compare.R
In unistbig/CellEnrich: Pathway Enrichment Analysis, Visualize for Single Cell Data
library(ggplot2)
library(dplyr)
# hyper
getHyperPvalue <- function(genes, genesets) {
A <- length(unique(unlist(genesets)))
pv <- rep(0, length(genesets))
for (i in 1:length(genesets)) {
q <- length(intersect(genesets[[i]], genes))
m <- length(genesets[[i]])
n <- A - m
k <- length(genes)
pv[i] <- 1 - phyper(q - 1, m, n, k)
}
names(pv) <- names(genesets)
res <- data.frame(name = names(pv), qv = p.adjust(pv, 'fdr'))
if(nrow(res %>% filter(qv <= 0.05)) > 10){
res <- res %>% filter(qv <= 0.05) %>% arrange(qv)
write.table(res, file='tmp.csv', sep=',', append = FALSE, row.names = FALSE, col.names = FALSE)
return()
}
else{
res <- res %>% arrange(qv)
write.table(res[1:10,], file='tmp.csv', sep = ',', append = FALSE, row.names = FALSE, col.names = FALSE)
}
}
########################### pbmc
# B
genes <- c('TYROBP','S100A9','S100A8','CST3','LYZ',
'FCER1G','FTL','TYMP','FTH1','HLA-DRA',
'GSTP1','SAT1','CTSS','GPX1','CFD',
'CD14','SERPINA1','CDA','VIM','APOBEC3A',
'FOLR3','NCF1','RPL29','RPL10','CD99',
'RPS24','RPS4Y1')
# CD14+MONO
genes <- c('CCL5','GZMA','IL32','CD3D','CTSW',
'CD3E','RPS27A','HCST','LCK','PRF1',
'B2M','HLA-C','CD8A','RPL21','HLA-A',
'CCL4','CD7','GZMB','RPS3A','CD99',
'RPS27','RPS12','NCR3','MATK','RPL13',
'RPS25','RPS3','RPL3','RPL32','RPS2',
'RPL14','RPL7','RPS24')
# CD8 T
genes <- c('FCGR3A','CD68','SPI1','CFD','FCER1G',
'IFI30','SERPINA1','TYROBP','TYMP','CST3',
'HLA-DPA1','CTSS','NPC2','SAT1','TIMP1',
'HES4','CEBPB','FTH1','FTL','CDKN1C',
'TMSB4X','RPS19','CD79B','ADA','B2M',
'VIM','RPL21','RPL10','RPL14','RPS24',
'RPL7','RPS4Y1')
# DC
genes <- c('GZMB','PRF1','GZMA','FCGR3A','CTSW',
'CCL4','TYROBP','FCER1G','CD247','CD7',
'XCL2','KLRD1','AKR1C3','CCL5','CD99',
'RPS27A','RPS3A','RPS24')
# FCGR3A+ Mono
genes <- c('CD3D','RPS27A','RPS27','LTB','RPL9',
'RPL21','RPS3A','IL32','LDHB','RPS25',
'CD3E','RPL31','RPL3','RPL30','RPS12',
'RPS3','RPS23','CD7','IL7R','RPLP2',
'RPL32','RPL13','RPS18','LCK','RPL18A',
'RPL11','CCR7','RPS14','RPS28','TMSB4X',
'CD27','RPL10','CD3G','LEF1','SELL',
'CD8B','FHIT','ACTN1','PFN2','NOG')
# Memory CD4 T
genes <- c('CD79A','HLA-DRB1','HLA-DQA1','HLA-DRA','HLA-DPB1',
'HLA-DPA1','HLA-DQB1','CD79B','MS4A1','CD74',
'LTB','TCL1A','CD37','CXCR4','FCER2',
'RPS27A','RPL21','RPL9','RPS3A','RPL30',
'BIRC3','RASGRP2')
# Naive CD4 T
genes <- c('IL32','LTB','CD3D','RPS27A','RPS27',
'CD3E','LDHB','RPSA','RPL9','RPL21',
'RPS25','RPS3A','IL7R','TMSB4X','RPS3',
'CD2','RPL3','RPL31','RPL30','RPS12',
'B2M','LCK','VIM','RPLP2','HLA-A',
'RPL32','CD7','RPL13','RPL11','RPL10',
'RPLP1','HLA-C','ACTB','CD3G','FTH1',
'RPS4Y1')
# NK
genes <- c('HLA-DQA1','IFI30','HLA-DQA2','HLA-DQB1','HLA-DRB5',
'FCER1A','CD33','SPI1','HLA-DMA','SERPINF1',
'HLA-DMB','CTSH','PLD4','CD1C','ALDH2',
'KLF4','FCER1G','CFP','TYROBP','CST3',
'SAMHD1','HLA-DPA1','HLA-DPB1','PPP1R14A','HLA-DRA',
'BLNK','CD74','CD72','VIM','RPL5',
'RPL10','RPL7A','RPL22','RPS24')
# Platelet
genes <- c('GP9','GNG11','PF4','ITGA2B','PTCRA',
'PPBP','CA2','CLU','SNCA','MYL9',
'CD9','GP1BA','F13A1','TUBB1','RUFY1',
'CCL5')
# compareplot
# CellEnrich
# Fisher
# intersect CellEnrich <-> Fisher
# scMerge
# intersect scMerge <-> Fisher
# pbmc 0.3 median 2
int <- data.frame(
val = c(
24, 5, 2,
29, 15, 6,
6, 4, 1,
38, 7, 4,
35, 7, 0,
8, 25, 1,
1, 10, 1,
13, 25, 3,
9, 5, 1
),
method = rep(c('CellEnrich', 'Fisher','Overlap')),
group = rep(c('B','CD14+ Mono','CD8 T','DC','FCGR3A+ Mono','Memory CD4 T','Naive CD4 T','NK','Platelet'), each = 3)
)
ggplot(int, aes(x = group, y= val, fill = method)) +
geom_bar(stat = 'identity', position = 'dodge') +
xlab('Group') +
ylab('Number of Pathway') +
ggtitle(label = 'pbmc OddRatio 0.3 median')
# pbmc 0.1 median
int <- data.frame(
val = c(
26, 5, 3,
41, 15, 7,
22, 4, 1,
48, 7, 4,
48, 7, 1,
9, 25, 1,
1, 10, 1,
31, 25, 7,
25, 5, 1
),
method = rep(c('CellEnrich', 'Fisher','Overlap')),
group = rep(c('B','CD14+ Mono','CD8 T','DC','FCGR3A+ Mono','Memory CD4 T','Naive CD4 T','NK','Platelet'), each = 3)
)
# pbmc 0.1 median 2
int <- data.frame(
val = c(
26, 5, 2,
42, 15, 7,
18, 4, 1,
47, 7, 4,
48, 7, 0,
11, 25, 1,
1, 10, 1,
23, 25, 3,
26, 5, 1
),
method = rep(c('CellEnrich', 'Fisher','Overlap')),
group = rep(c('B','CD14+ Mono','CD8 T','DC','FCGR3A+ Mono','Memory CD4 T','Naive CD4 T','NK','Platelet'), each = 3)
)
ggplot(int, aes(x = group, y= val, fill = method)) +
geom_bar(stat = 'identity', position = 'dodge') +
xlab('Group') +
ylab('Number of Pathway') +
ggtitle(label = 'pbmc OddRatio 0.1 median')
# pancreas
# acinar
genes <- c('MRC2','PDGFRB','GNG11','COL18A1','ITGA11',
'LTBP1','FMOD','COL12A1','SNAI2','SFRP2',
'LAMA2','COL6A2','COL5A1','THBS2','COL5A3',
'EDNRA','ZEB2','LUM','COL4A1','TPM2',
'VCAN','FBN1','COL5A2','COL4A2','COL15A1',
'ANTXR1','VIM','COL3A1','COL6A3','COL6A1',
'COL1A2','FN1','CREB3L1','UNC5B','LAMB1',
'COL1A1','CALD1','MSN','PSAT1')
# activated_stellate
genes <- c('ELOVL2','COL22A1','NPY1R','BHMT','BMP4',
'MYO1A','PDE2A','CALCR','CALY','VTN',
'AKR1C4','COL2A1','GHRL','ASGR1','OPRK1',
'FGF14','HMGCS2','HRH2','SUCNR1','NKX2-2',
'UGT2B4','L1CAM','GLDC','F10','CPLX2',
'DEFB1','APOH','ADRB1','PPP2R2B','KIF5C',
'SNAP25','HNF4A','CALD1','ENPP2','CDKN2A',
'FOXA2','SERPINA1','RASD1','ANK3','ACSL1',
'CLDN7','PAPSS2','SOX4')
# alpha
genes <- c('GCG','PPP1R1A','TTR','CPE','GNG4',
'MAFB','PAX6','PTPRN','NEUROD1','CNTN1',
'ENPP2','ISL1','KCNMB2','PDE3B','PTPRN2',
'GRIA3','PGR','F10','SSTR2','FEV',
'ALDH1A1','MYO10','SH3GL2','PLCE1','CAMK2B',
'CD99L2','FABP5','GLS','SSX2IP','DPP4',
'PAPSS2','SDC2','TUSC3','LDHA','PFN2',
'VIM','CLU','LAPTM4B','CKB','FOXA2',
'CLDN7')
# beta
genes <- c('HHEX','GAD2','ISL1','NEUROD1','SST',
'PARVB','PTPRN','RFX6','CAMK2B','ABCC8',
'SNAP25','CASR','UCHL1','KCNJ8','LEPR',
'CPE','GABRB3','SSTR1','CADM1','GHSR',
'HADH','EDN3','UNC5B','PPP1R1A','MAFB',
'CD9','PDX1','TJP2','PCLO','BAMBI',
'SEC11C','BHLHE41','SCD5','RGS2','SERPINA1',
'SLC38A1','AQP3','CALD1','PFN2','FOXA2',
'MARCKS')
# delta
genes <- c('ABCC8','INS','UCHL1','ADCYAP1','GAD2',
'HADH','MAFA','PDX1','IAPP','PAX6',
'PTPRN','NEUROD1','G6PC2','CASR','MAFB',
'ATP2A3','GNG4','SCD5','CPE','KIF5C',
'SH3GL2','PPP1R1A','PFKFB2','RASD1','ROBO2',
'GSN','RASGRF1','PFN2','VEGFA','SIPA1L2',
'ALCAM','RRAGD','FOXA2','CLDN7')
# ductal
genes <- c('CFTR','KRT19','SDC4','SERPING1','ZFP36L1',
'CLDN1','SLC4A4','DEFB1','LITAF','SOX4',
'CD9','CFB','COL18A1','SPP1','CAV2',
'SERPINA5','CTSH','CD24','LCN2','MMP7',
'AQP1','ALDH1A3','BACE2','VTCN1','CD44',
'KRT18','CXADR','DAB2','ENAH','TMSB4X',
'CLDN4','ANXA2','LAMC1','ADCY5','JUP',
'CLDN7','VAMP8','CDH1','ATP1B1','PERP'
)
# endothelial
genes <- c('KDR','ERG','S1PR1','GNG11','CDH5',
'ITGA5','MEF2C','RHOJ','CLEC2B','ESAM',
'SNAI1','RAMP2','ACVRL1','DLL4','A2M',
'CXCR4','PDE2A','COL4A1','PODXL','ARHGDIB',
'SHANK3','CALCRL','COL4A2','PTPRB','RAPGEF4',
'FLT1','VIM','EFNA1','ID3','MSN',
'WWTR1','PLXNA2')
# epsilon
genes <- c('ITGB3','GLI2','EDNRB','PDGFRB','SNAI1',
'GYPC','FABP4','CAV1','TRPV2','IFI16',
'EDNRA','A2M','MRC2','HEYL','TBX3',
'ENPEP','BST2','COLEC11','CD4','ITGA4',
'COL6A2','ZEB2','AVPR1A','COL5A1','LAMA4',
'WNT5A','COL4A1','ESAM','NOTCH2','COL5A2',
'ARHGDIB','TRPC6','COL4A2','COX4I2','COL15A1',
'ITGA1','SLC6A12','SLIT3','VIM','COL3A1',
'COL1A2','CALD1','CA2','COL1A1','CHSY1',
'COX7A1','UNC5B','ADCY4','SDC2')
# gamma
genes <- c('GSTA1','GSTA2','ANPEP','IL32','PRSS3',
'CTRB2','CPA2','AKR1C3','CTRB1','KLK1',
'PRSS1','BCAT1','PLA2G1B','GATM','CPA1',
'CPB1','CEL','SDC4','PNLIP','MGST1',
'LDHB','CD24','GDF15','CD44','KRT18',
'CLDN4','CLDN7','RAB11FIP1')
# macrophage
genes <- c('PTGS1','BTK','HPGDS','KIT','CPA3',
'NCF4','LCP2','RUNX3','BATF','CSF2RB',
'LAPTM5','TPSB2','GRAP2','PRKCB','TPSAB1',
'ALOX5AP','RAB27B','HCLS1','ARHGDIB','PTPRC',
'SLC45A3','CD37','IL4R','RAC2','ABCC4',
'VIM','SLC18A2','GMPR')
# mast
genes <- c('ITGB2','C1QC','RUNX3','CD86','CCR1',
'HCK','C1QB','LCP2','PIK3R5','BCL2A1',
'C1QA','CSF1R','FPR3','DOCK2','LAPTM5',
'VSIG4','TYROBP','HCLS1','FCGR2A','PTPRC',
'ACP5','SDS','SIRPA','ALOX5AP','HLA-DRA',
'TNFRSF1B','WIPF1','VIM','CD74','LITAF')
# quiescent_stellate
genes <- c('WNT16','PTPRZ1','EDNRB','UCN2','GNG11',
'ITGB3','IFI16','SOX2','HEPH','ZEB2',
'NGFR','PLA2G4A','SLIT2','COL9A3','A2M',
'MEF2C','MMP14','NLGN4X','ABCB4','RUNX2',
'NRXN3','CRYAB','NRXN1','COL4A2','ASPA',
'SEMA3C','TIMP3','ITGA6','TUBB6','WIPF1',
'LPCAT2','BVES','PLAUR','AP1S2','WWTR1',
'VIM','PPP2R2B','CALD1','ITGB8','CD9',
'ETS1','ITPK1','DAG1','MPDZ')
# schwann
genes <- c('ISL1','CXXC4','GAD2','KCNMB2','PPY',
'NEUROD1','ABCC8','PAX6','ETV1','CNTN1',
'PGR','PTPRN','CAMK2B','TSPAN7','GRIA3',
'UCHL1','TTR','GNG4','CARD11','FEV',
'CPE','AMOTL1','ID4','NPNT','ENTPD2',
'PCLO','S100A10','SEMA3E','SLC6A4','CHRM3',
'INPP5F','PRKACB','ID2','PTPRN2','ABCB1',
'TUBB2A','SLC40A1','GCNT3','AQP3','ALDH1A1',
'TUSC3','FGFR1','BHLHE41','CLU','RAP1GAP',
'FOXA2','MARCKS')
getHyperPvalue(genes, genesets)
# compareplot
# cellenrich
# fisher
# intersect
# pancreas 0.3 mean
int <- data.frame(
val = c(
46, 10, 0,
76, 1, 0,
25, 4, 0,
17, 3, 1,
21, 5, 0,
70, 6, 1,
59, 3, 1,
30, 11, 0,
52, 10, 0,
69, 1, 1,
26, 10, 3,
78, 8, 2,
52, 7, 0
),
method = rep(c('CellEnrich','Fisher','Overlap')),
group = rep(c('acinar', 'activated_stellate', 'alpha', 'beta', 'delta', 'ductal', 'endothelial', 'epsilon', 'gamma', 'macrophage', 'mast', 'quiescent_stellate','schwann'),each = 3)
)
# pancreas 0.3 mean - 2
int <- data.frame(
val = c(
36, 10, 1,
63, 1, 0,
22, 4, 0,
27, 3, 1,
1, 5, 0,
53, 6, 0,
71, 3, 2,
30, 11, 0,
36, 10, 0,
78, 1, 1,
26, 10, 2,
71, 8, 2,
46, 7, 0
),
method = rep(c('CellEnrich','Fisher','Overlap')),
group = rep(c('acinar', 'activated_stellate', 'alpha', 'beta', 'delta', 'ductal', 'endothelial', 'epsilon', 'gamma', 'macrophage', 'mast', 'quiescent_stellate','schwann'),each = 3)
)
ggplot(int, aes(x = group, y= val, fill = method)) +
geom_bar(stat = 'identity', position = 'dodge') +
xlab('Group') +
ylab('Number of Pathway') +
ggtitle(label = 'pancreas OddRatio 0.3 mean')
# pancreas 0.3 median
int <- data.frame(
val = c(
61, 10, 2,
92, 1, 0,
30, 4, 1,
27, 3, 1,
4, 5,0,
66, 6, 0,
79, 3, 2,
35, 11, 0,
40, 10, 0,
89, 1, 1,
29, 10, 2,
90, 8, 2,
67, 7, 0
),
method = rep(c('CellEnrich','Fisher','Overlap')),
group = rep(c('acinar', 'activated_stellate', 'alpha', 'beta', 'delta', 'ductal', 'endothelial', 'epsilon', 'gamma', 'macrophage', 'mast', 'quiescent_stellate','schwann'),each = 3)
)
# pancreas 0.3 median - 2
int <- data.frame(
val = c(
47, 10, 1,
73, 1, 0,
22, 4, 0,
25, 3, 0,
1, 5, 0,
55, 6, 0,
74, 3, 3,
27, 11, 0,
33, 10, 0,
81, 1, 1,
22, 10, 2,
79, 8, 2,
47, 7, 0
),
method = rep(c('CellEnrich','Fisher','Overlap')),
group = rep(c('acinar', 'activated_stellate', 'alpha', 'beta', 'delta', 'ductal', 'endothelial', 'epsilon', 'gamma', 'macrophage', 'mast', 'quiescent_stellate','schwann'),each = 3)
)
ggplot(int, aes(x = group, y= val, fill = method)) +
geom_bar(stat = 'identity', position = 'dodge') +
xlab('Group') +
ylab('Number of Pathway') +
ggtitle(label = 'pancreas OddRatio 0.3 median')
hsa <- keggList('hsa')
hsa <- keggLink('pathway', 'hsa')
hsa <- unname(hsa)
hsa <- sort(unique(hsa))
hsa <- sapply(hsa, function(i){gsub('path:','',i)})
hsa <- unname(hsa)
genesets <- list()
for(i in 1:length(hsa)){
res <- keggGet(hsa[i])[[1]]
genes <- unname(sapply(res$GENE[grep(';', res$GENE)], function(i){strsplit(i, '; ')[[1]][1]}))
genesets[[i]] <- genes
names(genesets)[i] <- gsub(' - Homo sapiens (human)', '', res$NAME)
}
names(genesets) <- unname(sapply(names(genesets), function(i){strsplit(i, ' - ')[[1]][1]}))
grep('Amoe',names(genesets))
unistbig/CellEnrich documentation built on July 27, 2020, 12:52 p.m.
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library(ggplot2)
library(dplyr)
# hyper
getHyperPvalue <- function(genes, genesets) {
A <- length(unique(unlist(genesets)))
pv <- rep(0, length(genesets))
for (i in 1:length(genesets)) {
q <- length(intersect(genesets[[i]], genes))
m <- length(genesets[[i]])
n <- A - m
k <- length(genes)
pv[i] <- 1 - phyper(q - 1, m, n, k)
}
names(pv) <- names(genesets)
res <- data.frame(name = names(pv), qv = p.adjust(pv, 'fdr'))
if(nrow(res %>% filter(qv <= 0.05)) > 10){
res <- res %>% filter(qv <= 0.05) %>% arrange(qv)
write.table(res, file='tmp.csv', sep=',', append = FALSE, row.names = FALSE, col.names = FALSE)
return()
}
else{
res <- res %>% arrange(qv)
write.table(res[1:10,], file='tmp.csv', sep = ',', append = FALSE, row.names = FALSE, col.names = FALSE)
}
}
########################### pbmc
# B
genes <- c('TYROBP','S100A9','S100A8','CST3','LYZ',
'FCER1G','FTL','TYMP','FTH1','HLA-DRA',
'GSTP1','SAT1','CTSS','GPX1','CFD',
'CD14','SERPINA1','CDA','VIM','APOBEC3A',
'FOLR3','NCF1','RPL29','RPL10','CD99',
'RPS24','RPS4Y1')
# CD14+MONO
genes <- c('CCL5','GZMA','IL32','CD3D','CTSW',
'CD3E','RPS27A','HCST','LCK','PRF1',
'B2M','HLA-C','CD8A','RPL21','HLA-A',
'CCL4','CD7','GZMB','RPS3A','CD99',
'RPS27','RPS12','NCR3','MATK','RPL13',
'RPS25','RPS3','RPL3','RPL32','RPS2',
'RPL14','RPL7','RPS24')
# CD8 T
genes <- c('FCGR3A','CD68','SPI1','CFD','FCER1G',
'IFI30','SERPINA1','TYROBP','TYMP','CST3',
'HLA-DPA1','CTSS','NPC2','SAT1','TIMP1',
'HES4','CEBPB','FTH1','FTL','CDKN1C',
'TMSB4X','RPS19','CD79B','ADA','B2M',
'VIM','RPL21','RPL10','RPL14','RPS24',
'RPL7','RPS4Y1')
# DC
genes <- c('GZMB','PRF1','GZMA','FCGR3A','CTSW',
'CCL4','TYROBP','FCER1G','CD247','CD7',
'XCL2','KLRD1','AKR1C3','CCL5','CD99',
'RPS27A','RPS3A','RPS24')
# FCGR3A+ Mono
genes <- c('CD3D','RPS27A','RPS27','LTB','RPL9',
'RPL21','RPS3A','IL32','LDHB','RPS25',
'CD3E','RPL31','RPL3','RPL30','RPS12',
'RPS3','RPS23','CD7','IL7R','RPLP2',
'RPL32','RPL13','RPS18','LCK','RPL18A',
'RPL11','CCR7','RPS14','RPS28','TMSB4X',
'CD27','RPL10','CD3G','LEF1','SELL',
'CD8B','FHIT','ACTN1','PFN2','NOG')
# Memory CD4 T
genes <- c('CD79A','HLA-DRB1','HLA-DQA1','HLA-DRA','HLA-DPB1',
'HLA-DPA1','HLA-DQB1','CD79B','MS4A1','CD74',
'LTB','TCL1A','CD37','CXCR4','FCER2',
'RPS27A','RPL21','RPL9','RPS3A','RPL30',
'BIRC3','RASGRP2')
# Naive CD4 T
genes <- c('IL32','LTB','CD3D','RPS27A','RPS27',
'CD3E','LDHB','RPSA','RPL9','RPL21',
'RPS25','RPS3A','IL7R','TMSB4X','RPS3',
'CD2','RPL3','RPL31','RPL30','RPS12',
'B2M','LCK','VIM','RPLP2','HLA-A',
'RPL32','CD7','RPL13','RPL11','RPL10',
'RPLP1','HLA-C','ACTB','CD3G','FTH1',
'RPS4Y1')
# NK
genes <- c('HLA-DQA1','IFI30','HLA-DQA2','HLA-DQB1','HLA-DRB5',
'FCER1A','CD33','SPI1','HLA-DMA','SERPINF1',
'HLA-DMB','CTSH','PLD4','CD1C','ALDH2',
'KLF4','FCER1G','CFP','TYROBP','CST3',
'SAMHD1','HLA-DPA1','HLA-DPB1','PPP1R14A','HLA-DRA',
'BLNK','CD74','CD72','VIM','RPL5',
'RPL10','RPL7A','RPL22','RPS24')
# Platelet
genes <- c('GP9','GNG11','PF4','ITGA2B','PTCRA',
'PPBP','CA2','CLU','SNCA','MYL9',
'CD9','GP1BA','F13A1','TUBB1','RUFY1',
'CCL5')
# compareplot
# CellEnrich
# Fisher
# intersect CellEnrich <-> Fisher
# scMerge
# intersect scMerge <-> Fisher
# pbmc 0.3 median 2
int <- data.frame(
val = c(
24, 5, 2,
29, 15, 6,
6, 4, 1,
38, 7, 4,
35, 7, 0,
8, 25, 1,
1, 10, 1,
13, 25, 3,
9, 5, 1
),
method = rep(c('CellEnrich', 'Fisher','Overlap')),
group = rep(c('B','CD14+ Mono','CD8 T','DC','FCGR3A+ Mono','Memory CD4 T','Naive CD4 T','NK','Platelet'), each = 3)
)
ggplot(int, aes(x = group, y= val, fill = method)) +
geom_bar(stat = 'identity', position = 'dodge') +
xlab('Group') +
ylab('Number of Pathway') +
ggtitle(label = 'pbmc OddRatio 0.3 median')
# pbmc 0.1 median
int <- data.frame(
val = c(
26, 5, 3,
41, 15, 7,
22, 4, 1,
48, 7, 4,
48, 7, 1,
9, 25, 1,
1, 10, 1,
31, 25, 7,
25, 5, 1
),
method = rep(c('CellEnrich', 'Fisher','Overlap')),
group = rep(c('B','CD14+ Mono','CD8 T','DC','FCGR3A+ Mono','Memory CD4 T','Naive CD4 T','NK','Platelet'), each = 3)
)
# pbmc 0.1 median 2
int <- data.frame(
val = c(
26, 5, 2,
42, 15, 7,
18, 4, 1,
47, 7, 4,
48, 7, 0,
11, 25, 1,
1, 10, 1,
23, 25, 3,
26, 5, 1
),
method = rep(c('CellEnrich', 'Fisher','Overlap')),
group = rep(c('B','CD14+ Mono','CD8 T','DC','FCGR3A+ Mono','Memory CD4 T','Naive CD4 T','NK','Platelet'), each = 3)
)
ggplot(int, aes(x = group, y= val, fill = method)) +
geom_bar(stat = 'identity', position = 'dodge') +
xlab('Group') +
ylab('Number of Pathway') +
ggtitle(label = 'pbmc OddRatio 0.1 median')
# pancreas
# acinar
genes <- c('MRC2','PDGFRB','GNG11','COL18A1','ITGA11',
'LTBP1','FMOD','COL12A1','SNAI2','SFRP2',
'LAMA2','COL6A2','COL5A1','THBS2','COL5A3',
'EDNRA','ZEB2','LUM','COL4A1','TPM2',
'VCAN','FBN1','COL5A2','COL4A2','COL15A1',
'ANTXR1','VIM','COL3A1','COL6A3','COL6A1',
'COL1A2','FN1','CREB3L1','UNC5B','LAMB1',
'COL1A1','CALD1','MSN','PSAT1')
# activated_stellate
genes <- c('ELOVL2','COL22A1','NPY1R','BHMT','BMP4',
'MYO1A','PDE2A','CALCR','CALY','VTN',
'AKR1C4','COL2A1','GHRL','ASGR1','OPRK1',
'FGF14','HMGCS2','HRH2','SUCNR1','NKX2-2',
'UGT2B4','L1CAM','GLDC','F10','CPLX2',
'DEFB1','APOH','ADRB1','PPP2R2B','KIF5C',
'SNAP25','HNF4A','CALD1','ENPP2','CDKN2A',
'FOXA2','SERPINA1','RASD1','ANK3','ACSL1',
'CLDN7','PAPSS2','SOX4')
# alpha
genes <- c('GCG','PPP1R1A','TTR','CPE','GNG4',
'MAFB','PAX6','PTPRN','NEUROD1','CNTN1',
'ENPP2','ISL1','KCNMB2','PDE3B','PTPRN2',
'GRIA3','PGR','F10','SSTR2','FEV',
'ALDH1A1','MYO10','SH3GL2','PLCE1','CAMK2B',
'CD99L2','FABP5','GLS','SSX2IP','DPP4',
'PAPSS2','SDC2','TUSC3','LDHA','PFN2',
'VIM','CLU','LAPTM4B','CKB','FOXA2',
'CLDN7')
# beta
genes <- c('HHEX','GAD2','ISL1','NEUROD1','SST',
'PARVB','PTPRN','RFX6','CAMK2B','ABCC8',
'SNAP25','CASR','UCHL1','KCNJ8','LEPR',
'CPE','GABRB3','SSTR1','CADM1','GHSR',
'HADH','EDN3','UNC5B','PPP1R1A','MAFB',
'CD9','PDX1','TJP2','PCLO','BAMBI',
'SEC11C','BHLHE41','SCD5','RGS2','SERPINA1',
'SLC38A1','AQP3','CALD1','PFN2','FOXA2',
'MARCKS')
# delta
genes <- c('ABCC8','INS','UCHL1','ADCYAP1','GAD2',
'HADH','MAFA','PDX1','IAPP','PAX6',
'PTPRN','NEUROD1','G6PC2','CASR','MAFB',
'ATP2A3','GNG4','SCD5','CPE','KIF5C',
'SH3GL2','PPP1R1A','PFKFB2','RASD1','ROBO2',
'GSN','RASGRF1','PFN2','VEGFA','SIPA1L2',
'ALCAM','RRAGD','FOXA2','CLDN7')
# ductal
genes <- c('CFTR','KRT19','SDC4','SERPING1','ZFP36L1',
'CLDN1','SLC4A4','DEFB1','LITAF','SOX4',
'CD9','CFB','COL18A1','SPP1','CAV2',
'SERPINA5','CTSH','CD24','LCN2','MMP7',
'AQP1','ALDH1A3','BACE2','VTCN1','CD44',
'KRT18','CXADR','DAB2','ENAH','TMSB4X',
'CLDN4','ANXA2','LAMC1','ADCY5','JUP',
'CLDN7','VAMP8','CDH1','ATP1B1','PERP'
)
# endothelial
genes <- c('KDR','ERG','S1PR1','GNG11','CDH5',
'ITGA5','MEF2C','RHOJ','CLEC2B','ESAM',
'SNAI1','RAMP2','ACVRL1','DLL4','A2M',
'CXCR4','PDE2A','COL4A1','PODXL','ARHGDIB',
'SHANK3','CALCRL','COL4A2','PTPRB','RAPGEF4',
'FLT1','VIM','EFNA1','ID3','MSN',
'WWTR1','PLXNA2')
# epsilon
genes <- c('ITGB3','GLI2','EDNRB','PDGFRB','SNAI1',
'GYPC','FABP4','CAV1','TRPV2','IFI16',
'EDNRA','A2M','MRC2','HEYL','TBX3',
'ENPEP','BST2','COLEC11','CD4','ITGA4',
'COL6A2','ZEB2','AVPR1A','COL5A1','LAMA4',
'WNT5A','COL4A1','ESAM','NOTCH2','COL5A2',
'ARHGDIB','TRPC6','COL4A2','COX4I2','COL15A1',
'ITGA1','SLC6A12','SLIT3','VIM','COL3A1',
'COL1A2','CALD1','CA2','COL1A1','CHSY1',
'COX7A1','UNC5B','ADCY4','SDC2')
# gamma
genes <- c('GSTA1','GSTA2','ANPEP','IL32','PRSS3',
'CTRB2','CPA2','AKR1C3','CTRB1','KLK1',
'PRSS1','BCAT1','PLA2G1B','GATM','CPA1',
'CPB1','CEL','SDC4','PNLIP','MGST1',
'LDHB','CD24','GDF15','CD44','KRT18',
'CLDN4','CLDN7','RAB11FIP1')
# macrophage
genes <- c('PTGS1','BTK','HPGDS','KIT','CPA3',
'NCF4','LCP2','RUNX3','BATF','CSF2RB',
'LAPTM5','TPSB2','GRAP2','PRKCB','TPSAB1',
'ALOX5AP','RAB27B','HCLS1','ARHGDIB','PTPRC',
'SLC45A3','CD37','IL4R','RAC2','ABCC4',
'VIM','SLC18A2','GMPR')
# mast
genes <- c('ITGB2','C1QC','RUNX3','CD86','CCR1',
'HCK','C1QB','LCP2','PIK3R5','BCL2A1',
'C1QA','CSF1R','FPR3','DOCK2','LAPTM5',
'VSIG4','TYROBP','HCLS1','FCGR2A','PTPRC',
'ACP5','SDS','SIRPA','ALOX5AP','HLA-DRA',
'TNFRSF1B','WIPF1','VIM','CD74','LITAF')
# quiescent_stellate
genes <- c('WNT16','PTPRZ1','EDNRB','UCN2','GNG11',
'ITGB3','IFI16','SOX2','HEPH','ZEB2',
'NGFR','PLA2G4A','SLIT2','COL9A3','A2M',
'MEF2C','MMP14','NLGN4X','ABCB4','RUNX2',
'NRXN3','CRYAB','NRXN1','COL4A2','ASPA',
'SEMA3C','TIMP3','ITGA6','TUBB6','WIPF1',
'LPCAT2','BVES','PLAUR','AP1S2','WWTR1',
'VIM','PPP2R2B','CALD1','ITGB8','CD9',
'ETS1','ITPK1','DAG1','MPDZ')
# schwann
genes <- c('ISL1','CXXC4','GAD2','KCNMB2','PPY',
'NEUROD1','ABCC8','PAX6','ETV1','CNTN1',
'PGR','PTPRN','CAMK2B','TSPAN7','GRIA3',
'UCHL1','TTR','GNG4','CARD11','FEV',
'CPE','AMOTL1','ID4','NPNT','ENTPD2',
'PCLO','S100A10','SEMA3E','SLC6A4','CHRM3',
'INPP5F','PRKACB','ID2','PTPRN2','ABCB1',
'TUBB2A','SLC40A1','GCNT3','AQP3','ALDH1A1',
'TUSC3','FGFR1','BHLHE41','CLU','RAP1GAP',
'FOXA2','MARCKS')
getHyperPvalue(genes, genesets)
# compareplot
# cellenrich
# fisher
# intersect
# pancreas 0.3 mean
int <- data.frame(
val = c(
46, 10, 0,
76, 1, 0,
25, 4, 0,
17, 3, 1,
21, 5, 0,
70, 6, 1,
59, 3, 1,
30, 11, 0,
52, 10, 0,
69, 1, 1,
26, 10, 3,
78, 8, 2,
52, 7, 0
),
method = rep(c('CellEnrich','Fisher','Overlap')),
group = rep(c('acinar', 'activated_stellate', 'alpha', 'beta', 'delta', 'ductal', 'endothelial', 'epsilon', 'gamma', 'macrophage', 'mast', 'quiescent_stellate','schwann'),each = 3)
)
# pancreas 0.3 mean - 2
int <- data.frame(
val = c(
36, 10, 1,
63, 1, 0,
22, 4, 0,
27, 3, 1,
1, 5, 0,
53, 6, 0,
71, 3, 2,
30, 11, 0,
36, 10, 0,
78, 1, 1,
26, 10, 2,
71, 8, 2,
46, 7, 0
),
method = rep(c('CellEnrich','Fisher','Overlap')),
group = rep(c('acinar', 'activated_stellate', 'alpha', 'beta', 'delta', 'ductal', 'endothelial', 'epsilon', 'gamma', 'macrophage', 'mast', 'quiescent_stellate','schwann'),each = 3)
)
ggplot(int, aes(x = group, y= val, fill = method)) +
geom_bar(stat = 'identity', position = 'dodge') +
xlab('Group') +
ylab('Number of Pathway') +
ggtitle(label = 'pancreas OddRatio 0.3 mean')
# pancreas 0.3 median
int <- data.frame(
val = c(
61, 10, 2,
92, 1, 0,
30, 4, 1,
27, 3, 1,
4, 5,0,
66, 6, 0,
79, 3, 2,
35, 11, 0,
40, 10, 0,
89, 1, 1,
29, 10, 2,
90, 8, 2,
67, 7, 0
),
method = rep(c('CellEnrich','Fisher','Overlap')),
group = rep(c('acinar', 'activated_stellate', 'alpha', 'beta', 'delta', 'ductal', 'endothelial', 'epsilon', 'gamma', 'macrophage', 'mast', 'quiescent_stellate','schwann'),each = 3)
)
# pancreas 0.3 median - 2
int <- data.frame(
val = c(
47, 10, 1,
73, 1, 0,
22, 4, 0,
25, 3, 0,
1, 5, 0,
55, 6, 0,
74, 3, 3,
27, 11, 0,
33, 10, 0,
81, 1, 1,
22, 10, 2,
79, 8, 2,
47, 7, 0
),
method = rep(c('CellEnrich','Fisher','Overlap')),
group = rep(c('acinar', 'activated_stellate', 'alpha', 'beta', 'delta', 'ductal', 'endothelial', 'epsilon', 'gamma', 'macrophage', 'mast', 'quiescent_stellate','schwann'),each = 3)
)
ggplot(int, aes(x = group, y= val, fill = method)) +
geom_bar(stat = 'identity', position = 'dodge') +
xlab('Group') +
ylab('Number of Pathway') +
ggtitle(label = 'pancreas OddRatio 0.3 median')
hsa <- keggList('hsa')
hsa <- keggLink('pathway', 'hsa')
hsa <- unname(hsa)
hsa <- sort(unique(hsa))
hsa <- sapply(hsa, function(i){gsub('path:','',i)})
hsa <- unname(hsa)
genesets <- list()
for(i in 1:length(hsa)){
res <- keggGet(hsa[i])[[1]]
genes <- unname(sapply(res$GENE[grep(';', res$GENE)], function(i){strsplit(i, '; ')[[1]][1]}))
genesets[[i]] <- genes
names(genesets)[i] <- gsub(' - Homo sapiens (human)', '', res$NAME)
}
names(genesets) <- unname(sapply(names(genesets), function(i){strsplit(i, ' - ')[[1]][1]}))
grep('Amoe',names(genesets))
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