pycmplot 0.1.2

Multi-track circular and linear Manhattan plot generation for GWAS summary statistics

pycmplot

Multi-track circular and linear Manhattan plot generation for GWAS summary statistics.

#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
|  PACKAGE FOR CIRCULAR AND LINEAR MANHATTAN PLOTTING  |
|                    Kevin Esoh, 2026                  |
|                    [e-mail]                   |
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#

---

Installation

From PyPI

pip install pycmplot

From GitHub

git clone https://github.com/esohkevin/pycmplot.git

cd pycmplot

pip install -e .

# or

pip install -e . --break-system-packages

Use python virtual environment if local installation is not possible

python -m venv ~/bin/pycmplot

source ~/bin/pycmplot/bin/activate

pip install --upgrade pip setuptools wheel

# then follow any of the installation steps above

Test the installation

pycmplot -h

Dependencies

Package	Purpose
pandas, numpy	Data loading & statistics
matplotlib	Plotting backend
pycirclize	Circular (Circos-style) tracks
natsort	Natural chromosome sorting
adjustText	Label collision avoidance
pyliftover	hg19 to hg38 coordinate conversion
Pillow	Image utilities

---

Command-line usage

Linear Manhattan (default)

pycmplot \
  --sum_stats HbF.tsv.gz,MCV.txt.gz,MCH.tsv.gz \
  --labels HbF,MCV,MCH \
  --logp \
  --signif_line \
  --highlight \
  --annotate GENE \
  --output_dir ./results \
  --output_format png \
  --dpi 300

Circular Manhattan

pycmplot \
  --sum_stats HbF.tsv.gz,MCV.tsv.gz \
  --labels HbF,MCV \
  --mode cm \
  --logp \
  --signif_threshold \
  --plot_title "RBC Traits" \
  --output_dir ./results

Key options

Flag	Description	Default
-s, --sum_stats	Comma-separated sumstats files	required
-l, --labels	Comma-separated track labels	required
-m, --mode	lm linear or cm circular	lm
-qq, --qq_plot	Also generate a QQ-plot	off (coming soon...)
--logp	Plot -log10(p)	off
-sig, --signif_threshold	Genome-wide significance threshold	off (auto 0.05/N)
-sigl, --signif_line	Value for genome-wide significance line if different from -sig	-sig
-sug, --suggest_threshold	Suggestive significance line	off
-hl, --highlight	Highlight significant loci	off
-a, --annotate	Annotate with SNP or GENE	SNP
-tp, --trim_pval	Trim variants above this p-value for speed	off
-st, --sort_track	Sort tracks by label or chrom_len	input order
-od, --output_dir	Output directory	.
-of, --output_format	Output format (png, pdf, svg, jpg)	png

Run pycmplot -h for the full option list.

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Python API

from pycmplot import plot_linear
import pandas as pd

df1 = pd.read_csv("HbF.tsv.gz", sep="\t")
df2 = pd.read_csv("MCV.tsv.gz", sep="\t")

plot_linear(
    tracks=[df1, df2],
    track_labels=["HbF", "MCV"],
    chr_col="CHR",
    pos_col="POS",
    p_col="P",
    logp=True,
    highlight=True,
    plot_title="results/HbF_MCV.png",
    figsize=(15, 8),
)

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Under development