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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,79 @@ | ||
| """ | ||
| step44-6.py: Volcano plot with differetially abundant taxa with blue/red | ||
| """ | ||
| import argparse | ||
| import warnings | ||
| import adjustText | ||
| import matplotlib | ||
| import matplotlib.pyplot | ||
| import numpy | ||
| import pandas | ||
| import step00 | ||
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| ratio_threshold = 2 | ||
| p_threshold = 0.05 | ||
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| if __name__ == "__main__": | ||
| parser = argparse.ArgumentParser() | ||
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| parser.add_argument("input", help="Input TSV file", type=str) | ||
| parser.add_argument("output", help="Output PDF file", type=str) | ||
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| args = parser.parse_args() | ||
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| if not args.input.endswith(".tsv"): | ||
| raise ValueError("INPUT must end with .TSV!!") | ||
| elif not args.output.endswith(".pdf"): | ||
| raise ValueError("Output file must end with .PDF!!") | ||
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| input_data = pandas.read_csv(args.input, sep="\t", index_col=0) | ||
| input_data["-log10(p)"] = -1 * numpy.log10(input_data["padj"]) | ||
| input_data["simple_name"] = list(map(step00.consistency_taxonomy, list(input_data.index))) | ||
| print(input_data) | ||
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| ceil = numpy.ceil(max(numpy.absolute(input_data["log2FoldChange"]))) | ||
| input_data.to_csv(args.output.replace(".pdf", ".list.tsv"), sep="\t") | ||
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| matplotlib.use("Agg") | ||
| matplotlib.rcParams.update(step00.matplotlib_parameters) | ||
| warnings.filterwarnings("error") | ||
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| fig, ax = matplotlib.pyplot.subplots(figsize=(24, 24)) | ||
| texts = list() | ||
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| down_results = input_data.loc[((input_data["log2FoldChange"] < numpy.log2(1 / ratio_threshold)) & (input_data["-log10(p)"] > (-1 * numpy.log10(p_threshold)))), :].sort_values("-log10(p)", ascending=False) | ||
| up_results = input_data.loc[((input_data["log2FoldChange"] > numpy.log2(ratio_threshold)) & (input_data["-log10(p)"] > (-1 * numpy.log10(p_threshold)))), :].sort_values("-log10(p)", ascending=False) | ||
| ns_results = input_data.loc[(((input_data["log2FoldChange"] < numpy.log2(ratio_threshold)) & (input_data["log2FoldChange"] > numpy.log2(1 / ratio_threshold))) | (input_data["-log10(p)"] < (-1 * numpy.log10(p_threshold)))), :] | ||
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| print(up_results.sort_values("simple_name")) | ||
| print(down_results.sort_values("simple_name")) | ||
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| matplotlib.pyplot.scatter(ns_results["log2FoldChange"], ns_results["-log10(p)"], s=400, c="gray", marker="o", edgecolors=None) | ||
| matplotlib.pyplot.scatter(up_results["log2FoldChange"], up_results["-log10(p)"], s=400, c="tab:red", marker="o", edgecolors=None) | ||
| matplotlib.pyplot.scatter(down_results["log2FoldChange"], down_results["-log10(p)"], s=400, c="tab:blue", marker="o", edgecolors=None) | ||
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| for index, row in down_results.iterrows(): | ||
| texts.append(matplotlib.pyplot.text(row["log2FoldChange"], row["-log10(p)"], step00.simplified_taxonomy(index), color="tab:blue", fontsize="xx-small")) | ||
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| for index, row in up_results.iterrows(): | ||
| texts.append(matplotlib.pyplot.text(row["log2FoldChange"], row["-log10(p)"], step00.simplified_taxonomy(index), color="tab:red", fontsize="xx-small")) | ||
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| matplotlib.pyplot.xlabel("log2(PTB/Normal)") | ||
| matplotlib.pyplot.ylabel("-log10(adj. p)") | ||
| matplotlib.pyplot.axvline(numpy.log2(1 / ratio_threshold), color="k", linestyle="--", linewidth=5) | ||
| matplotlib.pyplot.axvline(numpy.log2(ratio_threshold), color="k", linestyle="--", linewidth=5) | ||
| matplotlib.pyplot.axhline(-1 * numpy.log10(p_threshold), color="k", linestyle="--", linewidth=5) | ||
| matplotlib.pyplot.xlim((-ceil, ceil)) | ||
| matplotlib.pyplot.grid(True) | ||
| matplotlib.pyplot.title(f"Up: {up_results.shape[0]}; Down: {down_results.shape[0]}") | ||
| matplotlib.pyplot.tight_layout() | ||
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| adjustText.adjust_text(texts, arrowprops=dict(arrowstyle="-", color="black", alpha=0.3), lim=step00.big, ax=ax) | ||
| matplotlib.pyplot.tight_layout() | ||
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| fig.savefig(args.output) | ||
| matplotlib.pyplot.close(fig) | ||
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| output_results = input_data.loc[((input_data["log2FoldChange"] < numpy.log2(1 / ratio_threshold)) | (input_data["log2FoldChange"] > numpy.log2(ratio_threshold))) & (input_data["-log10(p)"] > (-1 * numpy.log10(p_threshold)))].sort_values("log2FoldChange") | ||
| output_results.to_csv(args.output.replace(".pdf", ".selected.tsv"), sep="\t") |
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