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ad2csv.py
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ad2csv.py
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#%%
import pandas as pd
import numpy as np
import anndata as ad
from sklearn.manifold import TSNE
from scipy.stats import pearsonr
import scanpy as sc
import json
from utils import AnnDataAggregator
#%%
# define if plots should be created (requires installation of plotly in environment)
make_plots = False
if make_plots:
import plotly
import plotly.express as px
import plotly.io as pio
pio.templates.default = "simple_white"
import dash_bio as dashbio
# load config
config = json.load(
open("MIRSORT_ANNOTATION_DF.json")
)
ad_path = config["ad_reduce_features_further.py"]["ad_aggregated_bloodcomponents_path"]
ad_extra_path = config["ad_reduce_features_further.py"]["ad_aggregated_wholeblood_path"]
tsne_path = config["ad2csv.py"]["tsne_path"]
obs_path = config["ad2csv.py"]["obs_path"]
GEO_smp_record_info = config["ad2csv.py"]["GEO_smp_record_info"]
suppl_table_1_path = config["ad2csv.py"]["suppl_table_1_path"]
suppl_table_2_path = config["ad2csv.py"]["suppl_table_2_path"]
suppl_table_3_path = config["ad2csv.py"]["suppl_table_3_path"]
sRNAtype_path = config["ad2csv.py"]["sRNAtype_path"]
sRNA_subtype_path = config["ad2csv.py"]["sRNA_subtype_path"]
expr_path = config["ad2csv.py"]["expr_path"]
expr_extra_path = config["ad2csv.py"]["expr_extra_path"]
box_path = config["ad2csv.py"]["box_path"]
sRNA_aggreg_path = config["ad2csv.py"]["sRNA_aggreg_path"]
expr_mean_path = config["ad2csv.py"]["expr_mean_path"]
marker_path = config["ad2csv.py"]["marker_path"]
cell_prop_path = config["ad2csv.py"]["cell_prop_path"]
RPM_mean_path = config["ad2csv.py"]["RPM_mean_path"]
scaled_prop_path = config["ad2csv.py"]["scaled_prop_path"]
corr_path = config["ad2csv.py"]["corr_path"]
tsne_plot_all_path = config["ad2csv.py"]["tsne_plot_all_path"]
tsne_plot_filtered_path = config["ad2csv.py"]["tsne_plot_filtered_path"]
counts_plot_path = config["ad2csv.py"]["counts_plot_path"]
FA_plot_path = config["ad2csv.py"]["FA_plot_path"]
#%%
cell_types = [
"Plasma",
"Erythrocytes",
"Thrombocytes",
"Monocytes",
"Neutrophils",
"Eosinophils",
"Basophils",
"NK cells",
"CD4+ T cells",
"CD8+ T cells",
"B cells",
]
color_list = ['rgb(169,169,169)','rgb(255,139,139)','rgb(239,231,222)','rgb(178,188,207)','rgb(108,194,74)','rgb(79,150,51)','rgb(176,233,160)','rgb(0,157,217)','rgb(0,91,153)','rgb(0,118,163)','rgb(0,112,192)']
celltype_lut = dict(zip(cell_types, color_list))
sRNAtype_lut = {
'miRNA': '#6cc24a',
'rRNA': '#005b99',
'YRNA': '#009dd9',
'tRNA': '#0076a1',
'lncRNA': '#8a9ab6',
'snoRNA': '#526483',
'snRNA': '#ccd3df',
'piRNA': '#edf4fd',
}
cell_counts_prev = [
'Erythrocytes',
'Thrombocytes',
'Monocytes',
'Neutrophils',
'Eosinophils',
'Basophils',
'NK_cells',
'CD4',
'CD8',
'B_cells',
'Monocytes__intermediate',
'Monocytes__classical',
'Monocytes__non-classical',
'NK_cells__Cd56dim',
'NK_cells__Cd56high',
'NK_cells__Cd56dim_mature',
'CD4__TEMRA',
'CD4__naive',
'CD4__TCM',
'CD4__TEM',
'CD4__Th17',
'CD4__Th1',
'CD4__Th2',
'CD4__Tregs',
'CD8__TEMRA',
'CD8__naive',
'CD8__TCM',
'CD8__TEM',
]
cell_counts = [
'Erythrocytes',
'Thrombocytes',
'Monocytes',
'Neutrophils',
'Eosinophils',
'Basophils',
'NK cells',
'CD4+ T cells',
'CD8+ T cells',
'B cells',
'Monocytes intermediate',
'Monocytes classical',
'Monocytes non-classical',
'NK cells CD56dim',
'NK cells CD56high',
'NK cells CD56dim mature',
'CD4+ TEMRA',
'CD4+ naive',
'CD4+ TCM',
'CD4+ TEM',
'CD4+ Th17',
'CD4+ Th1',
'CD4+ Th2',
'CD4+ Tregs',
'CD8+ TEMRA',
'CD8+ naive',
'CD8+ TCM',
'CD8+ TEM',
]
col_rename_dict = dict(zip(cell_counts_prev, cell_counts))
extended_color_list = ['rgb(255,139,139)','rgb(239,231,222)','rgb(178,188,207)','rgb(108,194,74)','rgb(79,150,51)','rgb(176,233,160)','rgb(0,157,217)','rgb(0,91,153)','rgb(0,118,163)','rgb(0,112,192)','rgb(178,188,207)','rgb(178,188,207)','rgb(178,188,207)','rgb(0,157,217)','rgb(0,157,217)','rgb(0,157,217)','rgb(0,91,153)','rgb(0,91,153)','rgb(0,91,153)','rgb(0,91,153)','rgb(0,91,153)','rgb(0,91,153)','rgb(0,91,153)','rgb(0,91,153)','rgb(0,118,163)','rgb(0,118,163)','rgb(0,118,163)','rgb(0,118,163)']
cellcounts_lut = dict(zip(cell_counts, extended_color_list))
obs_to_keep = [
'Patient',
'miRBlood_ID',
'sequencer',
'total_read_counts_before_preprocessing',
'total_count_after_preprocessing',
'total_count_ratio__after_to_before',
'total_count',
'RIN',
'QC_RNA_ratio',
'Lab_RNA_amount',
'Gender',
'Age',
'Age_binned_5',
'Age_binned_10',
'mirsort_group',
'cellsorting__cell_counts',
'cellsorting__elution_volume',
'cellsorting__small_concentration',
'cellsorting__miRNA_concentration',
'cellsorting__Purity',
'blood_count_microliter',
'Erythrocytes',
'Thrombocytes',
'Monocytes',
'Neutrophils',
'Eosinophils',
'Basophils',
'NK cells',
'CD4+ T cells',
'CD8+ T cells',
'B cells',
'Leukocytes',
'Lymphocytes',
'Monocytes_clinical',
'T_cells',
'Monocytes intermediate',
'Monocytes classical',
'Monocytes non-classical',
'NK cells CD56dim',
'NK cells CD56high',
'NK cells CD56dim mature',
'CD4+ TEMRA',
'CD4+ naive',
'CD4+ TCM',
'CD4+ TEM',
'CD4+ Th17',
'CD4+ Th1',
'CD4+ Th2',
'CD4+ Tregs',
'CD8+ TEMRA',
'CD8+ naive',
'CD8+ TCM',
'CD8+ TEM'
]
def streamline_obs(anndata):
# rename sequencer
anndata.obs.sequencer.cat.add_categories(['NextSeq_2000'], inplace=True)
anndata.obs.loc[anndata.obs.sequencer.isin(['VH00530']),'sequencer'] = 'NextSeq_2000'
anndata.obs.sequencer.cat.remove_unused_categories(inplace=True)
# rename columns
anndata.obs.rename(columns=col_rename_dict, inplace=True)
return anndata
# define sRNA and signature list of interest
name_OI = 'rRNA-28S_bin-162'
sRNAs_OI = ['miR-2115-3p', 'miR-224-5p', 'miR-218-5p', 'miR-4676-3p', 'miR-6503-5p']
#%%
# load anndata of blood components
MS_ad = ad.read_h5ad(ad_path)
MS_ad
#%%
# rename blood components and subpopulations
MS_ad.obs.mirsort_group.cat.add_categories(['Plasma', 'NK cells', 'CD4+ T cells', 'CD8+ T cells', 'B cells'], inplace=True)
MS_ad.obs.loc[MS_ad.obs.mirsort_group.isin(['Plasma_Norgen']),'mirsort_group'] = 'Plasma'
MS_ad.obs.loc[MS_ad.obs.mirsort_group.isin(['NK_cells']),'mirsort_group'] = 'NK cells'
MS_ad.obs.loc[MS_ad.obs.mirsort_group.isin(['CD4']),'mirsort_group'] = 'CD4+ T cells'
MS_ad.obs.loc[MS_ad.obs.mirsort_group.isin(['CD8']),'mirsort_group'] = 'CD8+ T cells'
MS_ad.obs.loc[MS_ad.obs.mirsort_group.isin(['B_cells']),'mirsort_group'] = 'B cells'
MS_ad.obs.mirsort_group.cat.remove_unused_categories(inplace=True)
MS_ad.obs.mirsort_group.unique()
#%%
# streamline obs
MS_ad = streamline_obs(MS_ad)
MS_ad.obs
#%%
# load anndata of unblocked whole blood + PAXgene
MS_extra_ad = ad.read_h5ad(ad_extra_path)
MS_extra_ad
#%%
# rename whole blood + PAXgene
MS_extra_ad.obs.mirsort_group.cat.add_categories(['Whole Blood'], inplace=True)
MS_extra_ad.obs.loc[MS_extra_ad.obs.mirsort_group.isin(['EDTA_WholeBlood']),'mirsort_group'] = 'Whole Blood'
MS_extra_ad.obs.mirsort_group.cat.remove_unused_categories(inplace=True)
MS_extra_ad.obs.mirsort_group.unique()
#%%
# streamline obs
MS_extra_ad = streamline_obs(MS_extra_ad)
MS_extra_ad.obs
#%%
# write data for supplementary table 1 to file
MS_ad.obs[['Patient'] + cell_counts].drop_duplicates().reset_index(drop=True).to_csv(suppl_table_1_path, index=False)
#%%
# write data for supplementary table 2 to file
obs_subs = MS_ad.obs.copy()
obs_subs[['miRBlood_ID', 'mirsort_group', 'cellsorting__cell_counts', 'cellsorting__elution_volume', 'cellsorting__small_concentration', 'cellsorting__miRNA_concentration', 'cellsorting__Purity']].reset_index(drop=True).to_csv(suppl_table_2_path, index=False)
#%%
# write info for GEO sample record
GEO_smp_record = pd.concat([MS_ad.obs, MS_extra_ad.obs],axis=0)
GEO_smp_record[['mirsort_group','sequencer']].to_csv(GEO_smp_record_info)
#%%
################################################################################################################################################################
# tSNE DATA
#%%
# quality control with purity filter
# set 'cellsorting__Purity' of Plasma to 1
MS_ad.obs.loc[MS_ad.obs.mirsort_group.str.match('Plasma'),'cellsorting__Purity'] = 1
# apply filter
MS_ad = MS_ad[MS_ad.obs.cellsorting__Purity > 0.7,:]
MS_ad
#%%
# quality control with tSNE
tsne = TSNE(n_components=2, random_state=0)
projections = tsne.fit_transform(MS_ad.X)
tsne_df = pd.concat([pd.DataFrame(projections, index=MS_ad.obs_names, columns=['tSNE vector 1', 'tSNE vector 2']),MS_ad.obs.mirsort_group],axis=1)
if make_plots:
fig_1 = px.scatter(
tsne_df, x='tSNE vector 1', y='tSNE vector 2',
color='mirsort_group',
color_discrete_map=celltype_lut,
hover_name=tsne_df.index
)
fig_1.update_layout(legend_title_text='cell type', font_family='Arial').show()
fig_1.write_image(tsne_plot_all_path, width=600, height=600)
fig_1.write_image(tsne_plot_all_path, width=600, height=600)
#%%
# apply tSNE filter
tSNE_outliers = [
'Neutrophils__P51',
'Plasma_Norgen__P02',
'Basophils__P37',
'B_cells__P38',
]
MS_ad = MS_ad[~MS_ad.obs_names.isin(tSNE_outliers),:]
MS_ad
#%%
# quality control with tSNE
from sklearn.manifold import TSNE
tsne = TSNE(n_components=2, random_state=0)
projections = tsne.fit_transform(MS_ad.X)
tsne_df = pd.concat([pd.DataFrame(projections, index=MS_ad.obs_names, columns=['tSNE vector 1', 'tSNE vector 2']),MS_ad.obs.mirsort_group],axis=1)
if make_plots:
fig_2 = px.scatter(
tsne_df, x='tSNE vector 1', y='tSNE vector 2',
color='mirsort_group',
color_discrete_map=celltype_lut,
hover_name=tsne_df.index
)
fig_2.update_layout(legend_title_text='cell type', font_family='Arial').show()
fig_2.write_image(tsne_plot_filtered_path, width=600, height=600)
fig_2.write_image(tsne_plot_filtered_path, width=600, height=600)
#%%
# write tSNE to file
tsne_df.to_csv(tsne_path)
#%%
if make_plots:
plot_df = MS_ad.obs.copy()
counts_vs_miRNA = px.scatter(plot_df, x='cellsorting__cell_counts', y='cellsorting__miRNA_concentration', color='mirsort_group', color_discrete_map=celltype_lut, hover_name=plot_df.index, category_orders={'mirsort_group': ['Plasma', 'Erythrocytes', 'Thrombocytes', 'Monocytes', 'Neutrophils', 'Eosinophils', 'Basophils', 'NK cells', 'CD4+ T cells', 'CD8+ T cells', 'B cells']})
counts_vs_miRNA.show()
counts_vs_sRNA = px.scatter(plot_df, x='cellsorting__cell_counts', y='cellsorting__small_concentration', color='mirsort_group', color_discrete_map=celltype_lut, hover_name=plot_df.index, category_orders={'mirsort_group': ['Plasma', 'Erythrocytes', 'Thrombocytes', 'Monocytes', 'Neutrophils', 'Eosinophils', 'Basophils', 'NK cells', 'CD4+ T cells', 'CD8+ T cells', 'B cells']})
counts_vs_sRNA.show()
# calculate ratio miRNA region / small RNA region
plot_df['miRNA_region/sRNA_region'] = plot_df.cellsorting__miRNA_concentration/plot_df.cellsorting__small_concentration
ratio_miRNA_sRNA = px.box(plot_df, x='mirsort_group', y='miRNA_region/sRNA_region', color='mirsort_group', color_discrete_map=celltype_lut, hover_name=plot_df.index, category_orders={'mirsort_group': ['Plasma', 'Erythrocytes', 'Thrombocytes', 'Monocytes', 'Neutrophils', 'Eosinophils', 'Basophils', 'NK cells', 'CD4+ T cells', 'CD8+ T cells', 'B cells']})
ratio_miRNA_sRNA.update_layout(xaxis_title="", showlegend=False, font_family='Arial').show()
ratio_miRNA_sRNA.update_xaxes(tickangle=0).write_image(FA_plot_path, width=1200, height=600)
#%%
################################################################################################################################################################
# OBS DATA
#%%
# write obs to file
obs_df = MS_ad.obs
obs_df[obs_to_keep].to_csv(obs_path)
#%%
# barplot of patient data (age and sex)
if make_plots:
patients = px.histogram(obs_df.drop_duplicates(subset=["miRBlood_ID"]), x='Age', color='Gender', barmode='group')
patients
#%%
# boxplot of total_count_after_preprocessing / RIN / total_count_ratio__after_to_before / QC_RNA_ratio / Lab_RNA_amount
if make_plots:
fig_3 = px.box(MS_ad.obs, x='mirsort_group', y='total_count_after_preprocessing', color='mirsort_group', color_discrete_map=celltype_lut)
fig_3.update_layout(xaxis_title="", yaxis_title="total read counts after preprocessing", showlegend=False, font_family='Arial')
fig_3.show()
fig_3.write_image(counts_plot_path, width=1200, height=600)
fig_3.write_image(counts_plot_path, width=1200, height=600)
#%%
################################################################################################################################################################
# SRNA TYPE DATA
#%%
sRNAtype_df = MS_ad.var
sRNAtype_df
#%%
# write to file
sRNAtype_df.to_csv(sRNAtype_path)
#%%
# plot pie chart of sRNA type attribution of signature
sRNA_type_label = sRNAtype_df.loc[sRNAs_OI,'small_RNA_class_annotation']
sRNA_type_label = sRNA_type_label.cat.remove_unused_categories()
if make_plots:
signature_sRNAannot = px.pie(sRNA_type_label, values=sRNA_type_label.value_counts().values, names=sRNA_type_label.value_counts().index, color=sRNA_type_label.value_counts().index, hole = 0.55, width=500, height=500, color_discrete_map=sRNAtype_lut)
signature_sRNAannot.update_layout(showlegend=False).update_traces(textposition="inside", textinfo="percent+label", textfont_size=15)
signature_sRNAannot
#%%
################################################################################################################################################################
# SRNA SUBTYPE DATA
#%%
sRNA_subtypes_df = sRNAtype_df.reset_index()
sRNA_subtypes_df.loc[sRNA_subtypes_df.small_RNA_class_annotation == 'rRNA','sRNA_subclass'] = sRNA_subtypes_df.subclass_name.str.split('_').str[0]
sRNA_subtypes_df.loc[sRNA_subtypes_df.small_RNA_class_annotation == 'YRNA','sRNA_subclass'] = sRNA_subtypes_df.subclass_name.str.split('_').str[0]
sRNA_subtypes_df.loc[sRNA_subtypes_df.small_RNA_class_annotation == 'tRNA','sRNA_subclass'] = sRNA_subtypes_df.subclass_name.str.split('__').str[0]
sRNA_subtypes_df.loc[sRNA_subtypes_df.small_RNA_class_annotation == 'piRNA','sRNA_subclass'] = 'piRNA'
sRNA_subtypes_df.loc[sRNA_subtypes_df.small_RNA_class_annotation == 'lncRNA','sRNA_subclass'] = 'lncRNA'
sRNA_subtypes_df.loc[sRNA_subtypes_df.subclass_name.str.contains('SNORA'),'sRNA_subclass'] = 'H/ACA Box'
sRNA_subtypes_df.loc[sRNA_subtypes_df.subclass_name.str.contains('SNORD'),'sRNA_subclass'] = 'C/D Box'
sRNA_subtypes_df.loc[((sRNA_subtypes_df.small_RNA_class_annotation == 'snoRNA') & (~sRNA_subtypes_df.subclass_name.str.contains(r'SNOR[A|D]'))),'sRNA_subclass'] = 'misc'
sRNA_subtypes_df.loc[sRNA_subtypes_df.small_RNA_class_annotation == 'snRNA','sRNA_subclass'] = sRNA_subtypes_df.subclass_name.str.split('-').str[0]
sRNA_subtypes_df.loc[((sRNA_subtypes_df.small_RNA_class_annotation == 'miRNA') & (~sRNA_subtypes_df.subclass_name.str.contains(r'-[5|3]p'))),'sRNA_subclass'] = 'misc'
sRNA_subtypes_df.loc[sRNA_subtypes_df.subclass_name.str.contains('-5p'),'sRNA_subclass'] = '5p'
sRNA_subtypes_df.loc[sRNA_subtypes_df.subclass_name.str.contains('-3p'),'sRNA_subclass'] = '3p'
sRNA_subtypes_df = pd.DataFrame(sRNA_subtypes_df.groupby('small_RNA_class_annotation').sRNA_subclass.value_counts())
sRNA_subtypes_df
#%%
# write to file
sRNA_subtypes_df.to_csv(sRNA_subtype_path)
#%%
################################################################################################################################################################
# EXPRESSION DATA (for download)
expr_df = MS_ad.to_df()
expr_df = expr_df.T
# add sRNA type as second index
expr_df['sRNA_type'] = MS_ad.var
expr_df = expr_df.set_index('sRNA_type', append=True)
expr_df.to_csv(expr_path)
#%%
################################################################################################################################################################
# EXTRA EXPRESSION DATA (for download)
expr_extra_df = MS_extra_ad.to_df()
expr_extra_df = expr_extra_df.T
# add sRNA type as second index
expr_extra_df['sRNA_type'] = MS_extra_ad.var
expr_extra_df = expr_extra_df.set_index('sRNA_type', append=True)
expr_extra_df.to_csv(expr_extra_path)
#%%
################################################################################################################################################################
# CELLTYPE MARKER DATA
#%%
sc.tl.rank_genes_groups(MS_ad, 'mirsort_group', use_raw=False, method='wilcoxon', pts=True)
sc.pl.rank_genes_groups_dotplot(MS_ad, n_genes=5, standard_scale='var')
#%%
# get ordered marker list (positive log2FC and 100% expression in blood component)
marker_dict = {}
for celltype in cell_types:
markers = sc.get.rank_genes_groups_df(MS_ad, group=celltype).set_index('names')
markers = markers.loc[((markers.pvals_adj < 0.05) & (markers.logfoldchanges > 0) & (markers.pct_nz_group == 1)),:].index
marker_dict.update({celltype: list(markers)})
marker_dict.keys()
#%%
for celltype in cell_types:
print(celltype)
print(len(marker_dict[celltype]))
#%%
# save to file
with open(marker_path, "w") as outfile:
json.dump(marker_dict, outfile)
#%%
################################################################################################################################################################
# BOXPLOT DATA
#%%
# calculate boxplot data for log2(RPM+1) plot
box_df = pd.concat([MS_ad.to_df(), MS_ad.obs.mirsort_group], axis=1)
box_df = box_df.groupby(by='mirsort_group').describe()
box_df = box_df.loc[cell_types,:]
box_df
#%%
# write to file
box_df.to_csv(box_path)
#%%
# plot boxplot for sRNA of interest
plot_df = pd.melt(box_df[name_OI][['min','25%','50%','75%','max']].T)
if make_plots:
single_box = px.box(plot_df, x='mirsort_group', y='value', color='mirsort_group', color_discrete_map=celltype_lut).update_layout(xaxis_title="", yaxis_title="log2(RPM+1)", showlegend=False)
single_box
#%%
################################################################################################################################################################
# SRNA CLASS AGGREGATED DATA
#%%
MS_ad_sRNAclass_aggreg = AnnDataAggregator(MS_ad, aggregate_obs=False, by='small_RNA_class_annotation', work_on_raw=True)
MS_ad_sRNAclass_aggreg
#%%
# calculate RPM from raw counts
MS_ad_sRNAclass_aggreg.X = (MS_ad_sRNAclass_aggreg.raw.X.T / MS_ad_sRNAclass_aggreg.raw.X.T.sum(axis=0) * 1e6).T
# calculate mean RPM
res_l_mean = []
for i in MS_ad_sRNAclass_aggreg.obs.mirsort_group.unique():
res_l_mean.append(
[
np.mean(MS_ad_sRNAclass_aggreg[MS_ad_sRNAclass_aggreg.obs.mirsort_group.isin([i]), :].X, axis=0)
]
)
res_l_mean = np.array(res_l_mean)[:, 0, :]
res_l_mean = pd.DataFrame(
res_l_mean,
columns=MS_ad_sRNAclass_aggreg.var_names,
index=MS_ad_sRNAclass_aggreg.obs.mirsort_group.unique()
)
res_l_mean = res_l_mean.loc[cell_types,:]
res_l_mean
#%%
# write to file
res_l_mean.to_csv(sRNA_aggreg_path)
#%%
# plot proportional sRNA class (RPM) expression per blood component (heatmap)
if make_plots:
px.imshow(res_l_mean / res_l_mean.sum(axis=1)[:,None], color_continuous_scale="blues")
#%%
# plot RPM proportion for a given blood component (pie)
celltype_OI = 'B cells'
pie_data = res_l_mean.copy()
# subset to celltype
pie_data = pie_data.loc[celltype_OI,:]
# calculate fraction
pie_data = pie_data / pie_data.sum()
# combine all sRNAs < 2% as others
others = pd.Series(data=pie_data[pie_data < 0.02].sum(), index=['others (<2%)'])
pie_data = pie_data[pie_data > 0.02]
pie_data = pd.concat([pie_data, others],axis=0)
if make_plots:
px.pie(pie_data, values=pie_data.values, names=pie_data.index, color_discrete_sequence=extended_color_list)
#%%
################################################################################################################################################################
# PIECHART/HEATMAP DATA
#%%
# calculate log2(RPM+1) mean
res_i_mean = []
for i in MS_ad.obs.mirsort_group.unique():
res_i_mean.append(
[
np.mean(MS_ad[MS_ad.obs.mirsort_group.isin([i]), :].X, axis=0)
]
)
res_i_mean = np.array(res_i_mean)[:, 0, :]
res_i_mean = pd.DataFrame(
res_i_mean,
columns=MS_ad.var_names,
index=MS_ad.obs.mirsort_group.unique()
)
res_i_mean = res_i_mean.loc[cell_types,:]
res_i_mean
#%%
# write to file
res_i_mean.to_csv(expr_mean_path)
#%%
# MARKER: plot log2(RPM+1) expression per celltype (top2_union only)
if make_plots:
top2_union = []
for celltype in marker_dict.keys():
top2_union = top2_union + marker_dict[celltype][0:2]
top2_union = list(set(top2_union))
heat_df = res_i_mean.loc[:,top2_union].copy().dropna().T
columns = list(heat_df.columns)
rows = list(heat_df.index)
top2_union_plot = dashbio.Clustergram(
data=heat_df.values,
column_labels=columns,
row_labels=rows,
center_values=False,
color_threshold={"row": 250, "col": 700},
cluster="row",
height=800,
width=700,
color_map="blues",
)
top2_union_plot.update_layout({'paper_bgcolor': 'rgba(255, 255, 255, 255)'})#.update_traces(dict(showscale=False, coloraxis=None), selector={"type": "heatmap"})
#%%
# TOPEXPRESSED: plot log2(RPM+1) expression per celltype (top20_union only)
if make_plots:
top20_union = []
for celltype in res_i_mean.index:
top20_union = top20_union + list(res_i_mean.loc[celltype,:].sort_values(ascending=False)[0:20].index)
top20_union = list(set(top20_union))
heat_df = res_i_mean.loc[:,top20_union].copy().dropna().T
columns = list(heat_df.columns)
rows = list(heat_df.index)
top20_union_plot = dashbio.Clustergram(
data=heat_df.values,
column_labels=columns,
row_labels=rows,
center_values=False,
color_threshold={"row": 250, "col": 700},
cluster="row",
height=800,
width=700,
color_map="blues",
)
top20_union_plot.update_layout({'paper_bgcolor': 'rgba(255, 255, 255, 255)'})#.update_traces(dict(showscale=False, coloraxis=None), selector={"type": "heatmap"})
#%%
# calculate RPM from raw counts
MS_ad.X = (MS_ad.raw.X.T / MS_ad.raw.X.T.sum(axis=0) * 1e6).T
# calculate RPM proportion
res_j_mean = []
res_j_std = []
for i in MS_ad.obs.mirsort_group.unique():
res_j_mean.append(
[
np.mean(MS_ad[MS_ad.obs.mirsort_group.isin([i]), :].X, axis=0)
]
)
res_j_std.append(
[
np.nanstd(MS_ad[MS_ad.obs.mirsort_group.isin([i]), :].X, axis=0)
]
)
res_j_mean = np.array(res_j_mean)[:, 0, :]
res_j_std = np.array(res_j_std)[:, 0, :]
res_j_mean = pd.DataFrame(
res_j_mean,
columns=MS_ad.var_names,
index=MS_ad.obs.mirsort_group.unique()
)
res_j_std = pd.DataFrame(
res_j_std,
columns=MS_ad.var_names,
index=MS_ad.obs.mirsort_group.unique()
)
res_j_prop = res_j_mean / res_j_mean.sum(axis=0)
res_j_prop.index = MS_ad.obs.mirsort_group.unique()
res_j_prop = res_j_prop.loc[cell_types,:]
res_j_prop
#%%
# calculate prop per celltype
cell_prop = res_j_mean / res_j_mean.sum(axis=1)[:,None]
cell_prop
#%%
# write to file
res_j_mean.to_csv(RPM_mean_path)
cell_prop.to_csv(cell_prop_path)
#%%
# plot RPM proportion per celltype
classes_OI = ['miRNA']
celltype_OI = 'B cells'
# OPTIONAL: subset to miRNAs
sRNAtype_df = pd.read_csv(sRNAtype_path, index_col=0)
sRNA_selected = sRNAtype_df.loc[sRNAtype_df.small_RNA_class_annotation.isin(classes_OI),:].index
pie_data = cell_prop[sRNA_selected]
# subset to celltype
pie_data = pie_data.loc[celltype_OI,:]
# calculate fraction
pie_data = pie_data / pie_data.sum()
# combine all sRNAs < 2% as others
others = pd.Series(data=pie_data[pie_data < 0.02].sum(), index=['others (<2%)'])
pie_data = pie_data[pie_data > 0.02]
pie_data = pd.concat([pie_data, others],axis=0)
if make_plots:
cell_proportion = px.pie(pie_data, values=pie_data.values, names=pie_data.index, color_discrete_sequence=extended_color_list)
cell_proportion
#%%
# calculate scaled RPM proportion
# set 'cellsorting__cell_counts' of Plasma to 1
MS_ad.obs.loc[MS_ad.obs.mirsort_group.str.match('Plasma'),'cellsorting__cell_counts'] = 1
MS_ad.obs.cellsorting__cell_counts
# set 'blood_count_microliter' of Plasma to 0.5
MS_ad.obs.loc[MS_ad.obs.mirsort_group.str.match('Plasma'),'blood_count_microliter'] = 0.5
MS_ad.obs.blood_count_microliter
# set cell count of 0 to nan (otherwise fails to calculate this sample)
MS_ad.obs.cellsorting__cell_counts[MS_ad.obs.cellsorting__cell_counts == 0] = np.nan
# calculate total small RNA eluted [pg]
MS_ad.obs['sRNA_mass_eluted'] = np.multiply(MS_ad.obs.cellsorting__small_concentration, MS_ad.obs.cellsorting__elution_volume)
# calculate small RNA mass per sorted cell [pg/cell]
MS_ad.obs['sRNA_mass_per_cells'] = np.divide(MS_ad.obs.sRNA_mass_eluted, MS_ad.obs.cellsorting__cell_counts)
# calculate sRNA mass of celltype per bloodvolume [pg/µl]
MS_ad.obs['sRNA_mass_per_bloodvol'] = np.multiply(MS_ad.obs.sRNA_mass_per_cells, MS_ad.obs.blood_count_microliter)
# create new adata with scaled RPM values
MS_ad_scaled = ad.AnnData(
X=MS_ad.X * MS_ad.obs.sRNA_mass_per_bloodvol[:, np.newaxis],
obs=MS_ad.obs,
var=MS_ad.var,
)
MS_ad_scaled
# create dataframe with mean, std and prop of expression per cell type
res_k_mean = []
res_k_std = []
for i in MS_ad_scaled.obs.mirsort_group.unique():
res_k_mean.append(
[
np.nanmean(MS_ad_scaled[MS_ad_scaled.obs.mirsort_group.isin([i]), :].X, axis=0)
]
)
res_k_std.append(
[
np.nanstd(MS_ad_scaled[MS_ad_scaled.obs.mirsort_group.isin([i]), :].X, axis=0)
]
)
res_k_mean = np.array(res_k_mean)[:, 0, :]
res_k_std = np.array(res_k_std)[:, 0, :]
res_k_mean = pd.DataFrame(
res_k_mean,
columns=MS_ad_scaled.var_names,
index=MS_ad_scaled.obs.mirsort_group.unique()
)
res_k_std = pd.DataFrame(
res_k_std,
columns=MS_ad_scaled.var_names,
index=MS_ad_scaled.obs.mirsort_group.unique()
)
res_k_prop = res_k_mean / res_k_mean.sum(axis=0)
res_k_prop.index = MS_ad_scaled.obs.mirsort_group.unique()
res_k_prop = res_k_prop.loc[cell_types,:]
res_k_prop
#%%
# write to file
res_k_prop.to_csv(scaled_prop_path)
MS_ad.obs[['miRBlood_ID','mirsort_group','sRNA_mass_eluted','sRNA_mass_per_cells','sRNA_mass_per_bloodvol']].reset_index(drop=True).to_csv(suppl_table_3_path, index=False)
#%%
# plot pie of sRNA OI
if make_plots:
single_deconvolution = px.pie(res_k_prop, values=name_OI, names=res_k_prop.index, color=res_k_prop.index, color_discrete_map=celltype_lut, hole = 0.55, width=500, height=500).update_layout(showlegend=False).update_traces(textposition="inside", textinfo="percent+label", textfont_size=15) # title="Cell type contribution to abundance in blood", title_x=0.5,
single_deconvolution
#%%
# plot heatmap of signature OI
if make_plots:
heat_df = res_k_prop.copy().dropna(axis=1).T
columns = list(heat_df)
rows = list(sRNAs_OI)
# clustermap of deconvolution
signature_deconvolution = dashbio.Clustergram(
data=heat_df.loc[rows].values,
column_labels=columns,
row_labels=rows,
color_threshold={"row": 250, "col": 700},
center_values=False,
cluster="row",
height=800,
width=700,
color_map="blues",
)
signature_deconvolution.update_layout({'paper_bgcolor': 'rgba(255, 255, 255, 255)'})
signature_deconvolution.update_traces({'colorbar': dict(title="log2(RPM+1)", orientation="h", xpad = 10)}, selector={"type": "heatmap"})
#.update_traces(dict(showscale=False, coloraxis=None), selector={"type": "heatmap"})
#%%
################################################################################################################################################################
# BLOODCOUNT CORRELATION DATA
#%%
# calculate Pearson correlation
def calc_correlation(X:pd.DataFrame, y:pd.Series, corr_fn:callable=pearsonr, get_p:bool=False):
correlations=[ corr_fn(X.iloc[:,i], y) for i in range(X.shape[1]) ]
r,p = zip(*correlations)
correlation = pd.Series(p if get_p else r, index=X.columns)
return correlation
def rank_by_correlation(X:pd.DataFrame, y:pd.Series, corr_fn:callable=pearsonr, rank_by_p:bool=False):
correlations=[ corr_fn(X.iloc[:,i], y) for i in range(X.shape[1]) ]
r,p = zip(*correlations)
ranking = pd.Series(p if rank_by_p else r, index=X.columns).sort_values(ascending=True if rank_by_p else False)
return ranking
#%%
corr_df = pd.DataFrame()
# calculate RPM from raw counts
MS_extra_ad.X = (MS_extra_ad.raw.X.T / MS_extra_ad.raw.X.T.sum(axis=0) * 1e6).T
# correlate against 'Whole Blood' expression
MS_extra_ad_subs = MS_extra_ad[MS_extra_ad.obs.mirsort_group.isin(['Whole Blood']),:]
for celltype in cell_counts:
corr_df[celltype] = calc_correlation(MS_extra_ad_subs[MS_extra_ad_subs.obs[celltype].dropna().index,:].to_df(),MS_extra_ad_subs.obs[celltype].dropna())
corr_df
#%%
# write to file
corr_df.to_csv(corr_path)
#%%
# plot blood count correlation heatmap for signature sRNAs
if make_plots:
signature_correlation = px.imshow(corr_df.loc[sRNAs_OI,:])#, color_continuous_scale='bluered', zmin=-0.3, zmax=0.3)
signature_correlation
#%%
# plot ranked correlation for defined small RNA name
pearson_coef = corr_df.loc[name_OI,:].sort_values(ascending=False)
if make_plots:
single_correlation = px.bar(pearson_coef, x=pearson_coef.index, y=pearson_coef.values, color=pearson_coef.index, color_discrete_map=cellcounts_lut).update_layout(yaxis_title="Pearson Coefficient", showlegend=False) # title='Blood count correlation', title_x=0.5,
single_correlation
#%%