pycmplot 0.1.5

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]                   |
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#

This package will take any number of per SNP/variant summary statistics, be it GWAS, selection scans (e.g. iHS, EHH, FST), etc and generate Manhattan plots. If given a single file, a single one-track Manhattan plot will be generated. Multiple files with results in the generation of a multi-track stacked Manhattan plot.

In the process, the package will generate a hits summary table for variants with p-value (or whatever statistic for significance is used) below the user-specified significance threshold. This hits summary table will contain annotated gene names, in addition to other annotations, that would then be used to annotate the plots.

Importantly, the package allows for conversion of hg19 genomic coordinates to hg38 coordinates. This ensures that summary stats obtained using different imputation panels, for instance, can be processed in the same run. That is, users can simply concatenate multiple summary stats files together, such as those for the same trait but analysed using different imputation panels. Users only need to add a new column specifying the genome build (hg19 or hg38) of the variants. Then the --build_column option of the package should be used to indicate the column and then the package will liftover all postions in hg19 to hg38 ensuring that hits table generation and plotting are done with one unified corrdinate system.

A key functionality of the package is its ability to auto-detect certain columns if ommited on the command-line or python API:

• Chromosome column: -chr, --chrom_column or ommited
• Basepair position column: -pos, --pos_column or ommited
• SNP or Marker ID column: -snp, --snp_column or ommited
• P-value (or whatever value) column: -p, --pval_column or ommited
• Build version column: -b, --build_column or ommited

Candidate names for each of the columns is shown below.

# Resolve column names
chr_candidates = [chrom, 'CHR', 'CHROM', 'Chromosome', '#CHROM', '#CHR', 'Chrom', 'chrom', 'chr', 'chromosome', '#chr', '#chrom']
pos_candidates = [pos, 'BP', 'POS', 'bp', 'pos', 'Basepair']
snp_candidates = [snp, 'SNP', 'RSID', 'rsID', 'MarkerName', 'MarkerID', 'Predictor', 'Marker', 'SNPID', 'ID']
pvl_candidates = [pcol, 'P', 'P-value', 'Wald_P', 'pvalue', 'p_val', 'pval']
bld_candidates = [build, 'BUILD', 'Genome', 'Genome_Build', 'Genome-build']

Since GWAS summary stats files can be very large, to improve speed and memory efficiency, it is highly recommended to use -tp, --trim_pval with a value to exclude variants with p-value above a certain threshold, e.g. 0.01 (1e-2) or 0.001 (1e-3).

---

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 \
  --trim_pval 0.01 \
  --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
-b, --build_column	Genome build column name (containing hg18/hg19/hg38)	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	5e-8
-sug, --suggest_threshold	Threshold for suggestive signals	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.

---

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),
)

---

Package structure

pycmplot/
├── pyproject.toml
├── setup.py
├── setup.cfg
├── README.md
└── pycmplot/
      ├── __init__.py          # public API exports
      ├── __main__.py          # python -m pycmplot
      ├── _core.py             # main() orchestration
      ├── cli.py               # argparse definitions
      ├── constants.py         # chromosome lengths, biotype weights
      ├── resources.py         # external resource path config
      ├── io.py                # sumstat loading, delimiter detection
      ├── stats.py             # get_lead_snps, get_highlight_snps
      ├── liftover.py          # lazy hg19→hg38 liftover
      ├── annotation.py        # nearest-gene annotation, hits table
      └── plotting/
          ├── __init__.py
          ├── linear.py        # plot_linear
          └── circular.py      # plot_circular, compute_track_radii_dict