BULLKpy 🧬#

BULLKpy is a Python framework for comprehensive bulk OMICs data analysis,
with a strong focus on biomedical and cancer research.

It provides a unified, AnnData-inspired workflow to perform:

Quality control and preprocessing
Dimensionality reduction and clustering
Differential expression analysis
Pathway and gene set enrichment
Metaprograms and tumor heterogeneity analysis
Survival analysis and clinical associations
Publication-ready visualization

BULLKpy on GitHub

BULLKpy is designed to integrate seamlessly with the scverse ecosystem,
and to help standardize and democratize bulk transcriptomics analysis in Python.

🚀 Installation#

Clone the repository:

git clone https://github.com/malumbres/BULLKpy.git
cd BULLKpy

Install from Pypi: https://pypi.org/project/bullkpy/

pip install bullkpy

📘 Quickstart#

import bullkpy as bk
import pandas as pd                        # for better manipulation of metadata
import seaborn as sns                      # for some plots
import gseapy as gspy                      # Additional GSEA tools

## Data input and loading
adata = bk.io.read_counts("counts.tsv").      # when using counts
bk.io.add_metadata(adata, metadata)

## Preprocessing
bk.pp.qc_metrics(adata)
bk.pl.qc_metrics(
    adata,
    color="n_genes_detected",
    vars_to_plot=("n_genes_detected",),
)
bk.pp.filter_samples(adata)
bk.pp.filter_genes(adata)

## PCA and bidimensional representation
bk.pp.highly_variable_genes(adata, layer="log1p_cpm", n_top_genes=2000)
bk.tl.pca(adata, layer="log1p_cpm", n_comps=20, 
         use_highly_variable=True,)  

bk.pl.pca_scatter(adata, color="Project_ID", palette="tab20", figsize=(7,7), point_size=8)
bk.pl.pca_variance_ratio(adata)
bk.tl.pca_loadings(adata, pcs=[1,2], n_top=10)

### Neighbors and UMAP
bk.tl.neighbors(adata, n_neighbors=15, n_pcs=20, metric="euclidean")
bk.tl.umap(adata, n_neighbors=8, n_pcs=30, min_dist=0.3, random_state=0)
bk.pl.umap(adata, color="Project_ID", 
           figsize=(8,7),
           point_size=10, palette="tab20")

## Clustering and groups
bk.tl.leiden_resolution_scan(adata=
bk.tl.cluster(adata, method="leiden", key_added="leiden_2.2", resolution=2.2)

## Genes and signatures
bk.tl.score_genes(adata, ne_score_25, score_name="NE25_score", layer="log1p_cpm")

## Data exploration
bk.pl.violin(
    adata,
    keys=["MYC_amplified", "n_genes_detected"],
    groupby="Project_ID",
)

bk.pl.corr_heatmap(
    adata_lung,
    layer="log1p_cpm",
    method="pearson",
    groupby="Project_ID",
)

## Markers and Differential Expression
bk.tl.de(adata, groupby="condition", groupA="A", groupB="B")
bk.pl.volcano(adata)

##Pathway and Gene Set Enrichment Analysis
bk.tl.gsea_preranked(
    adata,
    res=res,
    comparison=comparison,
    score_col="t",                                       
    gene_sets=["hallmark"], 
)

⸻

🔗 Links#

BULLKpy is available on GitHub (malumbres/BULLKpy).

Issue tracker: (malumbres/BULLKpy#issues)

BULLKpy 🧬#

🚀 Installation#

🚀 Getting started#

📘 Table of contents#

📘 Quickstart#

🔗 Links#