dgeapy.py is a Python toolkit for analyzing RNAseq data, focusing on differential gene expression and intersections among datasets.
Create a dedicated environment for dgeapy using Conda:
conda create --name dgeapy python=3.10
conda activate dgeapyInstall all necessary libraries with Conda:
conda install pandas numpy openpyxl matplotlib seaborn matplotlib-venn UpSetPlot- pandas: For dataframe analysis and manipulation.
- numpy: Numerical computing tasks.
- openpyxl: Reading and writing Excel xlsx files.
- matplotlib: Data visualization.
- seaborn: High-level manipulation of figures.
- matplotlib-venn: Venn diagrams.
- UpSetPlot: UpSet plots.
Available scripts are:
python dgeapy/dgeapy.py -h
Differential Gene Expression Analyisis in Python at different levels.
Usage: python dgeapy.py <COMMAND> [OPTIONS]
Commands:
analyze Perform differential gene expression analyisis
intersect Find intersections between indexes of multiple files
Options:
-h, --help Show this help message and exit
-V, --version Show version number and exit
Examples:
python dgeapy.py analyze -h
python dgeapy.py analyze <mygenes.csv> -f 2.0 -p 0.01
python dgeapy.py intersect -f <mutA.csv> -f <mutB.csv> -n "Mutant A" -n "Mutant B" -i gene_id
Determine the differentially expressed genes from a dataframe.
$ python dgeapy/dgeapy.py analyze -h
usage: dgeapy.py analyze <TABLE> [OPTIONS]
Differential Gene Expression Analysis.
Generates tables with differentially expressed plots to visualize the results.
positional arguments:
<TABLE> Path to the gene expression data file (CSV, TSV, XLSX).
options:
-h, --help show this help message and exit
-o, --output DIR Specify the output directory (default: cwd).
-p, --padj FLOAT Adjusted p-value threshold for significance (default: 0.05).
-f, --fold-change FLOAT Fold change threshold for significance (default: 1.5).
-F, --formats [STR] Output formats for plots (e.g. svg, pdf) (default: ['png']).
-e, --exclude [STR] Exclude indexes matching specified patterns.
-N, --nan-values [STR] Strings to recognize as NaN (default: ['', '--']).
-k, --keep-duplicated Keep duplicated indexes (default: False).
-I, --index-column STR Column name for index (default: index).
-L, --log2fc-column STR Column name for log2 Fold Change (default: log2_fold_change).
-P, --p-column STR Column name for adjusted p-values (default: padj).
Script workflow summary:
- Take a table in CSV, TSV, or XLSX format.
- Verify and clean the data by checking for NaN values, duplicated values in the index, and excluding indexes with specific patterns using
--exclude. - Utilize
--index-columnto index each row and add fold change and regulation columns. - Identify differentially expressed genes (DEG) by applying thresholds for p-adjusted value (
--padj) and fold change absolute value (--fc). - Output three tables (DEGs, upregulated and downregulated) and two figures (a bar plot and a volcano plot).
python dgeapy/dgeapy.py analyze example/data/GSE206442.xlsx -o example/analyze_output -L log2FoldChange -N NAOutput tables and figures can be found in example/analyze_output.
Example data can be downloaded from GSE206442.
Compute intersections of indexes among a list of dataframes.
$ python dgeapy/dgeapy.py intersect -h
usage: dgeapy.py intersections -f <file1> -f <file2> [...] -n <name1> -n <name2> [...] [OPTIONS]
Computes intersections between multiple data files and generates comprehensive intersection
tables and visualizations.
options:
-h, --help show this help message and exit
-o, --output DIR Specify the output directory for results (default: cwd).
-i, --index-column STR Name of the index column in the data files (default: index).
-F, --formats [STR] Output formats for the plots (e.g. svg) (default: ['png', 'pdf']).
-N, --nan-values [STR] Strings to recognize as NaN (default: ['', '--', 'NA']).
-e, --exclude [STR] Exclude indexes matching specified patterns.
required arguments:
-f, --files [<FILE>] Paths to the data files for intersection analysis.
-n, --names [STR] Names for the data files to label plots and tables.
Script workflow summary:
- Take multiple dataframes along with their assigned names.
- Validate and prepare data by checking for NaN, null, or duplicated values in the indexes, excluding specific patterns with
--exclude. - Compute all possible intersections between the indexes of the provided dataframes.
- Generate TSV and XLSX files for each non-empty intersection to document results.
- Produce visual representations of intersections: automatically generate a weighted and unweighted Venn Diagram for up to three dataframes, or an UpSet Plot for larger sets to visualize the present and missing intersections.
Intersection analysis between 3 files:
python dgeapy/dgeapy.py intersect -f example/data/condition1.xlsx -f example/data/condition2.xlsx -f example/data/condition3.xlsx -n "Condition 1" -n "Condition 2" -n "Condition 3" -o example/intersect3_outputResults can be found in example/intersect3_output
Example of a generated Venn Diagram:
Intersection analysis between 4 files:
python dgeapy/dgeapy.py intersect -f example/data/condition1.xlsx -f example/data/condition2.xlsx -f example/data/condition3.xlsx -f example/data/condition5.xlsx -n "Condition 1" -n "Condition 2" -n "Condition 3" -n "Condition 4" -o example/intersect4_outputResults can be found in example/intersect4_output
Example of a generated UpSet Plot:
