pycmplot 0.2.7

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 will result 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.

Key features

Column auto-detection

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']

NB: Upper and lower cases of the candidates are also considered, making each candidate expanded 3 times.

Density-aware sub-sampling

Another key feature is density-aware sub-sampling for Manhattan-style scatter plots. This was inspired by gwaslab's default behaviour (https://cloufield.github.io/gwaslab/).

Every variant whose "interestingness" signal is at or above keep_threshold is preserved (so peaks, suggestive hits, genome-wide-significant hits, and extreme selection-scan values are kept verbatim). It uniformly sub-samples the dense bulk below the threshold down to at most max_below rows in total. For a 10 M-variant scan with the defaults below, this typically cuts the plotted point count from 10 M to ~200 K + a few hundred peaks — visually indistinguishable above the suggestive band, but two orders of magnitude faster to render.

Trim insignificant variants for faster plotting

An optional parameter -tp, --trim_pval is provided to increase speed even further. Set with a value to exclude variants with p-value above a certain threshold, e.g. 0.01 (1e-2) or 0.001 (1e-3). Performed on top of the default auto-thin feature above, it siginificant increases speed and reduces peak memory usage. See benchmark figure (manuscript in preparation).

Genome build conversion (liftover)

Conversion of a both hg18 and hg19 positions to their hg38 equivalent is included through pyliftover.LiftOver.

This means you can concatenate multiple summary stats into one file and include a BUILD column to specify the genome build of each position ('hg18', 'hg19', or 'hg38') and all 'hg18' and 'hg19' positions will be converted to 'hg38' so that all positions are plotted using one coordinate system. If only 'hg18' or 'hg19' positions are present, no liftover be necessary. Hence, liftover is only performed in cases of mixed genome builds.

Nearest-gene annotation for GWAS lead SNPs

The package bundles GFF3 files in hg19 and hg38 coordinates processed to reduce size for gene annotation. Also included are UCSC chain files for coordinate conversion (liftover).

• chain_hg19_hg38 -- UCSC LiftOver chain file for hg19 to hg38 conversion. Resolved from PYCMPLOT_CHAIN_HG19_HG38 or the bundled hg19ToHg38.over.chain.gz.
• chain_hg18_hg38 -- UCSC LiftOver chain file for hg18 to hg38 conversion. Resolved from PYCMPLOT_CHAIN_HG18_HG38 or the bundled hg18ToHg38.over.chain.gz. Only required when any input summary statistics file carries a hg18 build label.
• geneinfo_hg38 -- Ensembl gene-info TSV for GRCh38, used for nearest-gene annotation. Resolved from PYCMPLOT_GENEINFO_HG38 or the bundled Homo_sapiens.GRCh38.geneinfo.tsv.gz.
• geneinfo_hg19 -- Ensembl gene-info TSV for GRCh37, used when input data carry a hg19 build label. Resolved from PYCMPLOT_GENEINFO_HG19 or the bundled Homo_sapiens.GRCh37.geneinfo.tsv.gz.

Application

A potential useful application is comparative visualization of results from multiple imputation panels, multiple populations, or multiple traits to observe shared genetic architecture.

Read more in the package documentation page: https://pycmplot.readthedocs.io/en/latest/

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Installation

From PyPI

pip install pycmplot

From GitHub

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

# or with most recent updates from development branch
# git clone -b dev 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

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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	Comma-separated genome builds of sumstats	off
-bc, --build_column	Genome build column name (containing hg18/hg19/hg38)	off
-m, --mode	lm linear or cm circular	lm
-qq, --qq_plot	Also generate a QQ-plot	off
-qq_thin, --qq_thin	Thin p-values for faster QQ-plotting	off
--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, gene, or any column in hits_table	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

A demonstration of how to use the python API is provided in this notebook: https://github.com/esohkevin/pycmplot/blob/main/pycmplot_python_api.ipynb

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Contributing

See how to contribute here https://github.com/esohkevin/pycmplot?tab=contributing-ov-file

Contributors

• Kevin Esoh