92 lines
2.6 KiB
Python
Executable File
92 lines
2.6 KiB
Python
Executable File
#!/usr/bin/env python
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"""Create workflow report."""
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import argparse
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from aplanat import annot, hist, report
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from bokeh.layouts import gridplot
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import numpy as np
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import pandas as pd
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def read_files(summaries):
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"""Combine a list of files into a single dataframe."""
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dfs = list()
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for fname in sorted(summaries):
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dfs.append(pd.read_csv(fname, sep="\t"))
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return pd.concat(dfs)
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def main():
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"""Run the entry point."""
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parser = argparse.ArgumentParser()
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parser.add_argument("report", help="Report output file")
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parser.add_argument("summaries", nargs='+', help="Read summary file.")
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args = parser.parse_args()
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report_doc = report.HTMLReport(
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"Workflow Template Sequencing report",
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("Results generated through the wf-template nextflow "
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"workflow by Oxford Nanopore Technologies"))
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report_doc.markdown('''
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### Read Quality control
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This section displays basic QC metrics indicating read data quality.
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''')
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np_blue = '#0084A9'
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# read length summary
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seq_summary = read_files(args.summaries)
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total_bases = seq_summary['sequence_length_template'].sum()
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mean_length = total_bases / len(seq_summary)
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median_length = np.median(seq_summary['sequence_length_template'])
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datas = [seq_summary['sequence_length_template']]
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length_hist = hist.histogram(
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datas, colors=[np_blue], bins=100,
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title="Read length distribution.",
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x_axis_label='Read Length / bases',
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y_axis_label='Number of reads',
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xlim=(0, 2000))
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length_hist = annot.subtitle(
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length_hist,
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"Mean: {:.0f}. Median: {:.0f}".format(
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mean_length, median_length))
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datas = [seq_summary['mean_qscore_template']]
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mean_q, median_q = np.mean(datas[0]), np.median(datas[0])
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q_hist = hist.histogram(
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datas, colors=[np_blue], bins=100,
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title="Read quality score",
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x_axis_label="Quality score",
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y_axis_label="Number of reads",
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xlim=(4, 25))
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q_hist = annot.subtitle(
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q_hist,
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"Mean: {:.0f}. Median: {:.0f}".format(
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mean_q, median_q))
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report_doc.plot(gridplot([[length_hist, q_hist]]))
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# Footer section
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report_doc.markdown('''
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### About
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**Oxford Nanopore Technologies products are not intended for use for health
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assessment or to diagnose, treat, mitigate, cure or prevent any disease or
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condition.**
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This report was produced using the
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[epi2me-labs/wf-template](https://github.com/epi2me-labs/wf-template). The
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workflow can be run using `nextflow epi2me-labs/wf-template --help`
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---
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''')
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# write report
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report_doc.write(args.report)
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if __name__ == "__main__":
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main()
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