382 lines
15 KiB
Python
Executable File
382 lines
15 KiB
Python
Executable File
#!/usr/bin/env python
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"""Create de report section."""
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from glob import glob
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import os
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from aplanat import hist, points
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from aplanat.bars import boxplot_series
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from aplanat.util import Colors
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import numpy as np
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import pandas as pd
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def parse_seqkit(fname):
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"""Get seqkit columns."""
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cols = {
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'Read': str, 'Ref': str, 'MapQual': int, 'Acc': float, 'ReadLen': int,
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'ReadAln': int, 'ReadCov': float, 'MeanQual': float,
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'IsSec': bool, 'IsSup': bool}
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df = pd.read_csv(fname, sep="\t", dtype=cols, usecols=cols.keys())
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df['Clipped'] = df['ReadLen'] - df['ReadAln']
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df['Type'] = 'Primary'
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df.loc[df['IsSec'], 'Type'] = 'Secondary'
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df.loc[df['IsSup'], 'Type'] = 'Supplementary'
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df["fname"] = os.path.basename(fname).rstrip(".seqkit.stats")
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return df
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def number_of_alignments(df, field_name):
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"""Group alignments for summary table."""
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grouped = df.groupby('fname').agg(**{
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field_name: ('Read', 'size'),
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})
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return grouped.transpose()
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def create_summary_table(df):
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"""Create summary table."""
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all_aln = number_of_alignments(df, "Read mappings")
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primary = number_of_alignments(df.loc[df['Type'] == 'Primary'], "Primary")
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secondary = number_of_alignments(
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df.loc[df['Type'] == 'Secondary'], "Secondary")
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supplementary = number_of_alignments(
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df.loc[df['Type'] == 'Supplementary'], "Supplementary")
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avg_acc = df.loc[df['Type'] == 'Primary'].groupby(
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'fname').agg(**{"Median Qscore": ('MeanQual', 'median'), }).transpose()
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avg_mapq = df.loc[df['Type'] == 'Primary'].groupby(
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'fname').agg(**{"Median MAPQ": ('MapQual', 'median'), }).transpose()
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return pd.concat([
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all_aln, primary, secondary, supplementary,
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avg_acc, avg_mapq])
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def dtu_table(gene_id, dtu_file, alignment_stats, condition_sheet):
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"""Create DTU table and plot."""
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dtu_results = pd.read_csv(dtu_file, sep='\t')
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f = open(condition_sheet)
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df = pd.read_csv(f, sep='\t')
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treated_df = df.loc[df['condition'] == "treated"]
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untreated_df = df.loc[df['condition'] == "untreated"]
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treated_samples = treated_df['sample'].tolist()
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control_samples = untreated_df['sample'].tolist()
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# parmaterise these
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control_name = "condition1"
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treated_name = "condition2"
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table = dtu_results.loc[(dtu_results["geneID"] == gene_id)]
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msg = "Gene ID \"{}\" does not exist in the dataset, please select another"
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assert not table.empty, msg.format(gene_id)
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alignment_stats[alignment_stats["Ref"].isin([gene_id])]
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alignment_stats["Transcript"] = alignment_stats["Ref"].apply(
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lambda x: x.split(".")[0])
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gene_alignments = alignment_stats[alignment_stats["Transcript"].isin(
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table["txID"])]
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gene_alignments = gene_alignments.loc[(
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gene_alignments["Type"] == "Primary")]
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gene_alignments["condition"] = gene_alignments.apply(
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lambda x: control_name if x["fname"] in
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control_samples else treated_name, axis=1)
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groups = gene_alignments.groupby(
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["Transcript", "fname", "condition"]).agg(
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**{"Transcript_count": ("Read", "size")})
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df = None
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for gene_id, group in gene_alignments.groupby("Transcript"):
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temp = group.groupby("fname").agg(**{
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gene_id: ("Read", "size")
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}).transpose()
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if df is None:
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df = temp
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else:
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df = pd.concat([df, temp])
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df = df.reset_index().rename(columns={"index": "Transcript_ID"})
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table = table.rename(columns={
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"txID": "Transcript_ID",
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"gene": "p_gene",
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"transcript": "p_transcript"
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})
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gene_table = pd.merge(df, table)
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gene_table = gene_table.set_index(["geneID", "Transcript_ID"])
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file_names = set(control_samples.keys())
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file_names.update(treated_samples.keys())
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gene_table = gene_table.fillna(0)
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table = groups.reset_index()[
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["condition", "Transcript", "Transcript_count"]]
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table['transcript, condition'] = \
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table['Transcript'].astype(str) + ', ' + table['condition'].astype(str)
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repeats = groups.groupby(level=['Transcript', 'condition']).size()
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min_rep, max_rep = min(repeats), max(repeats)
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plot = boxplot_series(
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table['transcript, condition'], table['Transcript_count'],
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x_axis_label='Transcript, condition',
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y_axis_label='Transcript count',
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height=200, width=200,
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title="Transcript counts (from {}-{} replicates)".format(
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min_rep, max_rep))
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plot.xaxis.major_label_orientation = 3.1452/2
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if min_rep < 7:
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for renderer in plot.renderers:
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renderer.glyph.line_alpha = 0.2
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try:
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renderer.glyph.fill_alpha = 0.2
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except Exception:
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pass
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plot.circle(
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table['transcript, condition'], table['Transcript_count'],
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fill_color='black', line_color='black')
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return (table, plot)
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def pool_csvs(folder):
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"""Concat seqkit stats."""
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files = glob(folder + "/*.seqkit.stats")
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dfs = [parse_seqkit(f) for f in files]
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return pd.concat(dfs)
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def abundance_histogram(filtered_counts, gene_counts, section):
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"""Create plot for abundance of transcripts across all samples."""
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section.markdown("""
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Histogram showing the abundance of transcript
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counts for genes identified in the analysis.
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""")
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filtered_count_file = filtered_counts
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gene_count_file = gene_counts
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transcripts_per_gene = pd.read_csv(
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filtered_count_file, sep='\t',
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usecols=['gene_id', 'feature_id']).groupby(['gene_id']).agg(['count'])
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transcripts_per_gene.columns = transcripts_per_gene.columns.droplevel()
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gene_ids = pd.read_csv(
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gene_count_file, sep='\t',
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usecols=[0]).index.values.tolist()
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singletons = [
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gene_id for gene_id in gene_ids if gene_id not in transcripts_per_gene.index.values.tolist()] # noqa
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singletons = pd.DataFrame(index=singletons, columns=['count']).fillna(1)
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transcripts_per_gene = pd.concat([singletons, transcripts_per_gene])
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transcript_plot = hist.histogram(
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[transcripts_per_gene['count'].tolist()],
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binwidth=1, colors=[Colors.cerulean])
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transcript_plot.xaxis.axis_label = "Number of isoforms per gene (n)"
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transcript_plot.yaxis.axis_label = "Number of occurences"
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section.markdown("### Transcripts per gene")
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section.plot(transcript_plot)
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def dexseq_section(dexseq_file, section, id_dic):
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"""Add gene isoforms table and plot."""
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section.markdown("### Differential Isoform usage")
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dexseq_caption = '''Table showing gene isoforms, ranked by adjusted
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p-value, from the DEXSeq analysis. Information shown includes the log2 fold
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change between experimental conditions, the log-scaled transcript
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abundance and the false discovery corrected p-value (FDR).
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This table has not been filtered
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for genes that satisfy statistical or magnitudinal thresholds'''
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section.markdown(dexseq_caption)
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dexseq_results = pd.read_csv(dexseq_file, sep='\t')
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dexseq_results.index.name = "gene_id:trancript_id"
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# Replace gene id with more useful gene name where possible
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dexseq_results.index = dexseq_results.index.map(
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lambda x: str(id_dic.get(x.split(':')[0])) + ':' + str(x.split(':')[1]))
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dexseq_pvals = dexseq_results.sort_values(by='pvalue', ascending=True)
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section.table(dexseq_results.loc[dexseq_pvals.index], index=True)
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section.markdown("""
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The figure below presents the MA plot from the DEXSeq analysis.
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M is the log2 ratio of isoform transcript abundance between conditions.
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A is the log2 transformed mean abundance value.
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Transcripts that satisfy the logFC and FDR corrected p-value
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thresholds defined are shaded as 'Up-' or 'Down-' regulated.""")
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pval_limit = 0.01
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up = dexseq_results.loc[
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(dexseq_results["Log2FC"] > 0) & (
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dexseq_results['pvalue'] < pval_limit)]
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down = dexseq_results.loc[
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(dexseq_results["Log2FC"] <= 0) & (
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dexseq_results['pvalue'] < pval_limit)]
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not_sig = dexseq_results.loc[(dexseq_results["pvalue"] >= pval_limit)]
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dexseq_plot = points.points(
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x_datas=[
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up["Log2MeanExon"],
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down["Log2MeanExon"],
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not_sig["Log2MeanExon"],
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],
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y_datas=[
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up["Log2FC"],
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down["Log2FC"],
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not_sig["Log2FC"],
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],
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title="Average copy per million (CPM) vs Log-fold change (LFC)",
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colors=["red", "blue", "black"],
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names=["Up", "Down", "NotSig"]
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)
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dexseq_plot.xaxis.axis_label = "A (log2 transformed mean exon read counts)"
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dexseq_plot.yaxis.axis_label = """
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M (log2 transformed differential abundance)
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"""
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dexseq_results_caption = "### Dexseq results"
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section.markdown(dexseq_results_caption)
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section.plot(dexseq_plot)
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def dtu_section(dtu_file, section, gt_dic, ge_dic):
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"""Plot dtu section."""
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dtu_results = pd.read_csv(dtu_file, sep='\t')
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dtu_results["gene_name"] = dtu_results["txID"].apply(
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lambda x: gt_dic.get(x))
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dtu_results["geneID"] = dtu_results["geneID"].apply(
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lambda x: ge_dic.get(x))
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dtu_pvals = dtu_results.sort_values(by='gene', ascending=True)
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dtu_caption = '''Table showing gene and transcript identifiers
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and their FDR corrected probabilities
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for the genes and their isoforms that have been
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identified as showing DTU using the R packages DEXSeq and StageR.
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This list has been shortened requiring that both gene and transcript
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must satisfy the p-value
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threshold'''
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section.markdown(dtu_caption)
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section.table(dtu_results.loc[dtu_pvals.index])
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def dge_section(dge_file, section, ids_dic):
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"""Create DGE table and plot."""
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section.markdown('### Differential gene expression')
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dge_results = pd.read_csv(dge_file, sep='\t')
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dge_pvals = dge_results.sort_values(by='FDR', ascending=True)
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dge_results[['logFC', 'logCPM', 'F']] = dge_results[
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['logFC', 'logCPM', 'F']].round(2)
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dge_caption = """
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Table showing the genes from the edgeR analysis.
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Information shown includes the log2 fold change between
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experimental conditions, the log-scaled counts per million measure of abundance
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and the false discovery corrected p-value (FDR). This table has not been
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filtered for genes that satisfy statistical or magnitudinal thresholds"""
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section.markdown(dge_caption)
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dge_results.index = dge_results.index.map(lambda x: ids_dic.get(x))
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dge_pvals.index = dge_pvals.index.map(lambda x: ids_dic.get(x))
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section.table(dge_results.loc[dge_pvals.index], index=True)
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dge = pd.read_csv(dge_file, sep="\t")
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section.markdown("""
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This plot visualises differences in measurements between the
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two experimental conditions. M is the log2 ratio of gene expression
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calculated between the conditions.
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A is a log2 transformed mean expression value.
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The figure below presents the MA figure from this edgeR analysis.
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Genes that satisfy the logFC and FDR corrected p-value thresholds
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defined are shaded as 'Up-' or 'Down-' regulated.
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""")
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pval_limit = 0.01
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up = dge.loc[(dge["logFC"] > 0) & (dge['PValue'] < pval_limit)]
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down = dge.loc[(dge["logFC"] <= 0) & (dge['PValue'] < pval_limit)]
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not_sig = dge.loc[(dge["PValue"] >= pval_limit)]
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logcpm_vs_logfc = points.points(
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x_datas=[
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up["logCPM"],
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down["logCPM"],
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not_sig["logCPM"],
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],
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y_datas=[
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up["logFC"],
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down["logFC"],
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not_sig["logFC"],
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],
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title="Average copy per million (CPM) vs Log-fold change (LFC)",
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colors=["red", "blue", "black"],
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names=["Up", "Down", "NotSig"]
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)
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logcpm_vs_logfc.xaxis.axis_label = "Average log CPM"
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logcpm_vs_logfc.yaxis.axis_label = "Log-fold change"
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logcpm_caption = """### Results of the edgeR Analysis."""
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section.markdown(logcpm_caption)
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section.plot(logcpm_vs_logfc)
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def salmon_table(salmon_counts, section):
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"""Create salmon counts summary table."""
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salmon_counts = pd.read_csv(salmon_counts, sep='\t')
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salmon_counts.set_index("Reference", drop=True, append=False, inplace=True)
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salmon_size_top = salmon_counts.sum(axis=1).sort_values(ascending=False)
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salmon_counts = salmon_counts.applymap(np.int64)
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salmon_count_caption = """
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Table showing the annotated Transcripts Per Million
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identified by Minimap2 mapping and Salmon transcript
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detection with the highest
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number of mapped reads"""
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section.markdown("### Transcripts Per Million ")
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section.markdown(salmon_count_caption, "salmon-head-caption")
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section.table(
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salmon_counts.loc[salmon_size_top.index].head(n=100), index=True)
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def get_translations(gtf):
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"""Create dict with gene_name and gene_references."""
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fn = open(gtf).readlines()
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gene_txid = {}
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gene_geid = {}
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def get_feature(row, feature):
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return row.split(feature)[1].split(
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";")[0].replace('=', '').replace("\"", "").strip()
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for i in fn:
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if i.startswith("#"):
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continue
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# Different gtf/gff formats contain different attributes
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# and different formating (eg. gene_name="xyz" or gene_name "xyz")
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if 'gene_name' in i:
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gene_name = get_feature(i, "gene_name")
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elif 'gene_id' in i:
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gene_name = get_feature(i, 'gene_id')
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elif 'gene' in i:
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gene_name = get_feature(i, "gene")
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else:
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continue
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if 'ref_gene_id' in i:
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gene_reference = get_feature(i, 'ref_gene_id')
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elif 'gene_id' in i:
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gene_reference = get_feature(i, 'gene_id')
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else:
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gene_reference = gene_name
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if 'transcript_id' in i:
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transcript_id = get_feature(i, 'transcript_id')
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else:
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transcript_id = "unknown"
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if 'gene_id' in i:
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gene_id = get_feature(i, 'gene_id')
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else:
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gene_id = gene_name
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gene_txid[transcript_id] = gene_name
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gene_geid[gene_id] = gene_reference
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return gene_txid, gene_geid
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def de_section(
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stringtie, dge, dexseq, dtu,
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tpm, report):
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"""Differential expression sections."""
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section = report.add_section()
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section.markdown("# Differential expression.")
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section.markdown("""
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This section shows differential gene expression
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and differential isoform usage. Salmon was used to
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assign reads to individual annotated isoforms defined by
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the GTF-format annotation.
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These counts were used to perform a statistical analysis to identify
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the genes and isoforms that show differences in abundance between
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the experimental conditions.
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""")
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section.markdown("### Alignment summary stats")
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alignment_stats = pool_csvs("seqkit")
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alignment_summary_df = create_summary_table(alignment_stats)
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alignment_summary_df = alignment_summary_df.fillna(0).applymap(np.int64)
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section.table(alignment_summary_df, key='alignment-stats', index=True)
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salmon_table(tpm, section)
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gene_txid, gene_name = get_translations(stringtie)
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dge_section(dge, section, gene_name)
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dexseq_section(dexseq, section, gene_name)
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dtu_section(dtu, section, gene_txid, gene_name)
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# missing dtu plots at the moment as too many
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section.markdown("""
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### View dtu_plots.pdf file to see plots of differential isoform usage
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""")
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