The pipeline consists of four successive steps: data pre-processing, cellular clustering and pseudo-temporal ordering, determining differential expressed genes and identifying biomarkers.
library(DIscBIO)
library(partykit)
library(enrichR)
Loading required package: SingleCellExperiment Loading required package: SummarizedExperiment Loading required package: MatrixGenerics Loading required package: matrixStats Attaching package: ‘MatrixGenerics’ The following objects are masked from ‘package:matrixStats’: colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse, colCounts, colCummaxs, colCummins, colCumprods, colCumsums, colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs, colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats, colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds, colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads, colWeightedMeans, colWeightedMedians, colWeightedSds, colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet, rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods, rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps, rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins, rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks, rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars, rowWeightedMads, rowWeightedMeans, rowWeightedMedians, rowWeightedSds, rowWeightedVars Loading required package: GenomicRanges Loading required package: stats4 Loading required package: BiocGenerics Loading required package: parallel Attaching package: ‘BiocGenerics’ The following objects are masked from ‘package:parallel’: clusterApply, clusterApplyLB, clusterCall, clusterEvalQ, clusterExport, clusterMap, parApply, parCapply, parLapply, parLapplyLB, parRapply, parSapply, parSapplyLB The following objects are masked from ‘package:stats’: IQR, mad, sd, var, xtabs The following objects are masked from ‘package:base’: anyDuplicated, append, as.data.frame, basename, cbind, colnames, dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep, grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank, rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply, union, unique, unsplit, which.max, which.min Loading required package: S4Vectors Attaching package: ‘S4Vectors’ The following object is masked from ‘package:base’: expand.grid Loading required package: IRanges Loading required package: GenomeInfoDb Loading required package: Biobase Welcome to Bioconductor Vignettes contain introductory material; view with 'browseVignettes()'. To cite Bioconductor, see 'citation("Biobase")', and for packages 'citation("pkgname")'. Attaching package: ‘Biobase’ The following object is masked from ‘package:MatrixGenerics’: rowMedians The following objects are masked from ‘package:matrixStats’: anyMissing, rowMedians Loading required package: grid Loading required package: libcoin Loading required package: mvtnorm Attaching package: ‘partykit’ The following object is masked from ‘package:SummarizedExperiment’: width The following object is masked from ‘package:GenomicRanges’: width The following object is masked from ‘package:IRanges’: width The following object is masked from ‘package:S4Vectors’: width The following object is masked from ‘package:BiocGenerics’: width Welcome to enrichR Checking connection ... Connection is Live!
Differentially expressed genes between individual clusters are identified using the significance analysis of sequencing data (SAMseq), which is a new function in significance analysis of microarrays (Li and Tibshirani 2011) in the samr package v2.0 (Tibshirani et all., 2015). SAMseq is a non-parametric statistical function dependent on Wilcoxon rank statistic that equalizes the sizes of the library by a resampling method accounting for the various sequencing depths. The analysis is implemented over the pure raw dataset that has the unnormalized expression read counts after excluding the ERCCs. Furthermore, DEGs in each cluster comparing to all the remaining clusters are determined using binomial differential expression, which is based on binomial counting statistics.
The user can define DEGs between all clusters generated by either K-means or model based clustering by applying the “DEGanalysis” function. Another alternative is to define DEGs between particular clusters generated by K-means clustering by applying the “DEGanalysis2clust” function. The outcome of these two functions is a list of two components:
load("SC.RData") # Loading the "SC" object that has include the data of the k-means clustering
load("Ndata.RData") # Loading the "Ndata" object and stored in the @ndata will be used to plot the expression of genes
load("expdata.RData") # Loading the "expdata" object and stored in the @expdata will be used to plot the expression of genes
sc<-SC # Storing the data of SC in the sc
sc@ndata<-Ndata
sc@expdata<-expdata
########## Removing the unneeded objects
rm(Ndata)
rm(expdata)
rm(SC)
The function ClustDiffGenes identifies differentially regulated genes for each cluster of the K-means clustering in comparison to the ensemble of all cells. It returns a list with a data.frame element for each cluster that contains the mean expression across all cells not in the cluster (mean.ncl) and in the cluster (mean.cl), the fold-change in the cluster versus all remaining cells (fc), and the p-value for differential expression between all cells in a cluster and all remaining cells. The p-value is computed based on the overlap of negative binomials fitted to the count distributions within the two groups akin to DESeq.
cdiffBinomial<-ClustDiffGenes(sc,K=4,export = T,fdr=.01,quiet=T) ########## Binomial differential expression analysis
#### To show the result table
head(cdiffBinomial[[1]]) # The first component
head(cdiffBinomial[[2]]) # The second component
DEGsE | DEGsS |
---|---|
ENSG00000001630 | CYP51A1 |
ENSG00000002586 | CD99 |
ENSG00000003402 | CFLAR |
ENSG00000003436 | TFPI |
ENSG00000003756 | RBM5 |
ENSG00000004059 | ARF5 |
Target Cluster | VS | Gene number | File name | Gene number | File name | |
---|---|---|---|---|---|---|
<chr> | <chr> | <int> | <chr> | <int> | <chr> | |
1 | Cluster 1 | Remaining Clusters | 1052 | Up-DEG-cluster1.csv | 678 | Down-DEG-cluster1.csv |
2 | Cluster 2 | Remaining Clusters | 0 | Up-DEG-cluster2.csv | 1 | Down-DEG-cluster2.csv |
3 | Cluster 3 | Remaining Clusters | 2 | Up-DEG-cluster3.csv | 5 | Down-DEG-cluster3.csv |
4 | Cluster 4 | Remaining Clusters | 1 | Up-DEG-cluster4.csv | 1 | Down-DEG-cluster4.csv |
Volcano plots are used to readily show the DEGs by plotting significance versus fold-change on the y and x axes, respectively.
name<-cdiffBinomial[[2]][1,6] ############ Selecting the DEGs' ############## Down-DEG-cluster1.csv
U<-read.csv(file=paste0(name),head=TRUE,sep=",")
Vplot<-VolcanoPlot(U,value=0.0001,name=name,FS=0.7,fc=0.75)
There are several methods to identify biomarkers, among them are decision trees and hub detection through networking analysis. The outcome of STRING analysis is stored in tab separated values (TSV) files. These TSV files served as an input to check both the connectivity degree and the betweenness centrality, which reflects the communication flow in the defined PPI networks
Decision trees are one of the most efficient classification techniques in biomarkers discovery. Here we use it to predict the sub-population of a target cell based on transcriptomic data. Two types of decision trees can be performed: classification and regression trees (CART) and J48. The decision tree analysis is implemented over a training dataset, which consisted of the DEGs obtained by either SAMseq or the binomial differential expression. The performance of the generated trees can be evaluated for error estimation by ten-fold cross validation assessment using the "J48DTeval" and "RpartEVAL" functions. The decision tree analysis requires the dataset to be class vectored by applying the “ClassVectoringDT” function.
###################### Finding biomarker genes between cluster 1 and cluster 4
First="CL1"
Second="CL4"
load("DATAforDT.RData")
j48dt<-J48DT(DATAforDT) #J48 Decision Tree
summary(j48dt)
rm(j48dt)
J48 pruned tree ------------------ MALAT1 <= 102.794044: CL1 (344.0/1.0) MALAT1 > 102.794044 | CA1 <= 0.103233: CL4 (693.0) | CA1 > 0.103233 | | SLC25A39 <= 6.310579: CL4 (17.0) | | SLC25A39 > 6.310579: CL1 (8.0/1.0) Number of Leaves : 4 Size of the tree : 7
=== Summary === Correctly Classified Instances 1060 99.8117 % Incorrectly Classified Instances 2 0.1883 % Kappa statistic 0.9957 Mean absolute error 0.0035 Root mean squared error 0.042 Relative absolute error 0.7976 % Root relative squared error 8.9321 % Total Number of Instances 1062 === Confusion Matrix === a b <-- classified as 350 0 | a = CL1 2 710 | b = CL4
rpartDT<-RpartDT(DATAforDT)
rm(rpartDT)
n= 1062 node), split, n, loss, yval, (yprob) * denotes terminal node 1) root 1062 350 CL4 (0.329566855 0.670433145) 2) MALAT1< 104.7847 344 1 CL1 (0.997093023 0.002906977) 4) SYNE2< 12.34286 343 0 CL1 (1.000000000 0.000000000) * 5) SYNE2>=12.34286 1 0 CL4 (0.000000000 1.000000000) * 3) MALAT1>=104.7847 718 7 CL4 (0.009749304 0.990250696) 6) SLC25A39>=7.9044 8 1 CL1 (0.875000000 0.125000000) 12) CFLAR< 4.819648 7 0 CL1 (1.000000000 0.000000000) * 13) CFLAR>=4.819648 1 0 CL4 (0.000000000 1.000000000) * 7) SLC25A39< 7.9044 710 0 CL4 (0.000000000 1.000000000) *
To define protein-protein interactions (PPI) over a list of genes, STRING-api is used. The outcome of STRING analysis was stored in tab separated values (TSV) files. These TSV files served as an input to check both the connectivity degree and the betweenness centrality, which reflects the communication flow in the defined PPI networks.
DEGs="All_DEGs"
FileName=paste0(DEGs)
data<-cdiffBinomial[[1]] [1:200,2] # DEGs gene list from Binomial analysis (taking only the firat 200 genes)
ppi<-PPI(data,FileName)
networking<-NetAnalysis(ppi)
networking ##### In case the Examine response components = 200 and an error "linkmat[i, ]" appeared, that means there are no PPI.
Retrieving URL. Please wait... Successful retrieval. ── Column specification ──────────────────────────────────────────────────────── cols( stringId_A = col_character(), stringId_B = col_character(), preferredName_A = col_character(), preferredName_B = col_character(), ncbiTaxonId = col_double(), score = col_double(), nscore = col_double(), fscore = col_double(), pscore = col_double(), ascore = col_double(), escore = col_double(), dscore = col_double(), tscore = col_double() ) Number of nodes: 174 Number of links: 451 Link Density: 2.59195402298851 The connectance of the graph: 0.0149823931964654 Mean Distences2.52208201892744 Average Path Length2.52208201892744
names | degree | betweenness | |
---|---|---|---|
<chr> | <dbl> | <dbl> | |
74 | HSP90AA1 | 29 | 818.84444 |
85 | ACTB | 24 | 389.53333 |
134 | RHOA | 22 | 0.00000 |
83 | FYN | 18 | 105.71667 |
125 | CD44 | 16 | 55.25000 |
24 | VCL | 15 | 0.00000 |
31 | ENO1 | 15 | 82.85556 |
79 | CS | 15 | 237.03889 |
127 | RPS20 | 15 | 26.20000 |
20 | TNFRSF1A | 14 | 98.58333 |
72 | HSPA5 | 14 | 160.82222 |
87 | TFRC | 14 | 191.15000 |
105 | EIF3I | 13 | 84.65000 |
52 | SDHA | 12 | 28.73889 |
114 | ACTN1 | 12 | 61.95000 |
43 | ITGA2B | 11 | 76.75000 |
66 | CTNNA1 | 11 | 47.00000 |
113 | NME1-NME2 | 11 | 56.28333 |
128 | MDH1 | 11 | 24.41667 |
129 | RPL18 | 11 | 20.25000 |
131 | CAPZB | 11 | 0.00000 |
144 | RPS5 | 11 | 0.00000 |
12 | PSMA4 | 10 | 0.00000 |
30 | SLC11A1 | 10 | 10.25000 |
119 | RPL31 | 10 | 16.48333 |
35 | SEC61A1 | 9 | 11.36667 |
64 | ITGB5 | 9 | 26.03333 |
102 | SLC25A5 | 9 | 26.06667 |
104 | CTSA | 9 | 35.14444 |
115 | CD59 | 9 | 23.00000 |
⋮ | ⋮ | ⋮ | ⋮ |
172 | TPD52 | 2 | 0 |
6 | CX3CL1 | 1 | 0 |
9 | SYPL1 | 1 | 0 |
11 | MAP4K5 | 1 | 0 |
21 | PTPN18 | 1 | 0 |
29 | TFPI | 1 | 0 |
32 | TBPL1 | 1 | 0 |
53 | DGKG | 1 | 0 |
54 | MGLL | 1 | 0 |
70 | DCBLD2 | 1 | 0 |
78 | JARID2 | 1 | 0 |
97 | INPP5A | 1 | 0 |
110 | CD99 | 1 | 0 |
126 | KLF6 | 1 | 0 |
132 | ELOVL5 | 1 | 0 |
140 | RASGRP2 | 1 | 0 |
142 | STXBP2 | 1 | 0 |
145 | MRPS24 | 1 | 0 |
149 | FCN1 | 1 | 0 |
157 | CCDC88C | 1 | 0 |
158 | ABHD5 | 1 | 0 |
159 | SH3YL1 | 1 | 0 |
160 | KAT6A | 1 | 0 |
161 | RBM5 | 1 | 0 |
163 | IGF2BP2 | 1 | 0 |
164 | FAT1 | 1 | 0 |
169 | JMJD4 | 1 | 0 |
170 | RNH1 | 1 | 0 |
173 | ST3GAL6 | 1 | 0 |
174 | ST3GAL1 | 1 | 0 |
data=networking[1:25,1] # plotting the network of the top 25 highly connected genes
network<-Networking(data,FileName,plot_width = 25, plot_height = 10)
Retrieving URL. Please wait... Successful retrieval. You can see the network with high resolution by clicking on the following link: https://string-db.org/api/highres_image/network?identifiers=HSP90AA1%0dACTB%0dRHOA%0dFYN%0dCD44%0dVCL%0dENO1%0dCS%0dRPS20%0dTNFRSF1A%0dHSPA5%0dTFRC%0dEIF3I%0dSDHA%0dACTN1%0dITGA2B%0dCTNNA1%0dNME1-NME2%0dMDH1%0dRPL18%0dCAPZB%0dRPS5%0dPSMA4%0dSLC11A1%0dRPL31&species=9606
dbs <- listEnrichrDbs()
head(dbs)
#print(dbs)
geneCoverage | genesPerTerm | libraryName | link | numTerms | |
---|---|---|---|---|---|
<dbl> | <dbl> | <chr> | <chr> | <dbl> | |
1 | 13362 | 275 | Genome_Browser_PWMs | http://hgdownload.cse.ucsc.edu/goldenPath/hg18/database/ | 615 |
2 | 27884 | 1284 | TRANSFAC_and_JASPAR_PWMs | http://jaspar.genereg.net/html/DOWNLOAD/ | 326 |
3 | 6002 | 77 | Transcription_Factor_PPIs | 290 | |
4 | 47172 | 1370 | ChEA_2013 | http://amp.pharm.mssm.edu/lib/cheadownload.jsp | 353 |
5 | 47107 | 509 | Drug_Perturbations_from_GEO_2014 | http://www.ncbi.nlm.nih.gov/geo/ | 701 |
6 | 21493 | 3713 | ENCODE_TF_ChIP-seq_2014 | http://genome.ucsc.edu/ENCODE/downloads.html | 498 |
############ Selecting the DEGs' table ##############
DEGs=cdiffBinomial[[2]][1,4] # Down-regulated markers in cluster 1
FileName=paste0(DEGs)
data<-read.csv(file=paste0(DEGs),head=TRUE,sep=",")
data<-as.character(data[,3])
dbs <- c("KEGG_2019_Human","GO_Biological_Process_2015")
enriched <- enrichr(data, dbs)
KEGG_2019_Human<-enriched[[1]][,c(1,2,3,9)]
GO_Biological_Process_2015<-enriched[[2]][,c(1,2,3,9)]
GEA<-rbind(KEGG_2019_Human,GO_Biological_Process_2015)
GEA
Uploading data to Enrichr... Done. Querying KEGG_2019_Human... Done. Querying GO_Biological_Process_2015... Done. Parsing results... Done.
Term | Overlap | P.value | Genes |
---|---|---|---|
<chr> | <chr> | <dbl> | <chr> |
Ribosome | 66/153 | 1.195049e-43 | RPL4;RPL5;RPL30;MRPS15;RPL3;RPL32;RPL31;MRPS11;RPLP1;MRPS12;RPLP0;RPL10A;RPL6;RPL7;RPS4X;RPS14;MRPL3;RPS16;RPL18A;RPL36;RPL35;MRPL9;RPS11;RPS13;RPL21;RPS7;RPS8;RPS5;RPS6;MRPS18A;RPL13A;MRPS21;RPSA;RPS3A;MRPS7;RPL37A;RPL24;RPL27;RPL26;RPL29;UBA52;RPL12;RPL11;MRPL14;MRPL15;MRPL12;MRPL13;MRPL11;RPL14;RPS3;RPL15;RPS2;RPL18;RPS27A;RPL17;RPL19;RPL41;RPL35A;RPL23A;RPS27;RPS20;RPL22L1;FAU;RPS21;RPS24;RPS23 |
Proteasome | 25/45 | 9.261661e-21 | PSMD14;PSMA7;PSMD8;PSMB6;PSMB7;PSMB4;PSMB5;PSMB2;PSMB3;PSMD2;PSMB1;PSMD3;PSMD1;PSMF1;ADRM1;PSMA5;PSMA6;PSMC5;PSMA3;PSMA4;PSMC3;PSMC4;PSME3;PSMC2;PSME1 |
Protein processing in endoplasmic reticulum | 36/165 | 2.122271e-13 | VCP;HSP90AB1;RPN2;RPN1;DERL1;HERPUD1;SEC61A1;GANAB;LMAN2;BAG1;CAPN2;UBQLN1;SEC61B;SKP1;TXNDC5;SEC31A;PDIA3;HSPA8;XBP1;SEC13;HSP90AA1;HSPA5;WFS1;SSR2;SSR3;RAD23A;HSPA2;PDIA6;EIF2S1;CKAP4;DDOST;PDIA4;DNAJA1;CANX;CALR;P4HB |
Huntington disease | 37/193 | 6.230550e-12 | NDUFB9;DNAH1;NDUFA11;NDUFB6;DCTN2;DCTN1;COX7A2;COX5B;COX5A;POLR2A;UQCRFS1;CYC1;POLR2H;NDUFV2;NDUFV1;AP2M1;POLR2K;TGM2;COX8A;NDUFA9;NDUFC2;COX6C;SDHA;COX6B1;SOD1;CREB3;NDUFS7;NDUFAB1;UQCRC1;NDUFS3;CYCS;NDUFS2;VDAC1;UQCRC2;SLC25A5;SLC25A4;SLC25A6 |
Cell cycle | 27/124 | 2.241324e-10 | PCNA;MCM7;PRKDC;YWHAB;PKMYT1;ANAPC11;CDC20;CCNB2;CCNB1;CCND1;PTTG1;YWHAQ;MYC;BUB3;SKP1;ANAPC7;PLK1;CDC25B;CCNA2;TFDP1;CCNE2;CDK4;CDK1;MCM3;MCM4;MCM5;MCM2 |
Oxidative phosphorylation | 28/133 | 2.399141e-10 | NDUFB9;NDUFA11;NDUFB6;COX7A2;COX5B;COX5A;UQCRFS1;CYC1;NDUFV2;NDUFV1;ATP6V1C2;COX8A;NDUFA9;ATP6V1G1;ATP6V0B;NDUFC2;SDHA;COX6C;COX6B1;PPA2;NDUFS7;PPA1;NDUFAB1;UQCRC1;NDUFS3;NDUFS2;UQCRC2;ATP6V0C |
Parkinson disease | 29/142 | 2.477250e-10 | NDUFB9;NDUFA11;NDUFB6;UBE2L6;COX7A2;COX5B;COX5A;UBB;UQCRFS1;CYC1;NDUFV2;NDUFV1;COX8A;NDUFA9;NDUFC2;SDHA;COX6C;COX6B1;NDUFS7;NDUFAB1;UQCRC1;NDUFS3;CYCS;NDUFS2;VDAC1;UQCRC2;SLC25A5;SLC25A4;SLC25A6 |
Protein export | 12/23 | 3.326536e-10 | OXA1L;SEC61A1;SPCS1;HSPA5;SRP72;SRP54;SRPRB;SRP68;SEC61B;SRP14;SEC11A;SRP9 |
Alzheimer disease | 30/171 | 5.527120e-09 | NDUFB9;NDUFA11;NDUFB6;COX7A2;ITPR3;COX5B;COX5A;NCSTN;CAPN2;UQCRFS1;CYC1;NDUFV2;NDUFV1;COX8A;PSENEN;NDUFA9;NDUFC2;SDHA;COX6C;COX6B1;TNFRSF1A;NDUFS7;NDUFAB1;UQCRC1;NDUFS3;CYCS;NDUFS2;UQCRC2;CALM2;GAPDH |
Non-alcoholic fatty liver disease (NAFLD) | 26/149 | 6.371987e-08 | NDUFB9;NDUFA11;NDUFB6;COX7A2;COX5B;COX5A;UQCRFS1;CYC1;NDUFV2;NDUFV1;COX8A;NDUFA9;XBP1;NDUFC2;SDHA;COX6C;EIF2S1;COX6B1;TNFRSF1A;NDUFS7;NDUFAB1;UQCRC1;NDUFS3;CYCS;NDUFS2;UQCRC2 |
RNA transport | 26/165 | 5.115286e-07 | EIF4A1;EIF4A3;TPR;NUP62;EIF4EBP1;SAP18;RAE1;EIF4B;PRMT5;SEC13;NUP133;NCBP2;RANGAP1;EIF2S1;EIF1;EEF1A1;EIF5;CLNS1A;EIF3I;STRAP;EIF3H;EIF3E;EIF3F;EIF3D;RAN;EIF4G1 |
Oocyte meiosis | 21/125 | 2.185197e-06 | ANAPC7;YWHAB;PLK1;ITPR3;PPP2R5D;PKMYT1;ANAPC11;AURKA;PPP1CA;CDC20;PPP1CB;PPP2CA;CCNB2;CCNB1;PTTG1;CCNE2;YWHAQ;PPP2R1A;CDK1;CALM2;SKP1 |
Thermogenesis | 29/231 | 1.278000e-05 | NDUFB9;NDUFA11;NDUFB6;COX7A2;COX5B;COX5A;ACTB;UQCRFS1;CYC1;NDUFV2;NDUFV1;COX8A;NDUFA9;ACTL6A;RPS6;NDUFC2;COX6C;SDHA;SMARCA4;COX6B1;CREB3;NDUFS7;NDUFAB1;UQCRC1;GNAS;NDUFS3;NDUFS2;UQCRC2;FGFR1 |
Epstein-Barr virus infection | 26/201 | 2.089389e-05 | PSMD14;PSMD8;CCND1;MYC;PSMD2;PSMD3;PSMD1;PDIA3;STAT1;DDX58;ADRM1;ISG15;CCNA2;PSMC5;PSMC3;CCNE2;OAS1;PSMC4;CDK4;OAS3;PSMC2;CYCS;CALR;VIM;CD44;IRF9 |
Hepatitis C | 21/155 | 6.423047e-05 | YWHAB;DDX58;STAT1;MX1;EIF2S1;EGFR;TNFRSF1A;PPP2CA;CCND1;YWHAQ;OAS1;PPP2R1A;CDK4;MYC;PSME3;OAS3;CLDN7;IFIT1B;CYCS;EIF3E;IRF9 |
DNA replication | 9/36 | 7.730573e-05 | POLD4;FEN1;PCNA;MCM7;POLD2;MCM3;MCM4;MCM5;MCM2 |
Human T-cell leukemia virus 1 infection | 26/219 | 9.099964e-05 | NRP1;ANAPC11;CDC20;CCNB2;CCND1;PTTG1;MYC;TSPO;BUB3;RANBP1;FDPS;ANAPC7;TNFRSF1A;FOSL1;CCNA2;CREB3;CCNE2;CDK4;CANX;VDAC1;CALR;LTBR;SLC25A5;SLC25A4;RAN;SLC25A6 |
Cellular senescence | 21/160 | 1.021684e-04 | IGFBP3;ITPR3;PPP1CA;PPP1CB;CCNA2;CCNB2;CCNB1;CCND1;CCNE2;RBBP4;CDK4;MYC;CAPN2;EIF4EBP1;CDK1;VDAC1;SLC25A5;SQSTM1;CALM2;SLC25A4;SLC25A6 |
Pathogenic Escherichia coli infection | 11/55 | 1.146683e-04 | TUBA1B;KRT18;YWHAQ;ARPC2;NCL;ARPC1A;ARPC5L;TUBB4B;ACTB;RHOA;TUBA8 |
Spliceosome | 18/134 | 2.327610e-04 | TCERG1;HSPA8;NCBP2;EIF4A3;SRSF1;HSPA2;LSM5;LSM4;SNRPD2;SNRPD1;PCBP1;SRSF3;SNRPA1;SNRPD3;HNRNPC;SNRNP200;SRSF7;SNRPB |
N-Glycan biosynthesis | 10/50 | 2.330233e-04 | GANAB;B4GALT1;RPN2;ALG5;RPN1;SRD5A3;ALG3;MGAT1;ALG11;DDOST |
Vibrio cholerae infection | 10/50 | 2.330233e-04 | SEC61A1;ATP6V1G1;ATP6V0B;GNAS;KDELR2;SEC61B;ATP6V0C;ACTB;PDIA4;ATP6V1C2 |
Necroptosis | 20/162 | 3.387172e-04 | RNF31;HSP90AA1;HSP90AB1;PARP1;STAT1;TNFRSF1A;SHARPIN;FTH1;CHMP2B;CAPN2;CHMP2A;CHMP3;VDAC1;RBCK1;SLC25A5;PPIA;SQSTM1;SLC25A4;IRF9;SLC25A6 |
Adherens junction | 12/72 | 3.477573e-04 | CTNND1;ERBB2;RAC2;CTNNA1;LMO7;ACTN4;IQGAP1;BAIAP2;ACTB;RHOA;EGFR;FGFR1 |
p53 signaling pathway | 12/72 | 3.477573e-04 | CCNB2;CCNB1;RRM2;CCND1;CCNE2;SIVA1;CDK4;IGFBP3;EI24;PERP;CDK1;CYCS |
Antigen processing and presentation | 12/77 | 6.536642e-04 | PDIA3;CD74;HSPA8;HSP90AA1;HSP90AB1;HSPA5;PSME3;CANX;PSME1;HSPA2;CALR;CTSB |
Pyrimidine metabolism | 10/57 | 7.019624e-04 | NME1-NME2;DTYMK;NT5E;DUT;RRM1;RRM2;TK1;TYMS;DCTPP1;NME1 |
Cardiac muscle contraction | 12/78 | 7.361984e-04 | COX8A;UQCRC1;UQCRFS1;ATP1B3;COX7A2;UQCRC2;ATP1A1;CYC1;COX5B;COX6C;COX5A;COX6B1 |
Citrate cycle (TCA cycle) | 7/30 | 7.652426e-04 | CS;MDH1;IDH1;IDH2;IDH3B;ACO2;SDHA |
Glyoxylate and dicarboxylate metabolism | 7/30 | 7.652426e-04 | CS;GRHPR;MDH1;SHMT2;CAT;ACO2;ACAT2 |
⋮ | ⋮ | ⋮ | ⋮ |
epithelial tube morphogenesis (GO:0060562) | 1/90 | 0.9923565 | TACSTD2 |
positive regulation of hormone secretion (GO:0046887) | 1/91 | 0.9927602 | PSMD9 |
positive regulation of cyclic nucleotide metabolic process (GO:0030801) | 1/91 | 0.9927602 | GNAS |
regulation of cAMP biosynthetic process (GO:0030817) | 1/98 | 0.9950485 | GNAS |
detection of external stimulus (GO:0009581) | 3/175 | 0.9956065 | FECH;SDC4;SDC1 |
tube morphogenesis (GO:0035239) | 1/101 | 0.9957925 | TACSTD2 |
detection of abiotic stimulus (GO:0009582) | 3/178 | 0.9961518 | FECH;SDC4;SDC1 |
positive regulation of secretion (GO:0051047) | 6/273 | 0.9965193 | PSMD9;MIF;GATA3;KCNN4;PPIA;CD276 |
membrane depolarization (GO:0051899) | 1/106 | 0.9967926 | CAV1 |
central nervous system development (GO:0007417) | 1/108 | 0.9971226 | S100B |
anterior/posterior pattern specification (GO:0009952) | 2/150 | 0.9972471 | PRKDC;ATP6AP2 |
regulation of cAMP metabolic process (GO:0030814) | 1/113 | 0.9978065 | GNAS |
regulation of cyclic nucleotide biosynthetic process (GO:0030802) | 1/116 | 0.9981361 | GNAS |
regulation of purine nucleotide biosynthetic process (GO:1900371) | 1/119 | 0.9984161 | GNAS |
regulation of nucleotide biosynthetic process (GO:0030808) | 1/119 | 0.9984161 | GNAS |
complement activation, classical pathway (GO:0006958) | 2/162 | 0.9984634 | C1QBP;CD46 |
regulation of transmembrane transport (GO:0034762) | 7/327 | 0.9986252 | LGALS3;CLIC3;AHNAK;S100A1;ATP1B3;ACTN4;AZIN1 |
adenylate cyclase-modulating G-protein coupled receptor signaling pathway (GO:0007188) | 1/122 | 0.9986541 | GNAS |
cilium organization (GO:0044782) | 1/125 | 0.9988562 | TTC17 |
complement activation (GO:0006956) | 2/175 | 0.9991867 | C1QBP;CD46 |
regulation of cyclic nucleotide metabolic process (GO:0030799) | 1/133 | 0.9992587 | GNAS |
regulation of ion transmembrane transport (GO:0034765) | 6/314 | 0.9992843 | LGALS3;CLIC3;AHNAK;S100A1;ATP1B3;ACTN4 |
synaptic transmission (GO:0007268) | 10/434 | 0.9993561 | NQO1;HSPA8;SLC38A1;SYPL1;GNG5;TSPO;VDAC1;KCNN4;GABARAP;AP2M1 |
pattern specification process (GO:0007389) | 8/385 | 0.9995267 | NOTCH2;NRP1;PRKDC;ALG5;ATP6AP2;IFT122;BMP7;AURKA |
protein activation cascade (GO:0072376) | 2/196 | 0.9997107 | C1QBP;CD46 |
G-protein coupled receptor signaling pathway, coupled to cyclic nucleotide second messenger (GO:0007187) | 1/153 | 0.9997484 | GNAS |
regionalization (GO:0003002) | 3/247 | 0.9998335 | PRKDC;ATP6AP2;IFT122 |
cellular component assembly involved in morphogenesis (GO:0010927) | 1/186 | 0.9999556 | OFD1 |
detection of chemical stimulus (GO:0009593) | 2/499 | 0.9999954 | AZGP1;PIP |
detection of chemical stimulus involved in sensory perception (GO:0050907) | 2/465 | 0.9999955 | AZGP1;PIP |