DIscBIO: a user-friendly pipeline for biomarker discovery in single-cell transcriptomics¶

Comparing DIscBIO with Granatum using the CTC dataset¶

Required Packages¶

In [1]:

library(DIscBIO)

Loading required package: SingleCellExperiment

Loading required package: SummarizedExperiment

Loading required package: MatrixGenerics

Loading required package: matrixStats


Attaching package: ‘MatrixGenerics’


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    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,
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    rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
    rowWeightedSds, rowWeightedVars


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Attaching package: ‘S4Vectors’


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Loading required package: GenomeInfoDb

Loading required package: Biobase

Welcome to Bioconductor

    Vignettes contain introductory material; view with
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    'citation("Biobase")', and for packages 'citation("pkgname")'.



Attaching package: ‘Biobase’


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Loading Granatum outcome¶

In [2]:

Gclusters <- read.csv(file = "Granatum Clusters.csv", sep = ",",header=F)
head(Gclusters)
Gexpression <- read.csv(file = "Granatum expression.csv", sep = ",",header=F)
rownames(Gexpression)<-Gexpression[,1]
Gexpression<-Gexpression[-1,-1]
head(Gexpression)
Gdegs <- read.csv(file = "Granatum DEGs.csv", sep = ",",header=F)
head(Gdegs)
G_DEGs<-split(Gdegs,Gdegs[,1])

A data.frame: 6 × 3
	V1	V2	V3
	<chr>	<chr>	<int>
1	CTC_GSE51827_Patient_1_SC.1_29	A	2
2	CTC_GSE51827_Patient_2_SC.1_29	A	2
3	CTC_GSE51827_Patient_3_SC.1_29	A	2
4	CTC_GSE51827_Patient_4_SC.1..6_29	A	3
5	CTC_GSE51827_Patient_5_SC.1_29	A	2
6	CTC_GSE51827_Patient_5_SC.2_29	A	2

A data.frame: 6 × 1462
	V2	V3	V4	V5	V6	V7	V8	V9	V10	V11	⋯	V1454	V1455	V1456	V1457	V1458	V1459	V1460	V1461	V1462	V1463
	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	⋯	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
ENSG00000108846	5.4072848	6.8109078	1.2908134	2.8492094	12.3229485	1.6683082	19.8480464	193.125083	59.174110	45.201623	⋯	0.6402611	0.6408244	0.6459228	0.6373460	0.6434012	0.6306340	0.6371360	0.6341252	0.6227913	0.6456889
ENSG00000173210	1.3567709	1.7423787	0.5709326	0.5732612	0.4440031	0.3086997	1.2850075	9.157289	2.281537	1.257446	⋯	0.6484419	0.6443141	0.6485020	0.6456889	0.6458140	0.6399252	0.6454222	0.6448441	0.6324347	0.6459228
ENSG00000111644	3.3447813	5.9108847	2.0065934	7.5542211	77.1120234	21.5736968	14.7261515	26.093964	132.499523	154.831043	⋯	0.6174161	0.6104179	0.6173906	0.6152936	0.6123675	0.6063123	0.6154869	0.6108782	0.5981478	0.6154869
ENSG00000130402	0.3980837	0.4118329	1.4933259	0.4090522	9.5066519	0.2977547	0.7979768	1.443908	2.759269	1.839443	⋯	0.6329959	0.6253810	0.6313437	0.6299671	6.3107720	6.5014828	6.7422157	0.6272627	0.6161573	0.6310276
ENSG00000106541	18.8177799	1.2721374	2.2301874	22.3207339	2.0601563	13.8736796	4.0764498	2.468832	3.449375	1.291451	⋯	0.6985622	0.6896522	0.6665373	0.6799377	0.6921019	0.6862371	0.6936271	0.6910232	0.6790786	0.6947489
ENSG00000101745	9.6810419	3.6371670	5.3989686	1.6138913	1.9072590	2.9976263	8.6929340	1.086224	7.898958	2.638993	⋯	0.5585965	0.5525144	0.5567729	0.5591832	0.5562217	0.5477898	12.9195377	0.5541856	0.5424044	0.5570941

A data.frame: 6 × 6
	V1	V2	V3	V4	V5	V6
	<chr>	<chr>	<dbl>	<dbl>	<dbl>	<dbl>
1	1 vs. 2	ENSG00000244734	0.000487000	0.005599241	-149406.590	149406.590
2	1 vs. 2	ENSG00000163736	0.000009740	0.000374000	15620.516	15620.516
3	1 vs. 2	ENSG00000101162	0.000340000	0.004967912	7281.581	7281.581
4	1 vs. 2	ENSG00000113140	0.000561000	0.006149656	3869.627	3869.627
5	1 vs. 2	ENSG00000158457	0.001088797	0.008659730	3752.162	3752.162
6	1 vs. 2	ENSG00000188536	0.000004980	0.000229000	3631.954	3631.954

Loading the CTC dataset¶

The "CTCdataset" dataset consisting of single migratory circulating tumor cells (CTCs) collected from patients with breast cancer. Data are available in the GEO database with accession number GSE51827.The dataset should be formatted in a data frame where columns refer to samples and rows refer to genes. We provide here the possibility to load the dataset either as ".csv" or ".rda" extensions.

In [3]:

FileName<-"CTCdataset"        # Name of the dataset
#CSV=TRUE                     # If the dataset has ".csv", the user shoud set CSV to TRUE
CSV=FALSE                     # If the dataset has ".rda", the user shoud set CSV to FALSE

if (CSV==TRUE){
    DataSet <- read.csv(file = paste0(FileName,".csv"), sep = ",",header=T)
    rownames(DataSet)<-DataSet[,1]
    DataSet<-DataSet[,-1]
} else{
    load(paste0(FileName,".rda"))
    DataSet<-get(FileName)
}
cat(paste0("The ", FileName," contains:","\n","Genes: ",length(DataSet[,1]),"\n","cells: ",length(DataSet[1,]),"\n"))

The CTCdataset contains:
Genes: 13181
cells: 1462

In [4]:

sc<- DISCBIO(DataSet)       # The DISCBIO class is the central object storing all information generated throughout the pipeline

1. Data Pre-processing¶

Prior to applying data analysis methods, it is standard to pre-process the raw read counts resulted from the sequencing. The preprocessing approach depends on the existence or absence of ERCC spike-ins. In both cases, it includes normalization of read counts and gene filtering.

Normalization of read counts To account for RNA composition and sequencing depth among samples (single-cells), the normalization method “median of ratios” is used. This method takes the ratio of the gene instantaneous median to the total counts for all genes in that cell (column median). The gene instantaneous median is the product of multiplying the median of the total counts across all cells (row median) with the read of the target gene in each cell. This normalization method makes it possible to compare the normalized counts for each gene equally between samples.

Gene filtering The key idea in filtering genes is to appoint the genes that manifest abundant variation across samples. Filtering genes is a critical step due to its dramatic impact on the downstream analysis. Gene filtering can be implemented based on gene expression.

DataPre-processing

In [5]:

load("distances.Rdata")
sc@distances<-distances

# Estimating a value for the "minexpr" parameter
S<-summary(rowMeans(DataSet,na.rm=TRUE))            # It gives an idea about the overall expression of the genes
minexpr= S[3]        

# Estimating a value for the "minnumber" parameters
minnumber= round(length(DataSet[1,])/10)                             # To be expressed in at 10% of the cells.

sc<-Normalizedata(sc, mintotal=1, minexpr=minexpr, minnumber=minnumber, maxexpr=Inf, downsample=FALSE, dsn=1, rseed=17000) 
sc<-FinalPreprocessing(sc,GeneFlitering="ExpF",export = TRUE)        # The GeneFiltering should be set to "ExpF"

The gene filtering method = Noise filtering

The Filtered Normalized dataset contains:
Genes: 209
cells: 1462



The Filtered Normalized dataset was saved as: filteredDataset.Rdata

Shared genes in the outcome of the pre-processing of DIscBIO and Granatum¶

In [6]:

SharedGenes<-cbind(rownames(sc@fdata),rownames(Gexpression))
colnames(SharedGenes)<-c("DIscBIO","Granatum")
gData<-rownames(Gexpression)
dData<-rownames(sc@fdata) 
idx_genes <- is.element(gData,dData)
SharedGenes<-gData[idx_genes]   
cat(paste0("There are ", length(SharedGenes)," shared genes between DIscBIO and Granatum.","\n"))

Warning message in cbind(...):
“number of rows of result is not a multiple of vector length (arg 1)”

There are 88 shared genes between DIscBIO and Granatum.

2. DIscBIO Clustering using K-means (Euclidean)¶

In [7]:

set.seed(222)
Km<-stats::kmeans(as.matrix(t(sc@fdata)),4)
sc@kmeans$kpart<-Km[[1]]
sc@cpart=Km[[1]]

Evaluating the stability and consistancy of the clusters generated by DIscBIO¶

In [8]:

options(repr.plot.width=12, repr.plot.height=25)
plotSilhouette(sc,K=4)

In [9]:

options(repr.plot.width=10, repr.plot.height=12)
Jaccard(sc,Clustering="K-means", K=4, plot = TRUE)     # Jaccard

0.43
0.348
0.639
0.576

In [10]:

f<-c(0.43, 0.348, 0.639, 0.576)
cat(paste0("The mean Jaccard of all the clusters is  ", round(mean(f), digits=1),"\n"))

The mean Jaccard of all the clusters is  0.5

CTC clusters with Granatum based on K-means (Euclidean)¶

In [11]:

SharedCells<- data.frame(sc@kmeans$kpart,Gclusters[,3])
same<-c()
for (i in 1: length(SharedCells[,1])){
	if (SharedCells[i,1]=="1" & SharedCells[i,2]=="2"){
		same<-c(same,i)
	}
	if (SharedCells[i,1]=="2" & SharedCells[i,2]=="3"){
		same<-c(same,i)
	}
	if (SharedCells[i,1]=="3" & SharedCells[i,2]=="4"){
		same<-c(same,i)
	}
	if (SharedCells[i,1]=="4" & SharedCells[i,2]=="1"){
		same<-c(same,i)
	}
}

cat(paste0("There are ", length(same),"\n"))
cat(paste0(round((length(same)/length(SharedCells[,1])) * 100,digits=1),"%", " of the cells are shared between DIscBIO and Granatum","\n"))

There are 763
52.2% of the cells are shared between DIscBIO and Granatum

In [12]:

Granatum<-sc
colnames(Gexpression)<-colnames(sc@fdata)
Granatum@fdata<-Gexpression
Granatum@kmeans$kpart<-Gclusters[,3]
names(Granatum@kmeans$kpart)<-Gclusters[,1]

Evaluating the stability and consistancy of the clusters generated by DIscBIO¶

In [13]:

options(repr.plot.width=12, repr.plot.height=25)
plotSilhouette(Granatum,K=4)

In [14]:

options(repr.plot.width=10, repr.plot.height=12)
Jaccard(Granatum,Clustering="K-means", K=4, plot = TRUE)     # Jaccard

0.611
0.273
0.305
0.625

In [15]:

f<-c(0.611, 0.273, 0.305, 0.625)
cat(paste0("The mean Jaccard of all the clusters is  ", round(mean(f), digits=1),"\n"))

The mean Jaccard of all the clusters is  0.5

Determining differentially expressed genes for K-means Clustering using DIscBIO¶

In [16]:

#cdiff<-DEGanalysis(sc,Clustering="K-means",K=4,fdr=0.05,name="DIscBIO",export = TRUE,quiet=TRUE)   ####### differential expression analysis between all clusters
#### To show the result table
#head(cdiff[[1]])                  # The first component 
#head(cdiff[[2]])                  # The second component

DIscBIO_DEGs

Comparing DEGs form DIscBIO and Granatum¶

In [17]:

#df1<-rbind(read.csv(file=paste0(cdiff[[2]][1,4]),head=TRUE,sep=","),read.csv(file=paste0(cdiff[[2]][1,6]),head=TRUE,sep=","))
df1<-rbind(read.csv(file="Up-regulated-DIscBIOCl4inCl1VSCl4.csv",head=TRUE,sep=","),read.csv(file="Low-regulated-DIscBIOCl4inCl1VSCl4.csv",head=TRUE,sep=","))
genes1vs4<-df1[,4]

#df2<-rbind(read.csv(file=paste0(cdiff[[2]][2,4]),head=TRUE,sep=","),read.csv(file=paste0(cdiff[[2]][2,6]),head=TRUE,sep=","))
df2<-rbind(read.csv(file="Up-regulated-DIscBIOCl3inCl1VSCl3.csv",head=TRUE,sep=","),read.csv(file="Low-regulated-DIscBIOCl3inCl1VSCl3.csv",head=TRUE,sep=","))
genes1vs3<-df2[,4]

#df3<-rbind(read.csv(file=paste0(cdiff[[2]][3,4]),head=TRUE,sep=","),read.csv(file=paste0(cdiff[[2]][3,6]),head=TRUE,sep=","))
df3<-rbind(read.csv(file="Up-regulated-DIscBIOCl2inCl1VSCl2.csv",head=TRUE,sep=","),read.csv(file="Low-regulated-DIscBIOCl2inCl1VSCl2.csv",head=TRUE,sep=","))
genes1vs2<-df3[,4]

#df4<-rbind(read.csv(file=paste0(cdiff[[2]][4,4]),head=TRUE,sep=","),read.csv(file=paste0(cdiff[[2]][4,6]),head=TRUE,sep=","))
df4<-rbind(read.csv(file="Up-regulated-DIscBIOCl3inCl4VSCl3.csv",head=TRUE,sep=","),read.csv(file="Low-regulated-DIscBIOCl3inCl4VSCl3.csv",head=TRUE,sep=","))
genes3vs4<-df4[,4]

#df5<-rbind(read.csv(file=paste0(cdiff[[2]][5,4]),head=TRUE,sep=","),read.csv(file=paste0(cdiff[[2]][5,6]),head=TRUE,sep=","))
df5<-rbind(read.csv(file="Up-regulated-DIscBIOCl4inCl2VSCl4.csv",head=TRUE,sep=","),read.csv(file="Low-regulated-DIscBIOCl4inCl2VSCl4.csv",head=TRUE,sep=","))
genes2vs4<-df5[,4]

#df6<-rbind(read.csv(file=paste0(cdiff[[2]][6,4]),head=TRUE,sep=","),read.csv(file=paste0(cdiff[[2]][6,6]),head=TRUE,sep=","))
df6<-rbind(read.csv(file="Up-regulated-DIscBIOCl2inCl3VSCl2.csv",head=TRUE,sep=","),read.csv(file="Low-regulated-DIscBIOCl2inCl3VSCl2.csv",head=TRUE,sep=","))
genes3vs2<-df6[,4]

[CL1 vs CL2 DIscBIO] vs [CL2 vs CL3 Granatum]¶

In [18]:

#################  [CL1 vs CL2  DIscBIO]   vs   [CL2 vs CL3 Granatum]
dData<-genes1vs2
cat(paste0("There are  ", length(dData), " DEGs detected by DIscBIO in CL1 vs CL2","\n"))
target=G_DEGs$`2 vs. 3`
S1<-subset(target,target[,6]>1.96)
targetGeneList<-S1[,2]
cat(paste0("There are  ", length(targetGeneList), " DEGs detected by Granatum in CL2 vs CL3","\n"))
gData<-targetGeneList
idx_genes <- is.element(dData,gData)
SharedGenes<-dData[idx_genes]   
cat(paste0("There are  ", length(SharedGenes), " DEGs shared between DIscBIO and Granatum","\n"))
df3[idx_genes,]

There are  133 DEGs detected by DIscBIO in CL1 vs CL2
There are  212 DEGs detected by Granatum in CL2 vs CL3
There are  51 DEGs shared between DIscBIO and Granatum

A data.frame: 51 × 9
	X	Row	Gene.ID	Gene.Name	Score.d.	Numerator.r.	Denominator.s.s0.	Fold.Change	q.value...
	<int>	<int>	<chr>	<chr>	<dbl>	<dbl>	<int>	<dbl>	<dbl>
4	6	186	TMSB10	ENSG00000034510	7315.95	7315.95	1	1.916397e+01	0.000000000
10	17	32	CXCL2	ENSG00000081041	4625.30	4625.30	1	5.631110e+09	0.000000000
18	31	65	HSPB1	ENSG00000106211	3898.20	3898.20	1	4.161295e+01	0.000000000
20	38	139	RPL34	ENSG00000109475	5204.40	5204.40	1	6.498620e+00	0.000000000
21	40	3	ACRBP	ENSG00000111644	3020.30	3020.30	1	1.683022e+01	0.000000000
23	44	125	RBM25	ENSG00000119707	5487.20	5487.20	1	2.160221e+02	0.000000000
27	49	134	RPL23	ENSG00000125691	4478.00	4478.00	1	6.296690e+00	0.000000000
31	59	140	RPL35	ENSG00000136942	3324.35	3324.35	1	8.804834e+00	0.000000000
34	63	131	RPL11	ENSG00000142676	4457.15	4457.15	1	5.475404e+00	0.000000000
36	66	129	RHOB	ENSG00000143878	6045.00	6045.00	1	2.638161e+10	0.000000000
37	69	142	RPL37	ENSG00000145592	4578.10	4578.10	1	1.501140e+01	0.000000000
38	72	24	CMIP	ENSG00000153815	4545.95	4545.95	1	3.202473e+01	0.000000000
39	74	136	RPL30	ENSG00000156482	3466.10	3466.10	1	5.190309e+00	0.000000000
40	75	116	PTGIR	ENSG00000160013	3204.55	3204.55	1	2.242519e-07	0.000000000
44	79	33	CXCL3	ENSG00000163734	3951.25	3951.25	1	8.235495e+06	0.000000000
45	81	200	UQCRQ	ENSG00000164405	4766.65	4766.65	1	1.211634e+02	0.000000000
47	84	132	RPL13	ENSG00000167526	3184.70	3184.70	1	1.726805e+00	0.000000000
51	92	144	RPL38	ENSG00000172809	4094.40	4094.40	1	1.355297e+01	0.000000000
52	93	149	RPLP2	ENSG00000177600	5609.80	5609.80	1	5.604572e+00	0.000000000
54	95	208	ZFP36L1	ENSG00000185650	5817.80	5817.80	1	2.189037e+10	0.000000000
57	99	143	RPL37A	ENSG00000197756	5125.80	5125.80	1	9.978369e+00	0.000000000
59	9	123	RASGRP2	ENSG00000068831	2652.05	2652.05	1	2.529199e-08	0.004101162
60	41	151	RPS12	ENSG00000112306	2864.35	2864.35	1	2.696123e+00	0.004101162
62	73	98	NRGN	ENSG00000154146	2886.00	2886.00	1	5.008879e+00	0.004101162
63	96	156	RPS23	ENSG00000186468	2762.60	2762.60	1	6.033706e+00	0.004101162
64	100	165	S100A6	ENSG00000197956	2980.60	2980.60	1	2.659768e+01	0.004101162
65	101	161	RPS4X	ENSG00000198034	2899.75	2899.75	1	1.164316e+01	0.004101162
66	102	145	RPL39	ENSG00000198918	2732.50	2732.50	1	4.174064e+00	0.004101162
70	88	78	KRT8	ENSG00000170421	2428.30	2428.30	1	1.007781e-08	0.007655502
71	103	159	RPS28	ENSG00000233927	2500.10	2500.10	1	4.147598e+00	0.007655502
74	32	171	SERPINE1	ENSG00000106366	2147.40	2147.40	1	8.806128e-09	0.014206087
76	36	2	ABCC3	ENSG00000108846	2193.00	2193.00	1	4.906766e+00	0.014206087
77	39	76	KRT18	ENSG00000111057	2215.95	2215.95	1	9.778015e-09	0.014206087
82	29	27	COTL1	ENSG00000103187	2100.35	2100.35	1	7.183117e+00	0.019317623
83	33	102	P2RX1	ENSG00000108405	2033.50	2033.50	1	8.323049e-01	0.019317623
84	62	157	RPS24	ENSG00000138326	2107.90	2107.90	1	3.278049e+00	0.019317623
85	68	29	CTDSPL	ENSG00000144677	2004.85	2004.85	1	4.546248e+01	0.019317623
87	86	175	SPINT2	ENSG00000167642	2031.70	2031.70	1	3.853579e+00	0.019317623
88	105	15	CD24	ENSG00000272398	2074.25	2074.25	1	1.000000e+00	0.019317623
92	11	137	RPL31	ENSG00000071082	1861.05	1861.05	1	1.885339e+00	0.027682843
100	27	93	MYL9	ENSG00000101335	1660.70	1660.70	1	1.831732e+00	0.039211110
101	64	163	RPS8	ENSG00000142937	1640.05	1640.05	1	2.623947e+00	0.039211110
105	80	105	PF4	ENSG00000163737	1573.50	1573.50	1	1.266317e+01	0.045112782
111	10	96	NFE2	ENSG00000123405	-5000.50	-5000.50	1	5.871481e-02	0.000000000
117	24	61	HBA2	ENSG00000188536	-9245.35	-9245.35	1	2.053707e-13	0.000000000
119	26	146	RPL41	ENSG00000229117	-4967.60	-4967.60	1	6.197302e-02	0.000000000
121	28	62	HBB	ENSG00000244734	-11411.90	-11411.90	1	6.838737e-15	0.000000000
124	1	111	PRKAR2B	ENSG00000005249	-3596.25	-3596.25	1	1.319292e-01	0.008105826
127	5	197	TUBB1	ENSG00000101162	-2955.35	-2955.35	1	3.003076e-01	0.046113853
129	18	179	TAL1	ENSG00000162367	-3051.65	-3051.65	1	3.256530e-03	0.046113853
130	17	37	EEF1A1	ENSG00000156508	-2807.45	-2807.45	1	6.570624e-01	0.046374678

[CL1 vs CL3 DIscBIO] vs [CL2 vs CL4 Granatum]¶

In [19]:

#################  [CL1 vs CL3  DIscBIO]   vs   [CL2 vs CL4 Granatum]
dData<-genes1vs3
cat(paste0("There are  ", length(dData), " DEGs detected by DIscBIO in CL1 vs CL3","\n"))
target=G_DEGs$`2 vs. 4`
S1<-subset(target,target[,6]>1.96)
targetGeneList<-S1[,2]
cat(paste0("There are  ", length(targetGeneList), " DEGs detected by Granatum in CL2 vs CL4","\n"))

gData<-targetGeneList
idx_genes <- is.element(dData,gData)
SharedGenes<-dData[idx_genes]   
cat(paste0("There are  ", length(SharedGenes), " DEGs shared between DIscBIO and Granatum","\n"))
df2[idx_genes,]

There are  169 DEGs detected by DIscBIO in CL1 vs CL3
There are  220 DEGs detected by Granatum in CL2 vs CL4
There are  74 DEGs shared between DIscBIO and Granatum

A data.frame: 74 × 9
	X	Row	Gene.ID	Gene.Name	Score.d.	Numerator.r.	Denominator.s.s0.	Fold.Change	q.value...
	<int>	<int>	<chr>	<chr>	<dbl>	<dbl>	<int>	<dbl>	<dbl>
6	6	120	RAB27B	ENSG00000041353	2758.80	2758.80	1	2.474279e+02	0
7	7	173	SLC2A3	ENSG00000059804	3465.85	3465.85	1	2.196246e+01	0
11	11	123	RASGRP2	ENSG00000068831	4991.70	4991.70	1	1.321138e+03	0
18	18	32	CXCL2	ENSG00000081041	4867.25	4867.25	1	1.460625e+10	0
21	21	185	TMEM40	ENSG00000088726	3064.70	3064.70	1	2.722438e+01	0
22	22	117	PTGS1	ENSG00000095303	2287.65	2287.65	1	1.466796e+01	0
27	27	209	ZMYND8	ENSG00000101040	3522.50	3522.50	1	5.818668e+01	0
28	28	93	MYL9	ENSG00000101335	2227.40	2227.40	1	4.668762e+00	0
31	31	27	COTL1	ENSG00000103187	1744.85	1744.85	1	8.397662e+00	0
32	32	180	TGFB1	ENSG00000105329	4386.05	4386.05	1	4.793467e+02	0
33	33	171	SERPINE1	ENSG00000106366	4648.15	4648.15	1	3.591061e+02	0
35	35	102	P2RX1	ENSG00000108405	4160.45	4160.45	1	6.189161e+02	0
37	37	2	ABCC3	ENSG00000108846	4228.50	4228.50	1	2.113856e+01	0
41	41	3	ACRBP	ENSG00000111644	4966.35	4966.35	1	1.067672e+02	0
44	44	203	WIPF1	ENSG00000115935	2119.80	2119.80	1	4.727605e+00	0
45	45	108	PLEK	ENSG00000115956	3254.65	3254.65	1	1.144383e+02	0
63	63	190	TPST2	ENSG00000128294	3179.80	3179.80	1	5.660956e+01	0
68	70	129	RHOB	ENSG00000143878	3997.00	3997.00	1	5.062392e+09	0
69	71	29	CTDSPL	ENSG00000144677	2874.90	2874.90	1	2.353335e+02	0
73	75	128	RGS18	ENSG00000150681	2347.50	2347.50	1	4.661234e+01	0
75	77	34	LOC112267931	ENSG00000153071	2838.90	2838.90	1	2.982788e+01	0
76	78	34	DAB2	ENSG00000153071	2838.90	2838.90	1	2.982788e+01	0
77	79	24	CMIP	ENSG00000153815	5474.65	5474.65	1	2.483892e+02	0
78	80	98	NRGN	ENSG00000154146	4446.95	4446.95	1	2.886860e+01	0
79	81	194	TSPAN33	ENSG00000158457	2499.30	2499.30	1	3.919939e+01	0
80	82	116	PTGIR	ENSG00000160013	5289.90	5289.90	1	1.778308e+04	0
83	85	179	TAL1	ENSG00000162367	1701.05	1701.05	1	2.053967e+00	0
84	86	95	NEXN	ENSG00000162614	4813.45	4813.45	1	2.971277e+02	0
85	87	25	CNST	ENSG00000162852	4021.10	4021.10	1	7.652394e+01	0
86	88	33	CXCL3	ENSG00000163734	5185.55	5185.55	1	1.500979e+10	0
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮
120	25	154	RPS2	ENSG00000140988	-3084.85	-3084.85	1	2.305001e-10	0.000000000
123	32	138	RPL32	ENSG00000144713	-2782.45	-2782.45	1	3.312366e-10	0.000000000
125	38	136	RPL30	ENSG00000156482	-2174.15	-2174.15	1	4.560216e-10	0.000000000
126	39	37	EEF1A1	ENSG00000156508	-3954.45	-3954.45	1	1.012268e-10	0.000000000
127	42	152	RPS14	ENSG00000164587	-2651.75	-2651.75	1	1.895682e-10	0.000000000
129	46	132	RPL13	ENSG00000167526	-2111.90	-2111.90	1	1.008847e-01	0.000000000
133	54	61	HBA2	ENSG00000188536	-5112.95	-5112.95	1	9.264138e-04	0.000000000
135	60	146	RPL41	ENSG00000229117	-3975.05	-3975.05	1	6.346323e-11	0.000000000
136	63	62	HBB	ENSG00000244734	-5863.00	-5863.00	1	5.203202e-14	0.000000000
138	18	153	RPS15A	ENSG00000134419	-1758.40	-1758.40	1	5.676041e-10	0.001618351
139	22	157	RPS24	ENSG00000138326	-1831.85	-1831.85	1	6.252795e-10	0.001618351
140	29	163	RPS8	ENSG00000142937	-2039.45	-2039.45	1	4.317256e-10	0.001618351
141	35	160	RPS3	ENSG00000149273	-1977.05	-1977.05	1	5.977778e-10	0.001618351
142	15	151	RPS12	ENSG00000112306	-1579.35	-1579.35	1	1.719020e-01	0.003121925
143	19	140	RPL35	ENSG00000136942	-1458.85	-1458.85	1	1.392181e-09	0.003121925
144	53	156	RPS23	ENSG00000186468	-1603.25	-1603.25	1	1.106718e-09	0.003121925
146	57	161	RPS4X	ENSG00000198034	-1353.95	-1353.95	1	1.095426e-09	0.004585327
147	52	149	RPLP2	ENSG00000177600	-1311.15	-1311.15	1	1.259745e-01	0.006030019
151	28	131	RPL11	ENSG00000142676	-1184.75	-1184.75	1	2.079297e-01	0.009474318
153	55	143	RPL37A	ENSG00000197756	-1171.75	-1171.75	1	7.668770e-10	0.009474318
158	50	144	RPL38	ENSG00000172809	-903.40	-903.40	1	1.491366e-09	0.020895161
159	62	159	RPS28	ENSG00000233927	-895.10	-895.10	1	2.541230e-09	0.020895161
160	49	77	KRT19	ENSG00000171345	-880.05	-880.05	1	1.000000e+00	0.022147994
161	56	165	S100A6	ENSG00000197956	-823.20	-823.20	1	1.065952e-08	0.024760766
162	16	134	RPL23	ENSG00000125691	-792.85	-792.85	1	1.107619e-09	0.027341080
163	33	142	RPL37	ENSG00000145592	-780.45	-780.45	1	1.997642e-09	0.027341080
165	41	167	S100A9	ENSG00000163220	-736.15	-736.15	1	3.213370e-08	0.032209126
166	58	145	RPL39	ENSG00000198918	-714.20	-714.20	1	1.330130e-09	0.033347832
168	12	139	RPL34	ENSG00000109475	-592.10	-592.10	1	5.934631e-01	0.047445778
169	13	76	KRT18	ENSG00000111057	-568.55	-568.55	1	6.042083e-08	0.049783550

[CL1 vs CL4 DIscBIO] vs [CL1 vs CL2 Granatum]¶

In [20]:

#################  [CL1 vs CL4  DIscBIO]   vs   [CL1 vs CL2 Granatum]
dData<-genes1vs4
cat(paste0("There are  ", length(dData), " DEGs detected by DIscBIO in CL1 vs CL4","\n"))
target=G_DEGs$`1 vs. 2`
S1<-subset(target,target[,6]>1.96)
targetGeneList<-S1[,2]
cat(paste0("There are  ", length(targetGeneList), " DEGs detected by Granatum in CL1 vs CL2","\n"))

gData<-targetGeneList
idx_genes <- is.element(dData,gData)
SharedGenes<-dData[idx_genes]   
cat(paste0("There are  ", length(SharedGenes), " DEGs shared between DIscBIO and Granatum","\n"))
df1[idx_genes,]

There are  180 DEGs detected by DIscBIO in CL1 vs CL4
There are  218 DEGs detected by Granatum in CL1 vs CL2
There are  71 DEGs shared between DIscBIO and Granatum

A data.frame: 71 × 9
	X	Row	Gene.ID	Gene.Name	Score.d.	Numerator.r.	Denominator.s.s0.	Fold.Change	q.value...
	<int>	<int>	<chr>	<chr>	<dbl>	<dbl>	<int>	<dbl>	<dbl>
5	6	120	RAB27B	ENSG00000041353	709.75	709.75	1	4.234006e+02	0
6	7	173	SLC2A3	ENSG00000059804	783.45	783.45	1	2.166678e+01	0
16	18	32	CXCL2	ENSG00000081041	970.20	970.20	1	3.420071e+10	0
19	21	185	TMEM40	ENSG00000088726	755.60	755.60	1	3.651309e+01	0
20	22	117	PTGS1	ENSG00000095303	550.10	550.10	1	1.326978e+01	0
24	27	209	ZMYND8	ENSG00000101040	1030.50	1030.50	1	1.082250e+02	0
25	33	180	TGFB1	ENSG00000105329	848.00	848.00	1	1.890163e+02	0
26	34	171	SERPINE1	ENSG00000106366	1048.35	1048.35	1	4.865769e+02	0
28	36	102	P2RX1	ENSG00000108405	978.40	978.40	1	8.097260e+02	0
30	38	2	ABCC3	ENSG00000108846	966.25	966.25	1	4.349073e+01	0
33	43	3	ACRBP	ENSG00000111644	1036.45	1036.45	1	9.214942e+01	0
35	47	108	PLEK	ENSG00000115956	475.50	475.50	1	7.177538e+01	0
48	66	190	TPST2	ENSG00000128294	636.50	636.50	1	6.178545e+01	0
52	72	129	RHOB	ENSG00000143878	590.60	590.60	1	6.454313e+09	0
53	73	29	CTDSPL	ENSG00000144677	623.55	623.55	1	3.459644e+02	0
57	77	128	RGS18	ENSG00000150681	534.75	534.75	1	7.042786e+01	0
59	79	34	DAB2	ENSG00000153071	880.05	880.05	1	5.215560e+01	0
60	80	34	LOC112267931	ENSG00000153071	880.05	880.05	1	5.215560e+01	0
61	81	24	CMIP	ENSG00000153815	1166.50	1166.50	1	3.061276e+02	0
62	82	98	NRGN	ENSG00000154146	747.55	747.55	1	2.132968e+01	0
63	84	116	PTGIR	ENSG00000160013	1170.00	1170.00	1	2.027628e+04	0
66	87	179	TAL1	ENSG00000162367	485.05	485.05	1	2.839989e+00	0
67	88	95	NEXN	ENSG00000162614	1047.35	1047.35	1	4.249000e+02	0
68	89	25	CNST	ENSG00000162852	1067.95	1067.95	1	1.138253e+02	0
69	90	33	CXCL3	ENSG00000163734	1073.80	1073.80	1	3.596989e+10	0
70	91	105	PF4	ENSG00000163737	701.70	701.70	1	1.565087e+01	0
71	92	23	CLEC1B	ENSG00000165682	788.80	788.80	1	1.647951e+02	0
72	93	112	PRKCB	ENSG00000166501	605.05	605.05	1	1.800988e+01	0
77	100	115	PTCRA	ENSG00000171611	937.95	937.95	1	7.983915e+02	0
78	101	169	SELP	ENSG00000174175	677.30	677.30	1	2.942648e+02	0
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮
120	24	153	RPS15A	ENSG00000134419	-555.95	-555.95	1	1.836601e-10	0.000000000
121	26	162	RPS6	ENSG00000137154	-750.10	-750.10	1	7.074152e-11	0.000000000
122	28	157	RPS24	ENSG00000138326	-561.00	-561.00	1	2.156889e-10	0.000000000
124	31	154	RPS2	ENSG00000140988	-808.75	-808.75	1	9.546738e-11	0.000000000
127	35	163	RPS8	ENSG00000142937	-629.10	-629.10	1	1.677663e-10	0.000000000
128	38	138	RPL32	ENSG00000144713	-730.50	-730.50	1	1.151961e-10	0.000000000
129	41	160	RPS3	ENSG00000149273	-518.05	-518.05	1	2.064880e-10	0.000000000
131	45	37	EEF1A1	ENSG00000156508	-951.05	-951.05	1	4.759329e-11	0.000000000
132	49	152	RPS14	ENSG00000164587	-770.55	-770.55	1	6.673755e-11	0.000000000
138	60	149	RPLP2	ENSG00000177600	-510.65	-510.65	1	1.678711e-10	0.000000000
139	62	61	HBA2	ENSG00000188536	-1129.25	-1129.25	1	4.363605e-13	0.000000000
141	69	146	RPL41	ENSG00000229117	-848.30	-848.30	1	3.210878e-11	0.000000000
142	72	62	HBB	ENSG00000244734	-1275.00	-1275.00	1	2.547064e-14	0.000000000
144	25	140	RPL35	ENSG00000136942	-450.65	-450.65	1	5.816038e-10	0.001515152
147	64	143	RPL37A	ENSG00000197756	-462.80	-462.80	1	2.447609e-10	0.001515152
148	66	161	RPS4X	ENSG00000198034	-464.00	-464.00	1	6.074866e-10	0.001515152
150	34	131	RPL11	ENSG00000142676	-442.30	-442.30	1	3.104817e-10	0.002754821
151	39	142	RPL37	ENSG00000145592	-445.05	-445.05	1	7.482142e-10	0.002754821
152	44	136	RPL30	ENSG00000156482	-435.75	-435.75	1	1.454757e-10	0.002754821
153	67	145	RPL39	ENSG00000198918	-434.45	-434.45	1	5.169295e-10	0.002754821
155	22	134	RPL23	ENSG00000125691	-371.75	-371.75	1	4.618991e-10	0.006226650
156	48	167	S100A9	ENSG00000163220	-376.90	-376.90	1	9.093490e-09	0.006226650
157	61	156	RPS23	ENSG00000186468	-391.05	-391.05	1	4.682182e-10	0.006226650
159	58	144	RPL38	ENSG00000172809	-363.45	-363.45	1	5.035793e-10	0.007224564
160	65	165	S100A6	ENSG00000197956	-367.50	-367.50	1	3.527907e-09	0.007224564
161	3	186	TMSB10	ENSG00000034510	-352.55	-352.55	1	2.615475e-10	0.009506833
163	71	159	RPS28	ENSG00000233927	-357.45	-357.45	1	1.285717e-09	0.009506833
165	17	139	RPL34	ENSG00000109475	-326.30	-326.30	1	4.182641e-10	0.013551666
174	56	77	KRT19	ENSG00000171345	-249.10	-249.10	1	1.000000e+00	0.034355180
176	2	16	CD74	ENSG00000019582	-210.55	-210.55	1	9.238000e-09	0.042597403

[CL2 vs CL3 DIscBIO] vs [CL3 vs CL4 Granatum]¶

In [21]:

#################  [CL2 vs CL3  DIscBIO]   vs   [CL3 vs CL4 Granatum]
dData<-genes3vs2
cat(paste0("There are  ", length(dData), " DEGs detected by DIscBIO in CL2 vs CL3","\n"))
target=G_DEGs$`3 vs. 4`
S1<-subset(target,target[,6]>1.96)
targetGeneList<-S1[,2]
cat(paste0("There are  ", length(targetGeneList), " DEGs detected by Granatum in CL3 vs CL4","\n"))

gData<-targetGeneList
idx_genes <- is.element(dData,gData)
SharedGenes<-dData[idx_genes]   
cat(paste0("There are  ", length(SharedGenes), " DEGs shared between DIscBIO and Granatum","\n"))
df6[idx_genes,]

There are  187 DEGs detected by DIscBIO in CL2 vs CL3
There are  227 DEGs detected by Granatum in CL3 vs CL4
There are  84 DEGs shared between DIscBIO and Granatum

A data.frame: 84 × 9
	X	Row	Gene.ID	Gene.Name	Score.d.	Numerator.r.	Denominator.s.s0.	Fold.Change	q.value...
	<int>	<int>	<chr>	<chr>	<dbl>	<dbl>	<int>	<dbl>	<dbl>
3	3	16	CD74	ENSG00000019582	37390.75	37390.75	1	1.276170e+07	0
4	4	186	TMSB10	ENSG00000034510	118100.80	118100.80	1	2.046809e+01	0
5	5	137	RPL31	ENSG00000071082	101610.85	101610.85	1	2.811637e+10	0
10	11	147	RPLP0	ENSG00000089157	102865.45	102865.45	1	2.017511e+10	0
15	16	65	HSPB1	ENSG00000106211	51118.50	51118.50	1	3.586486e+00	0
16	17	133	RPL19	ENSG00000108298	103917.35	103917.35	1	3.213299e+10	0
18	20	139	RPL34	ENSG00000109475	108582.25	108582.25	1	1.445390e+01	0
20	22	76	KRT18	ENSG00000111057	66571.05	66571.05	1	1.000000e+00	0
24	26	151	RPS12	ENSG00000112306	104815.45	104815.45	1	1.463882e+01	0
26	28	125	RBM25	ENSG00000119707	73668.35	73668.35	1	4.812357e+00	0
27	30	134	RPL23	ENSG00000125691	102181.00	102181.00	1	2.504398e+10	0
29	32	153	RPS15A	ENSG00000134419	86686.60	86686.60	1	2.359767e+10	0
31	34	140	RPL35	ENSG00000136942	112083.75	112083.75	1	2.904328e+10	0
32	35	162	RPS6	ENSG00000137154	111048.50	111048.50	1	3.221255e+10	0
34	37	157	RPS24	ENSG00000138326	103377.10	103377.10	1	2.110131e+10	0
37	40	154	RPS2	ENSG00000140988	102108.90	102108.90	1	1.958954e+10	0
39	43	131	RPL11	ENSG00000142676	106580.80	106580.80	1	2.633835e+01	0
40	44	163	RPS8	ENSG00000142937	103431.70	103431.70	1	2.989110e+10	0
44	48	138	RPL32	ENSG00000144713	99663.35	99663.35	1	2.001914e+10	0
45	49	142	RPL37	ENSG00000145592	107250.30	107250.30	1	3.035933e+10	0
46	50	160	RPS3	ENSG00000149273	95671.45	95671.45	1	3.692245e+10	0
49	53	136	RPL30	ENSG00000156482	116757.30	116757.30	1	4.719534e+10	0
50	54	37	EEF1A1	ENSG00000156508	98587.10	98587.10	1	2.724679e+10	0
51	56	167	S100A9	ENSG00000163220	49002.25	49002.25	1	1.986674e+09	0
52	57	200	UQCRQ	ENSG00000164405	84755.70	84755.70	1	1.258542e+10	0
53	58	152	RPS14	ENSG00000164587	108701.80	108701.80	1	4.217950e+10	0
56	62	132	RPL13	ENSG00000167526	110042.30	110042.30	1	2.033392e+01	0
57	64	175	SPINT2	ENSG00000167642	45330.50	45330.50	1	1.940608e+00	0
60	67	78	KRT8	ENSG00000170421	65415.15	65415.15	1	1.000000e+00	0
61	68	77	KRT19	ENSG00000171345	56479.35	56479.35	1	1.000000e+00	0
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮
120	36	2	ABCC3	ENSG00000108846	-104990.35	-104990.35	1	2.436534e-01	0
121	37	89	MMD	ENSG00000108960	-110236.75	-110236.75	1	3.919065e-02	0
124	40	3	ACRBP	ENSG00000111644	-132886.90	-132886.90	1	1.681374e-01	0
126	42	174	SPARC	ENSG00000113140	-90274.65	-90274.65	1	2.938913e-01	0
129	45	203	WIPF1	ENSG00000115935	-78992.25	-78992.25	1	5.531183e-02	0
130	46	108	PLEK	ENSG00000115956	-86365.15	-86365.15	1	1.985816e-01	0
134	50	96	NFE2	ENSG00000123405	-112208.90	-112208.90	1	5.840605e-02	0
148	64	190	TPST2	ENSG00000128294	-90372.50	-90372.50	1	1.927890e-01	0
149	65	19	CDKN2D	ENSG00000129355	-69355.65	-69355.65	1	3.988070e-01	0
152	68	29	CTDSPL	ENSG00000144677	-82773.30	-82773.30	1	2.270036e-01	0
158	74	128	RGS18	ENSG00000150681	-134443.50	-134443.50	1	5.033117e-02	0
160	76	34	LOC112267931	ENSG00000153071	-123611.80	-123611.80	1	2.037815e-01	0
161	77	34	DAB2	ENSG00000153071	-123611.80	-123611.80	1	2.037815e-01	0
162	78	24	CMIP	ENSG00000153815	-131448.90	-131448.90	1	1.199139e-01	0
163	79	98	NRGN	ENSG00000154146	-70993.40	-70993.40	1	2.232909e-01	0
164	80	194	TSPAN33	ENSG00000158457	-72248.25	-72248.25	1	8.830967e-02	0
165	81	116	PTGIR	ENSG00000160013	-141914.10	-141914.10	1	1.696344e-11	0
166	82	179	TAL1	ENSG00000162367	-109007.35	-109007.35	1	1.680974e-03	0
167	83	95	NEXN	ENSG00000162614	-132138.00	-132138.00	1	8.478831e-02	0
168	84	25	CNST	ENSG00000162852	-134592.85	-134592.85	1	1.176714e-01	0
170	86	109	PPBP	ENSG00000163736	-80498.40	-80498.40	1	2.163328e-01	0
171	87	105	PF4	ENSG00000163737	-116185.25	-116185.25	1	1.619752e-01	0
172	88	23	CLEC1B	ENSG00000165682	-104428.35	-104428.35	1	3.666309e-11	0
173	89	112	PRKCB	ENSG00000166501	-72170.50	-72170.50	1	8.370585e-02	0
178	94	115	PTCRA	ENSG00000171611	-124379.05	-124379.05	1	3.923883e-02	0
179	95	169	SELP	ENSG00000174175	-101903.35	-101903.35	1	3.912312e-11	0
180	96	164	RUFY1	ENSG00000176783	-130323.75	-130323.75	1	2.048110e-01	0
181	97	22	CLDN5	ENSG00000184113	-100336.95	-100336.95	1	4.095819e-11	0
184	100	104	PEAR1	ENSG00000187800	-111884.75	-111884.75	1	3.603943e-11	0
185	101	103	PARVB	ENSG00000188677	-44367.00	-44367.00	1	1.039726e-02	0

[CL2 vs CL4 DIscBIO] vs [CL1 vs CL3 Granatum]¶

In [22]:

#################  [CL2 vs CL4  DIscBIO]   vs   [CL1 vs CL3 Granatum]
dData<-genes2vs4
cat(paste0("There are  ", length(dData), " DEGs detected by DIscBIO in CL2 vs CL4","\n"))
target=G_DEGs$`1 vs. 3`
S1<-subset(target,target[,6]>1.96)
targetGeneList<-S1[,2]
length(targetGeneList)
cat(paste0("There are  ", length(targetGeneList), " DEGs detected by Granatum in CL1 vs CL3","\n"))

gData<-targetGeneList
idx_genes <- is.element(dData,gData)
SharedGenes<-dData[idx_genes]   
cat(paste0("There are  ", length(SharedGenes), " DEGs shared between DIscBIO and Granatum","\n"))
df5[idx_genes,]

There are  200 DEGs detected by DIscBIO in CL2 vs CL4

229

There are  229 DEGs detected by Granatum in CL1 vs CL3
There are  89 DEGs shared between DIscBIO and Granatum

A data.frame: 89 × 9
	X	Row	Gene.ID	Gene.Name	Score.d.	Numerator.r.	Denominator.s.s0.	Fold.Change	q.value...
	<int>	<int>	<chr>	<chr>	<dbl>	<dbl>	<int>	<dbl>	<dbl>
2	2	111	PRKAR2B	ENSG00000005249	22543.25	22543.25	1	1.186259e+01	0
6	6	120	RAB27B	ENSG00000041353	29245.10	29245.10	1	1.746580e+01	0
7	7	173	SLC2A3	ENSG00000059804	27416.95	27416.95	1	1.403941e+01	0
9	11	123	RASGRP2	ENSG00000068831	28195.00	28195.00	1	4.964944e+10	0
14	17	32	CXCL2	ENSG00000081041	20556.70	20556.70	1	1.399085e+01	0
17	20	185	TMEM40	ENSG00000088726	29389.70	29389.70	1	8.580335e+00	0
18	21	117	PTGS1	ENSG00000095303	19955.70	19955.70	1	7.306088e+02	0
22	25	209	ZMYND8	ENSG00000101040	35899.25	35899.25	1	1.129910e+01	0
23	26	197	TUBB1	ENSG00000101162	27873.85	27873.85	1	6.575802e+00	0
28	33	180	TGFB1	ENSG00000105329	15159.10	15159.10	1	3.522660e+01	0
29	34	171	SERPINE1	ENSG00000106366	27333.85	27333.85	1	4.898394e+10	0
31	36	102	P2RX1	ENSG00000108405	22605.45	22605.45	1	1.176286e+03	0
33	38	2	ABCC3	ENSG00000108846	25673.15	25673.15	1	4.949330e+00	0
35	40	89	MMD	ENSG00000108960	25046.55	25046.55	1	3.159345e+01	0
38	43	3	ACRBP	ENSG00000111644	30869.40	30869.40	1	6.953711e+00	0
39	45	174	SPARC	ENSG00000113140	16556.10	16556.10	1	3.402706e+00	0
42	48	203	WIPF1	ENSG00000115935	11263.90	11263.90	1	1.408732e+01	0
43	49	108	PLEK	ENSG00000115956	9893.40	9893.40	1	2.727689e+00	0
47	53	96	NFE2	ENSG00000123405	24496.65	24496.65	1	1.646400e+01	0
60	67	190	TPST2	ENSG00000128294	18676.10	18676.10	1	5.065305e+00	0
61	68	19	CDKN2D	ENSG00000129355	7647.20	7647.20	1	1.733864e+00	0
63	73	29	CTDSPL	ENSG00000144677	17305.05	17305.05	1	4.931069e+00	0
69	79	128	RGS18	ENSG00000150681	30705.15	30705.15	1	2.102980e+01	0
71	81	34	LOC112267931	ENSG00000153071	35375.90	35375.90	1	8.182020e+00	0
72	82	34	DAB2	ENSG00000153071	35375.90	35375.90	1	8.182020e+00	0
73	83	24	CMIP	ENSG00000153815	30106.75	30106.75	1	1.014572e+01	0
74	86	194	TSPAN33	ENSG00000158457	10503.30	10503.30	1	7.992067e+00	0
75	87	116	PTGIR	ENSG00000160013	33131.25	33131.25	1	9.469038e+10	0
76	88	179	TAL1	ENSG00000162367	25701.00	25701.00	1	6.099516e+02	0
77	89	95	NEXN	ENSG00000162614	29794.10	29794.10	1	1.510661e+01	0
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮
149	45	131	RPL11	ENSG00000142676	-29019.05	-29019.05	1	1.888300e-11	0.0000000000
150	46	163	RPS8	ENSG00000142937	-27047.50	-27047.50	1	2.524482e-11	0.0000000000
154	51	138	RPL32	ENSG00000144713	-25719.75	-25719.75	1	3.770373e-11	0.0000000000
155	52	142	RPL37	ENSG00000145592	-28853.20	-28853.20	1	2.467664e-11	0.0000000000
156	53	160	RPS3	ENSG00000149273	-24159.05	-24159.05	1	2.031353e-11	0.0000000000
159	56	136	RPL30	ENSG00000156482	-26068.10	-26068.10	1	1.585845e-11	0.0000000000
160	57	37	EEF1A1	ENSG00000156508	-24720.25	-24720.25	1	2.764802e-11	0.0000000000
163	60	167	S100A9	ENSG00000163220	-17371.90	-17371.90	1	3.760863e-10	0.0000000000
164	61	200	UQCRQ	ENSG00000164405	-21296.00	-21296.00	1	6.019143e-11	0.0000000000
165	62	152	RPS14	ENSG00000164587	-28910.10	-28910.10	1	1.778626e-11	0.0000000000
168	66	132	RPL13	ENSG00000167526	-28930.90	-28930.90	1	1.460993e-11	0.0000000000
170	68	175	SPINT2	ENSG00000167642	-11562.85	-11562.85	1	4.906858e-01	0.0000000000
172	71	78	KRT8	ENSG00000170421	-15208.80	-15208.80	1	1.000000e+00	0.0000000000
173	72	77	KRT19	ENSG00000171345	-10802.95	-10802.95	1	1.000000e+00	0.0000000000
175	74	144	RPL38	ENSG00000172809	-26287.15	-26287.15	1	2.189857e-11	0.0000000000
177	76	149	RPLP2	ENSG00000177600	-31738.60	-31738.60	1	1.166993e-11	0.0000000000
179	78	208	ZFP36L1	ENSG00000185650	-11437.30	-11437.30	1	5.310779e-02	0.0000000000
180	79	156	RPS23	ENSG00000186468	-22737.50	-22737.50	1	3.384936e-11	0.0000000000
182	83	143	RPL37A	ENSG00000197756	-29203.95	-29203.95	1	1.406148e-11	0.0000000000
183	84	165	S100A6	ENSG00000197956	-20092.75	-20092.75	1	7.252787e-11	0.0000000000
184	85	161	RPS4X	ENSG00000198034	-28989.55	-28989.55	1	1.695178e-11	0.0000000000
185	86	145	RPL39	ENSG00000198918	-22480.90	-22480.90	1	6.220205e-11	0.0000000000
186	89	146	RPL41	ENSG00000229117	-13859.25	-13859.25	1	1.507607e-10	0.0000000000
187	90	159	RPS28	ENSG00000233927	-20285.25	-20285.25	1	7.675815e-11	0.0000000000
188	92	15	CD24	ENSG00000272398	-12554.20	-12554.20	1	1.000000e+00	0.0000000000
190	15	93	MYL9	ENSG00000101335	-6214.00	-6214.00	1	7.779018e-01	0.0006015038
194	49	129	RHOB	ENSG00000143878	-5861.45	-5861.45	1	6.254779e-01	0.0011567380
198	81	103	PARVB	ENSG00000188677	-3840.15	-3840.15	1	6.586732e-09	0.0104712042
199	80	61	HBA2	ENSG00000188536	-3673.45	-3673.45	1	1.000000e+00	0.0109649123
200	91	62	HBB	ENSG00000244734	-3557.45	-3557.45	1	1.000000e+00	0.0143787303

[CL3 vs CL4 DIscBIO] vs [CL1 vs CL4 Granatum]¶

In [23]:

#################  [CL3 vs CL4  DIscBIO]   vs   [CL1 vs CL4 Granatum]
dData<-genes3vs4
length(dData)
cat(paste0("There are  ", length(dData), " DEGs detected by DIscBIO in CL3 vs CL4","\n"))
target=G_DEGs$`1 vs. 4`
S1<-subset(target,target[,6]>1.96)
targetGeneList<-S1[,2]
length(targetGeneList)

gData<-targetGeneList
idx_genes <- is.element(dData,gData)
SharedGenes<-dData[idx_genes]   
cat(paste0("There are  ", length(SharedGenes), " DEGs shared between DIscBIO and Granatum","\n"))
df4[idx_genes,]

149

There are  149 DEGs detected by DIscBIO in CL3 vs CL4

211

There are  61 DEGs shared between DIscBIO and Granatum

A data.frame: 61 × 9
	X	Row	Gene.ID	Gene.Name	Score.d.	Numerator.r.	Denominator.s.s0.	Fold.Change	q.value...
	<int>	<int>	<chr>	<chr>	<dbl>	<dbl>	<int>	<dbl>	<dbl>
2	4	186	TMSB10	ENSG00000034510	5787.45	5787.45	1	5.118726e+08	0.000000000
4	6	137	RPL31	ENSG00000071082	3653.85	3653.85	1	1.000000e+00	0.000000000
11	18	93	MYL9	ENSG00000101335	7843.65	7843.65	1	3.111100e+00	0.000000000
14	21	27	COTL1	ENSG00000103187	6423.20	6423.20	1	2.764530e+00	0.000000000
15	22	180	TGFB1	ENSG00000105329	5538.60	5538.60	1	1.356179e+00	0.000000000
16	23	65	HSPB1	ENSG00000106211	3739.40	3739.40	1	3.559201e+08	0.000000000
21	30	151	RPS12	ENSG00000112306	4115.60	4115.60	1	2.973225e+08	0.000000000
22	33	203	WIPF1	ENSG00000115935	3760.60	3760.60	1	1.275972e+00	0.000000000
23	34	108	PLEK	ENSG00000115956	5890.40	5890.40	1	1.902519e+00	0.000000000
32	46	19	CDKN2D	ENSG00000129355	4997.60	4997.60	1	1.472511e+00	0.000000000
40	61	131	RPL11	ENSG00000142676	4305.40	4305.40	1	1.316091e+08	0.000000000
41	64	129	RHOB	ENSG00000143878	4253.35	4253.35	1	2.211886e+00	0.000000000
44	73	98	NRGN	ENSG00000154146	8730.90	8730.90	1	1.474058e+00	0.000000000
45	75	194	TSPAN33	ENSG00000158457	4080.20	4080.20	1	1.426462e+00	0.000000000
48	80	167	S100A9	ENSG00000163220	3737.05	3737.05	1	1.000000e+00	0.000000000
49	81	109	PPBP	ENSG00000163736	8799.60	8799.60	1	1.488359e+00	0.000000000
50	82	105	PF4	ENSG00000163737	5103.65	5103.65	1	1.276117e+00	0.000000000
51	83	152	RPS14	ENSG00000164587	4128.30	4128.30	1	1.000000e+00	0.000000000
58	97	103	PARVB	ENSG00000188677	7196.65	7196.65	1	9.329993e+08	0.000000000
60	99	143	RPL37A	ENSG00000197756	3263.35	3263.35	1	1.000000e+00	0.000000000
61	100	161	RPS4X	ENSG00000198034	3988.30	3988.30	1	1.000000e+00	0.000000000
62	101	145	RPL39	ENSG00000198918	3670.05	3670.05	1	1.000000e+00	0.000000000
68	26	139	RPL34	ENSG00000109475	2775.55	2775.55	1	2.299543e+08	0.002921179
69	35	125	RBM25	ENSG00000119707	3100.35	3100.35	1	4.456822e+08	0.002921179
74	66	142	RPL37	ENSG00000145592	3108.65	3108.65	1	1.000000e+00	0.002921179
78	86	132	RPL13	ENSG00000167526	2907.60	2907.60	1	2.284005e+08	0.002921179
79	51	153	RPS15A	ENSG00000134419	2735.10	2735.10	1	1.000000e+00	0.005495286
80	94	149	RPLP2	ENSG00000177600	2737.85	2737.85	1	1.124757e+08	0.005495286
81	105	159	RPS28	ENSG00000233927	2741.90	2741.90	1	1.000000e+00	0.005495286
82	3	16	CD74	ENSG00000019582	2445.75	2445.75	1	1.000000e+00	0.009911141
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮
87	96	22	CLDN5	ENSG00000184113	2537.50	2537.50	1	1.1250731	0.009911141
88	62	163	RPS8	ENSG00000142937	2399.65	2399.65	1	1.0000000	0.011051361
89	12	32	CXCL2	ENSG00000081041	2367.35	2367.35	1	1.3141261	0.011961722
90	57	157	RPS24	ENSG00000138326	2327.80	2327.80	1	1.0000000	0.011961722
92	24	133	RPL19	ENSG00000108298	2248.55	2248.55	1	1.0000000	0.013875598
93	54	162	RPS6	ENSG00000137154	2291.65	2291.65	1	1.0000000	0.013875598
95	13	147	RPLP0	ENSG00000089157	2008.55	2008.55	1	1.0000000	0.023302531
97	53	140	RPL35	ENSG00000136942	1991.45	1991.45	1	1.0000000	0.023302531
98	69	160	RPS3	ENSG00000149273	2011.85	2011.85	1	1.0000000	0.023302531
99	74	37	EEF1A1	ENSG00000156508	1935.10	1935.10	1	1.0000000	0.025228360
100	65	138	RPL32	ENSG00000144713	1888.80	1888.80	1	1.0000000	0.026723374
102	92	144	RPL38	ENSG00000172809	1873.65	1873.65	1	1.0000000	0.026723374
109	13	209	ZMYND8	ENSG00000101040	-9178.50	-9178.50	1	0.5168245	0.000000000
116	32	34	LOC112267931	ENSG00000153071	-9238.75	-9238.75	1	0.6043628	0.000000000
117	33	34	DAB2	ENSG00000153071	-9238.75	-9238.75	1	0.6043628	0.000000000
118	37	25	CNST	ENSG00000162852	-7534.95	-7534.95	1	0.7097117	0.000000000
119	38	23	CLEC1B	ENSG00000165682	-5376.95	-5376.95	1	0.6274743	0.000000000
120	43	164	RUFY1	ENSG00000176783	-6147.65	-6147.65	1	0.7078849	0.000000000
122	12	185	TMEM40	ENSG00000088726	-4126.55	-4126.55	1	0.8255496	0.003343518
124	42	169	SELP	ENSG00000174175	-4235.20	-4235.20	1	0.7764394	0.003343518
128	5	120	RAB27B	ENSG00000041353	-3774.60	-3774.60	1	0.8055071	0.005441411
135	22	3	ACRBP	ENSG00000111644	-3414.65	-3414.65	1	0.8659601	0.014907667
137	36	95	NEXN	ENSG00000162614	-3308.25	-3308.25	1	0.8012412	0.014907667
141	34	24	CMIP	ENSG00000153815	-2811.00	-2811.00	1	0.8417712	0.031715653
143	18	2	ABCC3	ENSG00000108846	-2689.70	-2689.70	1	0.8651607	0.033458889
145	20	89	MMD	ENSG00000108960	-2404.65	-2404.65	1	0.7928590	0.036616162
146	14	197	TUBB1	ENSG00000101162	-2299.90	-2299.90	1	0.9753444	0.039234450
147	6	173	SLC2A3	ENSG00000059804	-1965.30	-1965.30	1	0.8896894	0.045939480
148	35	116	PTGIR	ENSG00000160013	-1962.00	-1962.00	1	0.8692328	0.045939480
149	41	115	PTCRA	ENSG00000171611	-1955.90	-1955.90	1	0.8832592	0.045939480

Plotting the clusters generated by DIscBIO¶

In [24]:

load("tSNE.Rdata")
sc@tsne<-tSNE
#sc<- comptSNE(sc,rseed=15555,max_iter=500, epoch=100,quiet = T)          # to perform the computation of a t-SNE map
plottSNE(sc)

** DIscBIO: a user-friendly pipeline for biomarker discovery in single-cell transcriptomics**¶

Comparing DIscBIO with Granatum using the CTC dataset¶

Required Packages¶

Loading Granatum outcome¶

Loading the CTC dataset¶

1. Data Pre-processing¶

Shared genes in the outcome of the pre-processing of DIscBIO and Granatum¶

2. DIscBIO Clustering using K-means (Euclidean)¶

Evaluating the stability and consistancy of the clusters generated by DIscBIO¶

CTC clusters with Granatum based on K-means (Euclidean)¶

Evaluating the stability and consistancy of the clusters generated by DIscBIO¶

Determining differentially expressed genes for K-means Clustering using DIscBIO¶

Comparing DEGs form DIscBIO and Granatum¶

[CL1 vs CL2 DIscBIO] vs [CL2 vs CL3 Granatum]¶

[CL1 vs CL3 DIscBIO] vs [CL2 vs CL4 Granatum]¶

[CL1 vs CL4 DIscBIO] vs [CL1 vs CL2 Granatum]¶

[CL2 vs CL3 DIscBIO] vs [CL3 vs CL4 Granatum]¶

[CL2 vs CL4 DIscBIO] vs [CL1 vs CL3 Granatum]¶

[CL3 vs CL4 DIscBIO] vs [CL1 vs CL4 Granatum]¶

Plotting the clusters generated by DIscBIO¶

DIscBIO: a user-friendly pipeline for biomarker discovery in single-cell transcriptomics¶