1 Load environment

Code

import warnings

warnings.filterwarnings("ignore")

import matplotlib.pyplot as plt
import seaborn as sns
import scanpy as sc
import pandas as pd
import numpy as np
import random
import itertools

from tqdm import tqdm

import decoupler as dc
import sys

sys.setrecursionlimit(20000)

sys.path.append("./../../../../utilities_folder")
from utilities import load_object, intTable, plotGenesInTerm, getAnnGenes, run_ora_catchErrors

Set R environment with rpy2:

Code

import rpy2.rinterface_lib.callbacks
import anndata2ri
import logging

from rpy2.robjects import pandas2ri
import rpy2.robjects as ro

sc.settings.verbosity = 0
rpy2.rinterface_lib.callbacks.logger.setLevel(logging.ERROR)

pandas2ri.activate()
anndata2ri.activate()

%load_ext rpy2.ipython

Set up of graphical parameters for Python plots:

Code

%matplotlib inline
sc.set_figure_params(dpi = 300, fontsize = 20)

plt.rcParams['svg.fonttype'] = 'none'

cmap_up = sns.light_palette("red", as_cmap=True)
cmap_down = sns.light_palette("blue", as_cmap=True)
cmap_all = sns.light_palette("seagreen", as_cmap=True)

Set up of graphical parameters for R plots:

Code

default_units = 'in' 
default_res = 300
default_width = 10
default_height = 9

import rpy2
old_setup_graphics = rpy2.ipython.rmagic.RMagics.setup_graphics

def new_setup_graphics(self, args):
    if getattr(args, 'units') is not None:
        if args.units != default_units:
            return old_setup_graphics(self, args)
    args.units = default_units
    if getattr(args, 'res') is None:
        args.res = default_res
    if getattr(args, 'width') is None:
        args.width = default_width
    if getattr(args, 'height') is None:
        args.height = default_height        
    return old_setup_graphics(self, args)


rpy2.ipython.rmagic.RMagics.setup_graphics = new_setup_graphics

Here the cell were we inject the parameters using Quarto renderer:

Code

# Injected Parameters
N = 3

Code

# Injected Parameters
N = 6

Import R libraries:

Code

%%R
source('./../../../../utilities_folder/GO_helper.r')
loc <- './../../../../R_loc' # pointing to the renv environment

.libPaths(loc)

library('topGO')
library('org.Hs.eg.db')
library(dplyr)
library(ggplot2)

Set output folders:

Code

output_folder = './'
folder = './tables/cluster_' + str(N) + '/'

2 Load data

Here we load the dataframe:

Code

markers = pd.read_excel(folder + 'genes_in_cluster_' + str(N) + '.xlsx', index_col = 0)
markers

	logFC.celltypes_leveledhEGCLC	logFC.celltypes_leveledhPGCLC	logFC.celltypes_levelediMeLC	logCPM	LR	PValue	FDR	clusters
ANGPTL4	0	16.728461	0	6.635963	4604.226070	0.000000e+00	0.000000e+00	6
ATP6V1C2	0	14.665205	0	4.343721	1868.060935	0.000000e+00	0.000000e+00	6
BAHCC1	0	15.059887	0	4.783488	2303.220998	0.000000e+00	0.000000e+00	6
CAPN13	0	15.246439	0	4.995978	2516.211782	0.000000e+00	0.000000e+00	6
CD38	0	14.833831	0	4.546918	2059.593173	0.000000e+00	0.000000e+00	6
...	...	...	...	...	...	...	...	...
HR	0	11.179795	0	-0.177908	453.402489	5.970864e-98	7.388483e-97	6
LPAR5	0	11.209256	0	-0.211692	452.402199	9.834900e-98	1.211995e-96	6
LIPT2	0	11.093360	0	-0.177148	445.954774	2.453055e-96	2.953262e-95	6
KCNMB1	0	11.107866	0	-0.180260	444.742688	4.490762e-96	5.379503e-95	6
NANOS1	0	11.091472	0	-0.267174	438.980631	7.956386e-95	9.298978e-94	6

372 rows × 8 columns

Code

allGenes_series = pd.read_csv('./tables/all_bkg_genes.csv')
allGenes = allGenes_series['0'].tolist()

Here we load the dictionary that associates to each GO term its genes:

Code

GO2gene = load_object('./../../../../data/GO2gene_complete.pickle')

3 Markers of cluster

We filter genes for the cluster under investigation based on the p-value adjusted that we then convert in -log(p-value adjusted):

Code

markers = markers[markers.FDR < 0.01]
markers['-log10(FDR)'] = -np.log10(markers.FDR)
markers = markers.replace(np.inf, markers[markers['-log10(FDR)'] != np.inf]['-log10(FDR)'].max())
markers

	logFC.celltypes_leveledhEGCLC	logFC.celltypes_leveledhPGCLC	logFC.celltypes_levelediMeLC	logCPM	LR	PValue	FDR	clusters	-log10(FDR)
ANGPTL4	0	16.728461	0	6.635963	4604.226070	0.000000e+00	0.000000e+00	6	319.310712
ATP6V1C2	0	14.665205	0	4.343721	1868.060935	0.000000e+00	0.000000e+00	6	319.310712
BAHCC1	0	15.059887	0	4.783488	2303.220998	0.000000e+00	0.000000e+00	6	319.310712
CAPN13	0	15.246439	0	4.995978	2516.211782	0.000000e+00	0.000000e+00	6	319.310712
CD38	0	14.833831	0	4.546918	2059.593173	0.000000e+00	0.000000e+00	6	319.310712
...	...	...	...	...	...	...	...	...	...
HR	0	11.179795	0	-0.177908	453.402489	5.970864e-98	7.388483e-97	6	96.131445
LPAR5	0	11.209256	0	-0.211692	452.402199	9.834900e-98	1.211995e-96	6	95.916499
LIPT2	0	11.093360	0	-0.177148	445.954774	2.453055e-96	2.953262e-95	6	94.529698
KCNMB1	0	11.107866	0	-0.180260	444.742688	4.490762e-96	5.379503e-95	6	94.269258
NANOS1	0	11.091472	0	-0.267174	438.980631	7.956386e-95	9.298978e-94	6	93.031565

372 rows × 9 columns

3.0.1 All regulated

Code

all_sign = markers.index.tolist()
allSelected = allGenes_series['0'].isin(all_sign).astype('int').tolist()

4 topGO

4.1 All significant

Code

%%R -i allSelected -i allGenes

allGenes_v <- c(allSelected)
#print(allGenes_v)
names(allGenes_v) <- allGenes
allGenes_v <- unlist(allGenes_v)

geneNames <- c(allGenes)

ann_org_BP <- topGO::annFUN.org(whichOnto='BP', feasibleGenes=names(allGenes_v), 
                           mapping='org.Hs.eg', ID='symbol')

ann_org_MF <- topGO::annFUN.org(whichOnto='MF', feasibleGenes=names(allGenes_v), 
                           mapping='org.Hs.eg', ID='symbol')

ann_org_CC <- topGO::annFUN.org(whichOnto='CC', feasibleGenes=names(allGenes_v), 
                           mapping='org.Hs.eg', ID='symbol')

selection <- function(allScores){return (as.logical(allScores))}

Biological Process

Code

%%R
#print(lapply(ann_org_BP, count_genes))

GOdata <- new("topGOdata",
  ontology="BP",
  allGenes=allGenes_v,
  annot=annFUN.GO2genes,
  GO2genes=ann_org_BP,
  geneSel = selection,
  nodeSize=10)

Code

%%R -o results

results <- runTest(GOdata, algorithm="weight01",statistic="fisher")

Code

scores = ro.r.score(results)
score_names = ro.r(
'''
names(results@score)
'''
)
go_data = ro.r.GOdata

genesData = ro.r(
'''
geneData(results)
'''
)
genesData

array([10903,   320,    10,  3782], dtype=int32)

Code

#num_summarize = min(100, len(score_names))
results_table = ro.r.GenTable(go_data, weight=results,
        orderBy="weight", topNodes=len(scores))

Code

results_table_py = ro.conversion.rpy2py(results_table)

Code

scores_py = ro.conversion.rpy2py(scores)
score_names = [i for i in score_names]

Code

scores_df = pd.DataFrame({'Scores': scores_py, 'GO.ID': score_names})
results_table_py = results_table_py.merge(scores_df, left_on = 'GO.ID', right_on = 'GO.ID')
results_table_py

	GO.ID	Term	Annotated	Significant	Expected	weight	Scores
0	GO:0001709	cell fate determination	19	7	0.56	6.5e-07	6.535443e-07
1	GO:0009755	hormone-mediated signaling pathway	131	8	3.84	1.3e-05	1.273941e-05
2	GO:0045944	positive regulation of transcription by ...	873	47	25.62	3.9e-05	3.913958e-05
3	GO:0008544	epidermis development	192	15	5.64	1.0e-04	9.953731e-05
4	GO:0051048	negative regulation of secretion	95	11	2.79	0.00013	1.257691e-04
...	...	...	...	...	...	...	...
5697	GO:2001251	negative regulation of chromosome organi...	88	0	2.58	1.00000	1.000000e+00
5698	GO:2001252	positive regulation of chromosome organi...	93	0	2.73	1.00000	1.000000e+00
5699	GO:2001256	regulation of store-operated calcium ent...	11	0	0.32	1.00000	1.000000e+00
5700	GO:2001258	negative regulation of cation channel ac...	23	0	0.68	1.00000	1.000000e+00
5701	GO:2001267	regulation of cysteine-type endopeptidas...	13	0	0.38	1.00000	1.000000e+00

5702 rows × 7 columns

Code

results_table_py = results_table_py[results_table_py['Scores'] < 0.05]
results_table_py = results_table_py[results_table_py['Annotated'] < 200]
results_table_py = results_table_py[results_table_py['Annotated'] > 15]

Code

results_table_py['-log10(pvalue)'] = - np.log10(results_table_py.Scores)
results_table_py['Significant/Annotated'] = results_table_py['Significant'] / results_table_py['Annotated']

Code

intTable(results_table_py, folder = folder, fileName = 'GO_BP_all.xlsx', save = True)

Code

%%R -i folder
Res <- GenTable(GOdata, weight=results,
        orderBy="weight", topNodes=length(score(results)))
#print(Res[0:10,])
colnames(Res) <- c("GO.ID", "Term", "Annotated", "Significant", "Expected", "Statistics")
Res$ER <- Res$Significant / Res$Expected

image = bubbleplot(Res, Ont = 'BP', fillCol = 'forestgreen')
ggsave(file=paste0(folder, "TopGO_results_BP.pdf"), plot=image, width=12, height=4)

bubbleplot(Res, Ont = 'BP', fillCol = 'forestgreen')

Code

%%R -i markers
image = plotGenesInTerm_v1(Res, GOdata, SE = markers, nterms=15, ngenes=12,
                             fillCol='forestgreen', log = TRUE)

ggsave(file=paste0(folder, "Genes_in_Term_results_BP.pdf"), plot=image, width=12, height=4)

plotGenesInTerm_v1(Res, GOdata, SE = markers, nterms=15, ngenes=12,
                             fillCol='forestgreen', log = TRUE)

Code

%%R -i markers -i folder
saveGenesInTerm(Res, GOdata, nterms = 20, path = paste0(folder,'GO_BP_genesInTerm_all.xlsx'), SE = markers)

Molecular Function

Code

%%R

GOdata <- new("topGOdata",
  ontology="MF",
  allGenes=allGenes_v,
  annot=annFUN.GO2genes,
  GO2genes=ann_org_MF,
  geneSel = selection,
  nodeSize=10)

Code

%%R -o results

results <- runTest(GOdata, algorithm="weight01",statistic="fisher")

Code

scores = ro.r.score(results)
score_names = ro.r(
'''
names(results@score)
'''
)
go_data = ro.r.GOdata

genesData = ro.r(
'''
geneData(results)
'''
)
genesData

array([11203,   329,    10,   529], dtype=int32)

Code

#num_summarize = min(100, len(score_names))
results_table = ro.r.GenTable(go_data, weight=results,
        orderBy="weight", topNodes=len(scores))

Code

results_table_py = ro.conversion.rpy2py(results_table)

Code

scores_py = ro.conversion.rpy2py(scores)
score_names = [i for i in score_names]

Code

scores_df = pd.DataFrame({'Scores': scores_py, 'GO.ID': score_names})
results_table_py = results_table_py.merge(scores_df, left_on = 'GO.ID', right_on = 'GO.ID')
results_table_py = results_table_py[results_table_py['Scores'] < 0.05]
results_table_py = results_table_py[results_table_py['Annotated'] < 200]
results_table_py = results_table_py[results_table_py['Annotated'] > 15]

intTable(results_table_py, folder = folder, fileName = 'GO_MF_all.xlsx', save = True)

Code

%%R
Res <- GenTable(GOdata, weight=results,
        orderBy="weight", topNodes=length(score(results)))
#print(Res[0:10,])
colnames(Res) <- c("GO.ID", "Term", "Annotated", "Significant", "Expected", "Statistics")
Res$ER <- Res$Significant / Res$Expected

image = bubbleplot(Res, Ont = 'MF', fillCol = 'forestgreen')

ggsave(file=paste0(folder, "TopGO_results_MF.pdf"), plot=image, width=12, height=4)

bubbleplot(Res, Ont = 'MF', fillCol = 'forestgreen')

Code

%%R -i markers
image = plotGenesInTerm_v1(Res, GOdata, SE = markers, nterms=15, ngenes=12,
                             fillCol='forestgreen', log = TRUE)

ggsave(file=paste0(folder, "Genes_in_Term_results_MF.pdf"), plot=image, width=12, height=4)

plotGenesInTerm_v1(Res, GOdata, SE = markers, nterms=15, ngenes=12,
                             fillCol='forestgreen', log = TRUE)

Code

%%R -i markers -i folder
saveGenesInTerm(Res, GOdata, nterms = 20, path = paste0(folder,'GO_MF_genesInTerm_all.xlsx'), SE = markers)

Cellular Compartment

Code

%%R

GOdata <- new("topGOdata",
  ontology="CC",
  allGenes=allGenes_v,
  annot=annFUN.GO2genes,
  GO2genes=ann_org_CC,
  geneSel = selection,
  nodeSize=10)

Code

%%R -o results

results <- runTest(GOdata, algorithm="weight01",statistic="fisher")

Code

scores = ro.r.score(results)
score_names = ro.r(
'''
names(results@score)
'''
)
go_data = ro.r.GOdata

genesData = ro.r(
'''
geneData(results)
'''
)
genesData

array([11323,   338,    10,   419], dtype=int32)

Code

#num_summarize = min(100, len(score_names))
results_table = ro.r.GenTable(go_data, weight=results,
        orderBy="weight", topNodes=len(scores))

Code

results_table_py = ro.conversion.rpy2py(results_table)

Code

scores_py = ro.conversion.rpy2py(scores)
score_names = [i for i in score_names]

Code

scores_df = pd.DataFrame({'Scores': scores_py, 'GO.ID': score_names})
results_table_py = results_table_py.merge(scores_df, left_on = 'GO.ID', right_on = 'GO.ID')

Code

results_table_py = results_table_py[results_table_py['Scores'] < 0.05]
results_table_py = results_table_py[results_table_py['Annotated'] < 200]
results_table_py = results_table_py[results_table_py['Annotated'] > 15]

intTable(results_table_py, folder = folder, fileName = 'GO_CC_all.xlsx', save = True)

Code

%%R
Res <- GenTable(GOdata, weight=results,
        orderBy="weight", topNodes=length(score(results)))
#print(Res[0:10,])
colnames(Res) <- c("GO.ID", "Term", "Annotated", "Significant", "Expected", "Statistics")
Res$ER <- Res$Significant / Res$Expected
image = bubbleplot(Res, Ont = 'CC', fillCol = 'forestgreen')

ggsave(file=paste0(folder, "TopGO_results_CC.pdf"), plot=image, width=12, height=4)

bubbleplot(Res, Ont = 'CC', fillCol = 'forestgreen')

Code

%%R -i markers
image = plotGenesInTerm_v1(Res, GOdata, SE = markers, nterms=12, ngenes=12,
                             fillCol='forestgreen', log = TRUE)

ggsave(file=paste0(folder, "Genes:_in_Term_results_CC.pdf"), plot=image, width=12, height=4)

plotGenesInTerm_v1(Res, GOdata, SE = markers, nterms=12, ngenes=12,
                             fillCol='forestgreen', log = TRUE)

Code

%%R -i markers -i folder
saveGenesInTerm(Res, GOdata, nterms = 20, path = paste0(folder,'GO_CC_genesInTerm_all.xlsx'), SE = markers)

Pathway annotation

4.1.0.1 Reactome

Code

curated = msigdb[msigdb['collection'].isin(['reactome_pathways'])]
curated = curated[~curated.duplicated(['geneset', 'genesymbol'])]

aggregated = curated[["geneset", "genesymbol"]].groupby("geneset").count().rename(columns={"genesymbol": "gene_count"})
curated = curated[~curated.geneset.isin(aggregated[aggregated.gene_count > 200].index.tolist())].copy()
curated = curated[~curated.geneset.isin(aggregated[aggregated.gene_count < 15].index.tolist())].copy()

Code

rank = pd.DataFrame(markers['-log10(FDR)'])

rank_copy = rank.copy()
rank_copy['pval'] = markers.loc[rank.index].FDR

Code

rank_copy

	-log10(FDR)	pval
ANGPTL4	319.310712	0.000000e+00
ATP6V1C2	319.310712	0.000000e+00
BAHCC1	319.310712	0.000000e+00
CAPN13	319.310712	0.000000e+00
CD38	319.310712	0.000000e+00
...	...	...
HR	96.131445	7.388483e-97
LPAR5	95.916499	1.211995e-96
LIPT2	94.529698	2.953262e-95
KCNMB1	94.269258	5.379503e-95
NANOS1	93.031565	9.298978e-94

372 rows × 2 columns

Code

results_table_py = run_ora_catchErrors(mat=rank.T, net=curated, source='geneset', target='genesymbol', verbose=False, n_up=len(rank), n_bottom=0)
len(results_table_py)

13

Code

intTable(results_table_py, folder = folder, fileName = 'Reactome_all.xlsx', save = True)

Code

if len(results_table_py) > 0:
    results_table_py = getAnnGenes(results_table_py, GO2gene['reactome_pathways'], rank_copy)
    _, df = plotGenesInTerm(results = results_table_py, GO2gene = GO2gene['reactome_pathways'], DEGs = rank_copy, n_top_terms = 10, cmap = cmap_all)

Code

if len(results_table_py) > 0:
    intTable(df, folder = folder, fileName = 'genesInTerm_Reactome_all.xlsx', save = True)

4.1.0.2 KEGG

Code

curated = msigdb[msigdb['collection'].isin(['kegg_pathways'])]
curated = curated[~curated.duplicated(['geneset', 'genesymbol'])]

aggregated = curated[["geneset", "genesymbol"]].groupby("geneset").count().rename(columns={"genesymbol": "gene_count"})
curated = curated[~curated.geneset.isin(aggregated[aggregated.gene_count > 200].index.tolist())].copy()
curated = curated[~curated.geneset.isin(aggregated[aggregated.gene_count < 15].index.tolist())].copy()

Code

results_table_py = run_ora_catchErrors(mat=rank.T, net=curated, source='geneset', target='genesymbol', verbose=False, n_up=len(rank), n_bottom=0)

Code

intTable(results_table_py, folder = folder, fileName = 'KEGG_all.xlsx', save = True)

Code

if len(results_table_py) > 0:
    results_table_py = getAnnGenes(results_table_py, GO2gene['kegg_pathways'], rank_copy)
    _, df = plotGenesInTerm(results_table_py, GO2gene['kegg_pathways'], rank_copy, n_top_terms = 10, n_top_genes = 15, cmap = cmap_all)

Code

if len(results_table_py) > 0:
    intTable(df, folder = folder, fileName = 'genesInTerm_KEGG_all.xlsx', save = True)