changes and their statistical significance.
Plotly resource
heatmap
displaying average expression levels across different groups with rows representing individual genes ensembl id and columns representing samples. The hierarchical clustering dendrograms are typically displayed on the side of the heatmap, showing the relationships between samples based on their similarity in expression profiles.
File
volcano plot
Top 30 statistical significance ( represented as p-values) on the y-axis against fold change values on the x-axis for each feature (genes) in a dataset
File

Configuration

genes_colname

Column name containing gene ids in expression matrix

Type : string

control_condition

normal_condition

Type : string

unnormal_condition

unnormal_condition

Type : string

padj_value

Optional

padj_value

Type : floatDefault value : 0.05

log2FoldChange_value

Optional

log2FoldChange value

Type : floatDefault value : 0.5