def plot_long_RNA_peak(peaks, ax, ce, top_n=10, y_val='log10p'):
lp = peaks[peaks.sense_gtype.str.contains('Long RNA')] \
.query('sample_count >= %i' %sample_cutoff)\
.groupby('sense_gname', as_index=False)\
.apply(pick_lp)
rfam_labs = defaultdict(lambda: 'Others') #get_peak_rfam_annotation(lp)
rfam_labs['CPN1'] = 'tRNA-lookalike'
rfam_labs['CASKIN2'] = 'Excised structured intron RNA'
rfam_labs['DAPK1'] = 'miRNA-like'
rfam_labs['RP11-51O6.1'] = 'Pseudogene'
assert (y_val in ['log10p', 'pileup'])
name_conversion = NameConversion()
rev_name_conversion = {v: k for k, v in name_conversion.encoder.items()}
lp = lp\
.assign(picked_RNA_sense = lambda d: d.sense_gname.map(name_conversion.convert).str.replace('-NPIPA8','')) \
.groupby('picked_RNA_sense')\
.apply(lambda d: d.nlargest(1, y_val))\
.nlargest(top_n, y_val) \
.pipe(cat_long_rna_type)\
.sort_values(y_val, ascending=False)
colors = lp.rt.map(peak_type_ce.encoder).values
sns.barplot(data=lp, x='picked_RNA_sense', y=y_val, palette=colors, ax=ax)
ax.legend().set_visible(False)
ax.set_xlabel('')
if y_val == 'log10p':
ax.set_ylabel('-$log_{10}$ p-value', fontsize=20)
else:
ax.set_ylabel('Coverage', fontsize=20)
ax.set_xticklabels(ax.get_xticklabels(),
rotation=70,
rotation_mode='anchor',
ha='right')
used_rfam = []
for i, xt in enumerate(ax.get_xticklabels()):
gn = xt.get_text()
if gn in rev_name_conversion.keys():
gn = rev_name_conversion[gn]
rfam = rfam_labs[gn]
used_rfam.append(rfam)
used = lp.rt.unique()
cc_ce = color_encoder()
cc_ce.encoder = {
k: v
for k, v in peak_type_ce.encoder.items() if k in used
}
cc_ce.show_legend(ax=ax, frameon=False, fontsize=20)
for col, xt in zip(colors, ax.get_xticklabels()):
xt.set_color(col)
def plot_pymc_bar(ax):
ce = color_encoder()
bar_df = get_pymc_df()\
.query('bayes_factor > 3')\
.nlargest(15,'delta')\
.assign(color = lambda d: ce.fit_transform(d['is_telo']))\
.assign(delta = lambda d: d.delta*100)
bar_df.plot\
.bar('gene_name','delta',color = bar_df.color.tolist(), ax = ax)
ce.show_legend(ax, frameon=False, fontsize=20, bbox_to_anchor=(0.5, 0.7))
xts = []
for xt in ax.get_xticklabels():
xtext = xt.get_text().split(':')[1]
xts.append((xtext))
ax.set_xticklabels(xts, rotation=70, rotation_mode='anchor', ha='right')
ax.set_xlabel('')
ax.set_ylabel('$\Delta$ % Plus strand')
return prep_order[3]
else:
return x
def rename_sample(xs):
sample_dict = defaultdict(int)
out_name = []
for x in xs:
prep = label_sample(x)
sample_dict[prep] += 1
out_name.append('%s %i' % (prep, sample_dict[prep]))
return out_name
label_ce = color_encoder()
label_ce.encoder = {}
for label, color in zip([
'DNase I', 'DNase I + Exo I', 'DNase I + NaOH',
'DNase I + Exo I + NaOH', 'NaOH', 'Untreated', 'Ladder', 'Fragmented',
"DNase I - 3'P", 'HEK293'
], [
'#d12604', '#ff96cb', '#964b06', '#f2a157', '#4286f4', 'black', 'grey',
'#592782', '#870c47', 'black'
]):
label_ce.encoder[label] = color
RNA_type = [
'Antisense', 'Mt', 'Other ncRNA', 'Other sncRNA', 'Protein coding',
'Repeats', 'miRNA', 'rRNA', 'snoRNA', 'tRNA', 'Vault RNA', 'Unannotated',
'5/5.8S rRNA', '18/28S rRNA', 'Mt-tRNA'
import numpy as np
import re
from sklearn.linear_model import LinearRegression, Ridge
from sklearn.model_selection import KFold, LeaveOneOut, GridSearchCV
from sklearn.preprocessing import LabelEncoder
from sklearn.metrics import r2_score
from sequencing_tools.viz_tools import color_encoder, okabeito_palette, simpsons_palette
import seaborn as sns
import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
from scipy.stats import pearsonr
from operator import itemgetter
import os
import pysam
from collections import Counter
end_ce = color_encoder()
end_ce.fit(["3' end", "5' end"],['darkgoldenrod','purple'])
def positioning(x):
return x[-1]
def count_to_cpm(count_array):
count_array = np.true_divide(count_array,count_array.sum()) * 1e6
return count_array
def get_end(x):
if 'head' in x:
return "5' N+"
elif 'tail' in x:
return "3' N-"
from collections import defaultdict
from sequencing_tools.viz_tools import okabeito_palette, \
simpsons_palette, \
color_encoder
from sequencing_tools.io_tools import ReadPicardRNA
from collections import defaultdict
import re
import glob
import os
from plotting_utils import label_sample, rename_sample, \
label_ce, rna_type_ce, \
figure_path, work_path
from functools import lru_cache
plt.rc('font', **{'family': 'sans-serif', 'sans-serif': 'Arial'})
small_RNA_ce = color_encoder()
label_order = [
'Untreated', 'NaOH', 'WGS-sim', 'DNase I', 'DNase I + Exo I',
"DNase I - 3'P"
]
metric_path = work_path + '/cfNA/tgirt_map/merged_bam/filtered_bam'
metrics = glob.glob(metric_path + '/*.RNA_Metrics')
metrics = list(filter(lambda x: 'sense' not in x, metrics))
def read_metric(metric):
return pd.read_table(metric, skiprows=6, nrows=1)\
.pipe(pd.melt) \
.pipe(lambda d: d[d.variable.str.contains('TRANSCRIPT_STRAND_')])\
.pipe(lambda d: d[d.variable.str.contains('PCT')])
import matplotlib.pyplot as plt
sys.path.insert(0, '/stor/home/cdw2854/cfNA/peak_callings')
from structural_peaks import PeakAnalyzer, mRNAFilter, GenicIntersect, NameConversion, GeneMapper, TrnaLookAlike
from exon_coverage import ExonFilter
import dask.dataframe as dd
plt.rc('axes', labelsize=20)
plt.rc('xtick', labelsize=20)
plt.rc('ytick', labelsize=20)
plt.rc('font', **{'family': 'sans-serif', 'sans-serif': 'Arial'})
pileup_cutoff = 5
sample_cutoff = 5
project_path = '/stor/work/Lambowitz/cdw2854/cfNA/tgirt_map'
project_path = '/stor/work/Lambowitz/yaojun/Work/cfNA/tgirt_map'
peak_path = project_path + '/bed_files/merged_bed/MACS2/annotated'
peak_type_ce = color_encoder()
peak_type_ce.encoder = {
'mRNA': 'purple',
'Pseudogene': 'darkblue',
'Exon': 'purple',
'Intron': '#fca237',
'Exon-intron': '#7bb73e',
'Within intron': '#f9b109',
'Stem-loop': '#f9b109',
'miRNA': 'darkgreen',
'rRNA': '#15879b',
'Mismapped': '#bcbb76',
'Others': 'black',
'Intergenic': 'black',
'tRNA-lookalike': 'red',
'Full-length intron': '#725001',
import numpy as np
from sequencing_tools.viz_tools import color_encoder, okabeito_palette
from sklearn.preprocessing import LabelEncoder, StandardScaler, label_binarize, OneHotEncoder, LabelBinarizer
from sklearn.feature_extraction import DictVectorizer
from sklearn.decomposition import PCA, FactorAnalysis
import matplotlib.patches as mpatches
from sklearn.model_selection import LeaveOneOut, KFold, train_test_split, GridSearchCV
from sklearn.metrics import roc_auc_score, roc_curve
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier
from sklearn.pipeline import Pipeline
from helper_function import *
import glob
import os
pca_ce = color_encoder()
def pca_color(labs, colors):
return {lab: col for lab, col in zip(labs, colors)}
def pca_biplot(train_df, ax):
pca = PCA(n_components=3)
train_df = train_df \
.query('label !="Zero" & label != ""')
tdf = extract_train_cols(train_df)
d = pca.fit_transform(StandardScaler().fit_transform(tdf))
pca_df = pd.DataFrame(d)
pca_df.columns = ['PC%i' % (int(col) + 1) for col in pca_df.columns]
pca_df['label'] = train_df.label.values