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MakeSamplePlots.py
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734 lines (525 loc) · 26.6 KB
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import argparse
import logging
import os
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns;sns.set()
from scipy.stats import entropy
from scipy.stats import mannwhitneyu
from scipy.stats import linregress
import sys
# MakeSamplePlots
# goal: show sample statistics. How many reads are mapped to what reference genomes?
# how does it relate to some metadata?
# this should be generic for the metadata file format
# however, we might want to plug in some specific stuff for certain projects
# input:
# 1. sample_stats.txt (statistics of samples vs ref genomes)
# 2. metadata_ERP005989.txt (metadata project)
# 3. ref_genome_ids.txt (metadata ref genomes)
# 4. sample_measures.txt (extra generated measures aggregated from results by CalcDiversiMeasures)
class MakeSamplePlots:
project_dir = ""
dir_sep = ""
plot_dir = ""
threshold_breadth = 0
sample_meta_df = None
sample_stats_df = None
ref_meta_df = None
sample_measures_df = None
merge_df = None
genus_df = None
entropies_df = None
genus_palette = {}
age_cat_palette = {}
genus_order = ["1", "2", "3", "4", "5", "6", "7", "8", "9", "10"]
filter = ""
def __init__(self, sample_dir, tb):
self.sample_dir = sample_dir
self.threshold_breadth = tb
if os.name == 'nt':
self.dir_sep = "\\"
else:
self.dir_sep = "/"
logging.basicConfig(filename=self.sample_dir + self.dir_sep + 'MakeSamplePlots.log', filemode='w', format='%(asctime)s - %(message)s',
level=logging.INFO)
def read_files(self):
logging.info("start reading tables")
self.read_sample_metadata()
self.read_sample_stats()
self.read_ref_metadata()
self.read_sample_measures()
logging.info("finished reading tables")
def read_sample_metadata(self):
metadata_filename = self.sample_dir+ self.dir_sep + "metadata_ERP005989.txt"
self.sample_meta_df = pd.read_csv(metadata_filename
, sep='\t'
)
self.sample_meta_df = self.sample_meta_df[['analysis.run.accession', 'sample.sample_name', 'sample.sample_desc']]
self.sample_meta_df.rename(columns={'analysis.run.accession': 'run',
'sample.sample_name': 'sample_name',
'sample.sample_desc': 'sample_desc'}, inplace=True)
def read_sample_stats(self):
stats_file_name = self.sample_dir + self.dir_sep + "sample_stats.txt"
self.sample_stats_df = pd.read_csv(stats_file_name
, sep='\t'
, header=None
, names=["run", "ref", "mapped", "nonmapped"])
# rename NC_024711.1 to crassphage_refseq, for being able to join with sample_measures
self.sample_stats_df.loc[self.sample_stats_df.ref == "NC_024711.1", "ref"] = "crassphage_refseq"
def read_ref_metadata(self):
# to do: also read from ref_dir
# NB: for now ref_genome_ids.txt should be copied from scripts/mgx/ref_seqs
ref_file_name = self.sample_dir + self.dir_sep + "ref_genome_ids.txt"
self.ref_meta_df = pd.read_csv(ref_file_name
, sep="\t"
, header=None
, comment="#"
, names=["ref", "genus"]
)
self.ref_meta_df.genus = self.ref_meta_df.apply(self.shorten_genus, axis=1)
# to do: cleaner way to exclude this row
self.ref_meta_df = self.ref_meta_df[self.ref_meta_df.ref != "NC_024711.1"]
# add color palettes the same way as in MakeGenePlots
self.build_color_palette_from_ref()
# use same color coding in MakeGenePlots for consistent color coding of genera and accompanying refs
def build_color_palette_from_ref(self):
# If you would like to extend manually on the palette colors and copy color codes see:
# https://github.com/mwaskom/seaborn/blob/master/seaborn/palettes.py
colors = sns.color_palette("bright", n_colors=10)
i = 0
for index, row in self.ref_meta_df.iterrows():
self.genus_palette[row.genus] = colors[i]
i = i + 1
def read_sample_measures(self):
# sample measures contains the breadth_1x, ..10x, ..50x and ..100x fractions
measure_file_name = self.sample_dir + self.dir_sep + "sample_measures.txt"
self.sample_measures_df = pd.read_csv(measure_file_name,
sep="\t",
)
nr_1x_05 = len(self.sample_measures_df[self.sample_measures_df.breadth_1x > 0.05])
nr_1x_20 = len(self.sample_measures_df[self.sample_measures_df.breadth_1x > 0.20])
nr_1x_80 = len(self.sample_measures_df[self.sample_measures_df.breadth_1x > 0.80])
nr_1x_95 = len(self.sample_measures_df[self.sample_measures_df.breadth_1x > 0.95])
logging.info("nr of 1x > 5%: {nr}".format(nr=nr_1x_05))
logging.info("nr of 1x > 20%: {nr}".format(nr=nr_1x_20))
logging.info("nr of 1x > 80%: {nr}".format(nr=nr_1x_80))
logging.info("nr of 1x > 95%: {nr}".format(nr=nr_1x_95))
def merge_files(self):
# now we join all the files together, and this is still generic i.e. not specific for a certain project
# and after doing that we do the specific ERP005989 enrichment
merge_df = self.sample_stats_df.merge(self.sample_meta_df
, left_on=self.sample_stats_df.run
, right_on=self.sample_meta_df.run
, how="right").drop(["key_0", "run_y"], axis=1)
merge_df.rename(columns={'run_x': 'run'}, inplace=True)
assert(len(self.sample_stats_df) == len(merge_df))
merge_df = merge_df.merge(self.ref_meta_df
, left_on=merge_df.ref
, right_on=self.ref_meta_df.ref
, how="inner").drop(["key_0", "ref_y"], axis=1)
merge_df.rename(columns={'ref_x': 'ref'}, inplace=True)
assert(len(self.sample_stats_df) == len(merge_df))
nr_of_mappings = len(merge_df)
logging.info("Number of mappings analyzed: {nr_of_mappings}".format(nr_of_mappings=nr_of_mappings))
logging.info("total number of mapped reads: {mapped}".format(mapped=merge_df.mapped.sum()))
merge_df = merge_df.merge(self.sample_measures_df
, left_on=[merge_df.run, merge_df.ref]
# nb: sample is reserved word of pandas DataFrame
, right_on=[self.sample_measures_df["sample"], self.sample_measures_df.ref]
, how="outer").drop(["key_0", "key_1", "ref_y", "sample"], axis=1)
merge_df.rename(columns={'ref_x': 'ref'}, inplace=True)
return merge_df
def filter_on_breadth_threshold(self):
merge_df = self.merge_df
self.filter = "{perc}%/1x".format(perc=self.threshold_breadth*100)
return merge_df[merge_df.breadth_1x > self.threshold_breadth]
def make_bar_plot(self):
figure_name = "{}{}sample_plots.totals.mapped.genus.pdf".format(self.plot_dir, self.dir_sep)
ax = sns.barplot(self.genus_df.genus, self.genus_df.mapped_sum, log=True
, order=self.genus_order
, palette=self.genus_palette)
ax.set(xlabel='crAss-like genus', ylabel='total # of reads mapped')
plt.title("total reads mapped for genera")
plt.savefig(figure_name)
plt.clf()
def prepare_data(self):
self.merge_df["genus"] = self.merge_df.apply(self.shorten_genus, axis=1)
return self.merge_df
def drop_zero_coverage_and_normalize_data(self):
self.merge_df = self.merge_df[self.merge_df.mapped != 0]
# normalize to nr mapped reads per 50 million total reads
self.merge_df['mapped_norm'] = 50e6 * self.merge_df.mapped / \
(self.merge_df.nonmapped + self.merge_df.mapped)
nr_mappings = len(self.merge_df)
logging.info("nr of non-zero mappings among samples: {nr_mappings}".format(nr_mappings=nr_mappings))
self.merge_df["log10_mapped"] = np.log10(self.merge_df.mapped_norm)
return self.merge_df
def sort_genus_according_to_abundance(self):
df = self.merge_df.groupby(["ref", "genus"]).agg(
{
'mapped': ["sum", "std"]
}).reset_index()
df.columns = ["_".join(x) for x in df.columns.ravel()]
df.rename(columns={'genus_': 'genus', "ref_": "ref"}, inplace=True)
return df
@staticmethod
def ge_5_perc(breadth):
return breadth[breadth.ge(0.05)].count().astype(int)
@staticmethod
def ge_50_perc(breadth):
return breadth[breadth.ge(0.5)].count().astype(int)
@staticmethod
def ge_95_perc(breadth):
return breadth[breadth.ge(0.95)].count().astype(int)
def make_filter_sample_plots(self):
# now we want to show the number of valid samples after applying filter
# for each genus we want to show a barplot with nr of samples at different cut-offs
# so we want to show
counts_df = self.merge_df.groupby(["ref", "genus"]).agg(
{
"breadth_1x": [self.ge_5_perc, self.ge_50_perc, self.ge_95_perc],
"breadth_10x": [self.ge_5_perc, self.ge_50_perc, self.ge_95_perc],
"breadth_50x": [self.ge_5_perc, self.ge_50_perc, self.ge_95_perc]
}
).reset_index()
counts_df.columns = ["_".join(x) for x in counts_df.columns.ravel()]
counts_df.rename(columns={'genus_': 'genus', "ref_": "ref"}, inplace=True)
depths = ["1x", "10x", "50x"]
percentages = [5, 50, 95]
for depth in depths:
for perc in percentages:
ax = sns.barplot(x=counts_df.genus,
y=counts_df["breadth_{depth}_ge_{perc}_perc".format(depth=depth, perc=perc)],
palette=self.genus_palette,
order=self.genus_order
)
ax.set(xlabel='crAss-like genus', ylabel='#of samples with breadth > {perc}% for {depth}'.
format(depth=depth, perc=perc))
plt.title("number of samples in which genera are detected")
figure_name = "{dir}{sep}sample_plots.filter.{depth}.{perc}_perc.pdf".format(
dir=self.plot_dir, sep=self.dir_sep, depth=depth, perc=perc
)
plt.savefig(figure_name)
plt.clf()
def do_statistical_analysis_on_unfiltered_data(self):
df = self.merge_df
group = df.groupby("run")
data = group.apply(self.count_nr_genera_above_threshold_and_calc_entropy)
file_name = "{}{}sample_nr_genera_and_entropy.txt".format(
self.plot_dir, self.dir_sep
)
data.to_csv(path_or_buf=file_name, sep='\t', index=False)
self.calc_and_write_means(data)
self.calc_and_write_pvalues(data, "count")
self.calc_and_write_pvalues(data, "entropy")
def count_nr_genera_above_threshold_and_calc_entropy(self, group):
df = group[["run", "genus", "breadth_1x", "mapped"]]
df = df[df.breadth_1x > self.threshold_breadth]
count_genus = df.genus.count()
mapped_entropy = entropy(df.mapped, base=10)
df_return = pd.DataFrame({
"run": group["run"].head(1),
"age_cat": group["age_cat"].head(1),
"count": count_genus,
"entropy": mapped_entropy}
)
return df_return
def calc_and_write_means(self, data):
# calculate mean and standard deviation per age category
age_cat_means = data.groupby("age_cat").agg(
{'count': ['mean', 'std'],
'entropy': ['mean', 'std']}
).reset_index()
age_cat_means.columns = ["_".join(x) for x in age_cat_means.columns.ravel()]
age_cat_means.rename(columns={'age_cat_': 'age_cat'}, inplace=True)
age_cat_means = age_cat_means.sort_values(["entropy_mean"])
file_name = "{}{}age_cat_means.txt".format(
self.plot_dir, self.dir_sep
)
age_cat_means.to_csv(path_or_buf=file_name, sep='\t', index=False)
def calc_and_write_pvalues(self, data, measure):
label = "pval_{}".format(measure)
mw_data = pd.DataFrame(columns=['age_cat1', 'age_cat2', label])
mw_data.set_index(['age_cat1', 'age_cat2'])
age_cats1 = data.age_cat.unique()
age_cats2 = age_cats1.copy()
for age_cat1 in age_cats1:
for age_cat2 in age_cats2:
if age_cat1 == age_cat2:
mw_data.loc[age_cat1, age_cat2] = 1
else:
ds_1 = data[data.age_cat == age_cat1][measure]
ds_2 = data[data.age_cat == age_cat2][measure]
mw_result = mannwhitneyu(x=ds_1, y=ds_2)
mw_data.loc[age_cat1, age_cat2] = mw_result.pvalue
mw_data = mw_data.drop(['age_cat1', 'age_cat2', label], axis=1)
file_name = "{}{}age_cat_p_values_{measure}.txt".format(
self.plot_dir, self.dir_sep, measure=measure
)
mw_data.to_csv(path_or_buf=file_name, sep='\t', index=True)
# specific ERP005989 enrichment
# prepare data plot for the project that contains metadata about age in the sample name
def prepare_specifics_for_project(self):
merge_df = self.merge_df
merge_df["age_cat_short"] = merge_df.apply(self.age_category, axis=1)
merge_df["family"] = merge_df.apply(self.family, axis=1)
merge_df.loc[merge_df.age_cat_short == "B", "age_cat"] = "baby"
merge_df.loc[merge_df.age_cat_short == "4M", "age_cat"] = "4 months"
merge_df.loc[merge_df.age_cat_short == "12M", "age_cat"] = "12 months"
merge_df.loc[merge_df.age_cat_short == "M", "age_cat"] = "mother"
merge_df = merge_df.sort_values("age_cat")
return merge_df
def write_normalized_mapping_statistics(self):
df = self.merge_df
file_name = "{}{}sample_genera_normalized_mapping_statistics.txt".format(
self.plot_dir, self.dir_sep,
)
df.to_csv(path_or_buf=file_name, sep='\t', index=False)
def write_mapping_statistics(self):
df = self.merge_df
file_name = "{}{}sample_genera_mapping_statistics.txt".format(
self.plot_dir, self.dir_sep,
)
df.to_csv(path_or_buf=file_name, sep='\t', index=False)
def write_sample_genera_matrix(self):
# self.merge_df now contains among others:
# run (= sample); age_cat (dimension of run/sample)
# ref, genus
# value: mapped_norm (normalized)
df = self.merge_df
df["ref_genus"] = df["ref"] + " (" + df["genus"] + ")"
df = df[["run", "ref_genus", "mapped_norm"]]
df = df.sort_values(["run","ref_genus"])
df = df.set_index(["run", "ref_genus"])
# convert to matrix (it stays multiindex!)
df = df.unstack()
df.columns = ["_".join(x) for x in df.columns.ravel()]
df.columns = [x.replace("mapped_norm_", "") for x in df.columns]
df = df.reset_index()
df["total"] = df.drop('run', axis=1).sum(axis=1)
cols = list(df.columns.values)
new_cols = [cols[0], cols[-1]]
for col in cols[1:-1]:
new_cols.append(col)
df = df[new_cols]
df = df.round(decimals=0)
df_age = self.merge_df.groupby(["run", "age_cat"]).agg({'ref': 'count'})
df_age = df_age.reset_index().drop(['ref'], axis=1)
df = pd.merge(df_age, df, on="run", how="inner")
df.rename(columns={'run': 'sample'}, inplace=True)
df = df.sort_values(["sample"])
file_name = "{}{}sample_genera_matrix.txt".format(
self.plot_dir, self.dir_sep,
)
df.to_csv(path_or_buf=file_name, sep='\t', index=False)
def build_color_palette_for_ages(self):
# If you would like to extend manually on the palette colors and copy color codes see:
# https://github.com/mwaskom/seaborn/blob/master/seaborn/palettes.py
# check color codes: https://www.color-hex.com/color/00d7ff
green = "#138D75"
dark_blue = "#2E86C1"
purple = "#884EA0"
orange = "#F39C12"
bright_4 = [green, dark_blue, purple, orange]
self.age_cat_palette["baby"] = green
self.age_cat_palette["4 months"] = dark_blue
self.age_cat_palette["12 months"] = purple
self.age_cat_palette["mother"] = orange
@staticmethod
def family(row):
return row.sample_name.split("_")[0]
@staticmethod
def age_category(row):
return row.sample_name.split("_")[1]
@staticmethod
def shorten_genus(row):
return row.genus.replace("genus_", "")
# this is a specific plot for the project that contains metadata about age in the sample name
def make_abundance_plots(self):
self.make_category_plot("swarm")
self.make_category_plot("box")
def make_category_plot(self, kind):
# to do: we should filter out the reads that are not above the 5% 1x threshold
sns.catplot(x="genus",
y="log10_mapped",
kind=kind,
data=self.merge_df,
hue="age_cat",
hue_order=["baby", "4 months", "12 months", "mother"],
palette=self.age_cat_palette, order=self.genus_order
)
plt.title("log abundance of normalized mapped reads for one ref genome per genus")
plt.ylabel("log10 (norm. mapped reads)")
figure_name = "{}{}sample_plots.categories.mapped.genus.{kind}.pdf".format(self.plot_dir, self.dir_sep,
kind=kind)
plt.savefig(figure_name)
plt.clf()
def calculate_entropies_per_sample(self):
# use genus and mean_depth to calculate entropy for each sample and categorize for age_cat
df_entropies = self.merge_df.groupby(["run", "age_cat", "age_cat_short" ]).agg(
{
'mean_depth': ["mean", self.nr_genera, self.macro_entropy]
}
).reset_index()
df_entropies.columns = ["_".join(x) for x in df_entropies.columns.ravel()]
df_entropies.rename(columns={'run_': 'run',
'age_cat_': 'age_cat',
"age_cat_short_": "age_cat_short",
"mean_depth_nr_genera": "nr_genera"
}, inplace=True)
return df_entropies
@staticmethod
def macro_entropy(mean_depth):
macro_entropy = entropy(mean_depth, base=10)
return macro_entropy
@staticmethod
def nr_genera(mean_depth):
return len(mean_depth)
def make_diversity_plots(self):
df_entropies = self.entropies_df
self.make_cat_plot_for_age_categories(df_entropies, "mean_depth_macro_entropy",
"macro diversity (Shannon entropy)")
self.make_cat_plot_for_age_categories(df_entropies, "nr_genera",
"macro diversity (nr of genera)")
measure="nr_genera"
title="macro diversity: nr of genera"
self.regression_plot(df_entropies, measure, title)
measure="mean_depth_macro_entropy"
title="macro diversity: entropy based on genus abundances"
self.regression_plot(df_entropies, measure, title)
def make_cat_plot_for_age_categories(self, data, measure, title):
kind = "swarm"
sns.catplot(x="age_cat", y=measure, kind=kind, data=data,
palette=self.age_cat_palette
, order=["baby", "4 months", "12 months", "mother"]
)
plt.title("{title} ({filter})".format(
title=title, filter=self.filter
))
figure_name = "{dir}{sep}catplot.age_cat.{measure}.{filter}.svg".format(
dir=self.plot_dir, sep=self.dir_sep, measure=measure, filter=self.filter.replace("/", "_"),
)
plt.savefig(figure_name)
plt.clf()
def regression_plot(self, data, measure, title):
data = data[data.age_cat_short != "B"]
g0 = sns.lmplot(x="mean_depth_mean", y=measure,
palette=self.age_cat_palette,
hue="age_cat",
hue_order=["4 months", "12 months", "mother"],
data=data,
height=5,
aspect=1.5)
if measure == "nr_genera":
plt.ylim(0, 7)
if measure == "mean_depth_macro_entropy":
plt.ylim(-0.2, 0.8)
plt.ylabel("macro entropy")
title = "{title} ({filter})".format(title=title, filter=self.filter)
plt.xlabel("mean sample depth")
plt.title(title)
figure_name = "{}{}macro_{measure}_against_depth_{filter}.svg".\
format(self.plot_dir, self.dir_sep, measure=measure, filter=self.filter.replace("/", "_"))
plt.savefig(figure_name)
plt.clf()
def write_linear_regressions(self):
df_entropies = self.entropies_df
self.write_linear_regression_for_measure(df_entropies, "nr_genera")
self.write_linear_regression_for_measure(df_entropies, "mean_depth_macro_entropy")
def write_linear_regression_for_measure(self, data, measure):
df_lr = pd.DataFrame(columns=('slope', 'intercept', 'r_value', 'p_value', 'std_err'))
df_lr.index.name = 'age_cat'
age_cats = data.age_cat.unique()
for age_cat in age_cats:
data_age = data[data.age_cat == age_cat]
x_data = data_age.mean_depth_mean
y_data = data_age[measure]
slope, intercept, r_value, p_value, std_err = linregress(x=x_data, y=y_data)
df_lr.loc[age_cat] = [
slope, intercept, r_value, p_value, std_err
]
filename = "{}{}linear_regression_for_{measure}.txt".format(
self.plot_dir, self.dir_sep, measure=measure)
df_lr.to_csv(path_or_buf=filename, sep='\t')
def make_scatter_plots(self):
# total number of mappings between age 4 and 12 months and mother?
# might also make a mixed scatterplot matrix
df_families = self.merge_df.pivot_table(
values="mapped_norm",
index=["family", "genus"],
columns="age_cat"
).reset_index()
# extra derived field: hue does not work with strings that only contains numbers
df_families["genus_"] = "genus_" + df_families["genus"]
genera = df_families.genus.unique().tolist()
for genus in genera:
self.make_scatter_plot(df_families, genus, "mother", "baby")
self.make_scatter_plot(df_families, genus, "baby", "4 months")
self.make_scatter_plot(df_families, genus, "4 months", "12 months")
self.make_scatter_plot(df_families, genus, "mother", "12 months")
def make_scatter_plot(self, data, genus, x_value, y_value):
data = data[data.genus == genus]
# s_plot = sns.scatterplot(x=x_value, y=y_value, data=data, hue="genus_")
s_plot = sns.scatterplot(x=x_value, y=y_value, data=data, palette=self.age_cat_palette)
s_plot.set(xscale="log")
s_plot.set(yscale="log")
plt.title("genus {genus}: normalized mapped reads/50 million total reads".format(genus=genus))
y_value = y_value.replace(" ", "_")
figure_name = "{dir}{sep}sample_plots.scatter.{x_value}.{y_value}.genus_{genus}.png".format(
dir=self.plot_dir, sep=self.dir_sep,
x_value=x_value, y_value=y_value, genus=genus
)
plt.savefig(figure_name)
plt.clf()
def create_plot_dir(self):
self.plot_dir = self.sample_dir + self.dir_sep + "SamplePlots"
os.makedirs(self.plot_dir, exist_ok=True)
def do_analysis(self):
self.read_files()
self.create_plot_dir()
self.merge_df = self.merge_files()
self.merge_df = self.prepare_data()
# adding specific metadata from sample name
self.merge_df = self.prepare_specifics_for_project()
self.write_mapping_statistics() # all 4000 rows, also with zero coverage
self.do_statistical_analysis_on_unfiltered_data()
# NB: after normalization we lose the zero coverage rows (to prevent divide by zero)
self.merge_df = self.drop_zero_coverage_and_normalize_data()
self.genus_df = self.sort_genus_according_to_abundance()
self.build_color_palette_for_ages()
# before filtering
self.make_filter_sample_plots()
# NB: including mapped_norm (normalized) and therefore no rows with zero coverage
self.write_normalized_mapping_statistics()
self.merge_df = self.filter_on_breadth_threshold()
self.make_bar_plot()
# this may so be done before filtering
self.write_sample_genera_matrix()
# after filtering
self.make_abundance_plots()
self.entropies_df = self.calculate_entropies_per_sample()
self.make_diversity_plots()
self.write_linear_regressions()
verbose = False
if verbose:
self.make_scatter_plots()
def do_analysis(args_in):
parser = argparse.ArgumentParser()
parser.add_argument("-d", "--project_dir", dest="project_dir",
help="project directory with sample vs ref runs in subfolders", metavar="[project_dir]", required=True)
parser.add_argument("-tb", "--threshold_breadth", dest="threshold_breadth", metavar="[threshold_breadth]",
type=float,
help="threshold for breadth, between 0 and 1, will be combined with threshold for depth")
args = parser.parse_args(args_in)
print("Start running MakeSamplePlots")
print("project_dir: " + args.project_dir)
make = MakeSamplePlots(args.project_dir, args.threshold_breadth)
make.do_analysis()
# if __name__ == "__main__":
# do_analysis(sys.argv[1:])
# to do for testing, do not use in production
project_dir= r"D:\17 Dutihl Lab\_tools\_pipeline\ERP005989"
threshold_breadth="0.05"
do_analysis(["-d", project_dir, "-tb", threshold_breadth])