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Quant. Biol.    2016, Vol. 4 Issue (4) : 243-260    https://doi.org/10.1007/s40484-016-0089-7
RESEARCH ARTICLE
Differential expression analyses for single-cell RNA-Seq: old questions on new data
Zhun Miao1,Xuegong Zhang1,2()
1. MOE Key Laboratory of Bioinformatics; Bioinformatics Division and Center for Synthetic & Systems Biology, TNLIST; Department of Automation, Tsinghua University, Beijing 100084, China
2. School of Life Sciences, Tsinghua University, Beijing 100084, China
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Abstract

Background: Single-cell RNA sequencing (scRNA-seq) is an emerging technology that enables high resolution detection of heterogeneities between cells. One important application of scRNA-seq data is to detect differential expression (DE) of genes. Currently, some researchers still use DE analysis methods developed for bulk RNA-Seq data on single-cell data, and some new methods for scRNA-seq data have also been developed. Bulk and single-cell RNA-seq data have different characteristics. A systematic evaluation of the two types of methods on scRNA-seq data is needed.

Results: In this study, we conducted a series of experiments on scRNA-seq data to quantitatively evaluate 14 popular DE analysis methods, including both of traditional methods developed for bulk RNA-seq data and new methods specifically designed for scRNA-seq data. We obtained observations and recommendations for the methods under different situations.

Conclusions: DE analysis methods should be chosen for scRNA-seq data with great caution with regard to different situations of data. Different strategies should be taken for data with different sample sizes and/or different strengths of the expected signals. Several methods for scRNA-seq data show advantages in some aspects, and DEGSeq tends to outperform other methods with respect to consistency, reproducibility and accuracy of predictions on scRNA-seq data.
Author Summary  Differential expression (DE) analysis is to find the genes whose expression values are significantly different among the groups of samples compared. Gene expression values could be measured by bulk RNA sequencing (RNA-seq) or single-cell RNA sequencing (scRNA-seq), which is emerging recently and could get the expression values of individual cell, while DE analysis methods designed for bulk RNA-seq are still commonly used on scRNA-seq data. We found that, since the characteristics of the two kinds of data are quite different, different DE analysis methods should be carefully chosen with regard to different situations of data when applied to scRNA-seq.
Keywords single-cell      RNA-Seq      differential expression     
PACS:     
Fund: 
Corresponding Author(s): Xuegong Zhang   
Online First Date: 23 November 2016    Issue Date: 01 December 2016
 Cite this article:   
Zhun Miao,Xuegong Zhang. Differential expression analyses for single-cell RNA-Seq: old questions on new data[J]. Quant. Biol., 2016, 4(4): 243-260.
 URL:  
https://academic.hep.com.cn/qb/EN/10.1007/s40484-016-0089-7
https://academic.hep.com.cn/qb/EN/Y2016/V4/I4/243
Method Model Input Platform Threshold Run time Ref.
SCDE Poisson and negative binomial model Read counts matrix R(package) p-value Minutes [13]
monocle Generalized additive models Read counts matrix R(package) p-value Minutes [14]
D3E Non-parametric (test of distribution) Read counts matrix Python(package) p-value 1 hour [15]
BPSC Beta-Poisson model Read counts matrix R(package) p-value 1 hour [16]
DESeq Negative binomial model Read counts matrix R(package) p-value Minutes [10]
edgeR Negative binomial model Read counts matrix R(package) p-value Minutes [11]
baySeq Negative binomial model Read counts matrix R(package) Likelihood 12 hours [24]
NBPSeq Negative binomial model Read counts matrix R(package) p-value Minutes [25]
Cuffdiff Beta negative binomial model Sam file Linux p-value 13 hours [26]
DEGseq Poisson model Read counts matrix R(package) p-value Minutes [12]
TSPM Poisson model Read counts matrix R(script) p-value 1 hour [27]
limma Linear models Read counts matrix R(package) p-value Seconds [28]
ballgown Nested linear models Read counts matrix /ctab file R(package) p-value Seconds [29]
SAMseq Non-parametric (resampling) Read count matrix R(package) p-value Minutes [30]
Tab.1  Information of gene differential expression analysis methods used.
Dataset Cell type Group Group description Sample size Ref.
GSE48968 Mouse bone-marrow-derived dendritic cells SBR LPS stimulation for 4 h, biological replicate 96 [32]
STR1 LPS stimulation for 4 h, technical replicate 1 81
STR2 LPS stimulation for 4 h, technical replicate 2 56
UBR Unstimulated, biological replicate 96
GSE59127 Mouse kidney cells E11.5 Embryonic day 11.5 total kidney 49 [33]
GSE59129 Mouse kidney cells E12.5 Embryonic day 12.5 total kidney 86
GSE59130 Mouse kidney cells P4 Renal vesicle cells from post-natal day (P4) kidneys 57
GSE74923 Mouse CD8+ T-cells CD8 Activated murine CD8+ T-cells 106 [34]
Mouse lymphocytic leukemia cells L1210 Lymphocytic leukemia cell line lineages 88
Tab.2  Information of samples for experiments.
Experiments Experiments type Abbreviation Group 1 Group 2
1 Between group comparison SBR_v_UBR SBR UBR
2 STR1_v_UBR STR1 UBR
3 STR1_v_STR2 STR1 STR2
4 Within group comparison SBR_v_SBR SBR SBR
5 UBR_v_UBR UBR UBR
6 STR1_v_STR1 STR1 STR1
7 Identical comparison SBRsplit SBR(split) SBR(split)
Tab.3  Information of experiments on dataset GSE48968.
DE genes mean SCDE monocle D3E BPSC DESeq edgeR baySeq NBPSeq Cuffdiff DEGseq TSPM limma ballgown SAMseq
SBR_v_UBR-1v1 NA 0 NA NA 0 NA 128.6 NA 62.1 2663.4 2755.4 NA NA NA
SBR_v_UBR-2v2 0.4 0.2 127.2 0 2 0 1.1 899.6 7 3815.8 137.1 268.6 27.9 0
SBR_v_UBR-5v5 31.8 10.9 0 2.1 10.6 9.6 38.2 1549.9 252.1 5711.8 5.5 1592.8 17.4 4.7
SBR_v_UBR-10v10 120.4 35.3 172.7 172.2 169.2 35.8 1554 1991.4 439 7612.2 11.8 3235.4 109.7 152.8
SBR_v_UBR-20v20 253.1 108.2 688.5 692.2 277.4 161.8 5972.2 2572.9 627.5 9972.3 133.5 8250.6 250.5 490.4
SBR_v_UBR-40v40 487 276 1267 1380 458 2209 8537 3430 956 12862 496 13834 511 963
STR1_v_UBR-1v1 NA 0.3 NA NA 0 NA 32.3 NA 47.5 2172.8 2306.7 NA NA NA
STR1_v_UBR-2v2 1.4 17.8 768.1 0 6.2 0 1 862.3 15.7 3100.1 109.8 243.1 125.3 0
STR1_v_UBR-5v5 38.6 8.3 0 14.1 20.1 13.7 35.8 1563.8 170.2 4664.4 10.7 161.2 15.5 53.2
STR1_v_UBR-10v10 129.9 59.6 86.5 362.2 276.9 39 375.4 1988 266.2 6191 40.5 803.9 75.5 204.4
STR1_v_UBR-20v20 287.8 188.8 386.9 756.2 462.6 143.4 4619.6 2534.3 393.4 7979.8 183.8 2896.1 218.5 543.1
STR1_v_UBR-40v40 630 412 787 1373 804 539 6660 3205 600 10285 503 6877 516 1077
STR1_v_STR2-1v1 NA 0.1 NA NA 0.2 NA 3.1 NA 100.2 1762.7 1850.4 NA NA NA
STR1_v_STR2-2v2 0 10.9 84 0 0.6 0 0 695.5 0 2523.4 70.1 113.8 54.5 0
STR1_v_STR2-5v5 0 0.9 0 0.3 0.2 0 0.4 1135.9 113.3 3710.3 0.6 20.6 0.1 0
STR1_v_STR2-10v10 0.9 1 0 75.5 10.8 0 13.2 1405 129.2 4847.1 0 256.1 0.2 3.4
STR1_v_STR2-20v20 11.3 1.3 0.4 172.1 4.5 0.2 2821.3 1674.2 170.6 6124.2 0 2029.3 0.6 2.5
STR1_v_STR2-40v40 21 10 23 281 14 0 3952 1930 180 7590 1 5318 11 6
SBR_v_SBR-1v1 NA 0.3 NA NA 0.2 NA 171.6 NA 126.5 3004.9 3119.8 NA NA NA
SBR_v_SBR-2v2 0.2 0.7 344.8 0 0.7 0 0.2 837.4 0 4128.1 116.2 168.5 66 0
SBR_v_SBR-5v5 0.2 1.3 0 0.1 0.2 0 1.4 1037 230.2 6244.1 0.7 329.6 25.5 0
SBR_v_SBR-10v10 0.2 1 0 24.6 15.2 0 137.9 1273.9 342.5 8312 0 1223 0 1.1
SBR_v_SBR-20v20 6.8 0.8 0 281.6 5.5 0.1 5772.6 1261.8 442.9 10945.2 0 1502 0 1.6
UBR_v_UBR-1v1 NA 0.2 NA NA 0 NA 22.4 NA 110.7 2301.6 2444 NA NA NA
UBR_v_UBR-2v2 0 0.6 235.9 0 0.2 0 0 875.1 0 3314.8 78.2 131.7 46 0
UBR_v_UBR-5v5 0 0.7 0 0.1 0.2 0 0.8 1469.6 149.6 5024.9 0.8 12.6 0.4 0
UBR_v_UBR-10v10 0.4 0.4 0 49.2 7 0.1 58.3 1847 201.2 6646.9 0 17.1 0 12.6
UBR_v_UBR-20v20 11.1 0.6 0 153.1 1.8 0 4414.4 2202.9 245.8 8533.8 0 139.9 0 4.8
STR1_v_STR1-1v1 NA 0.2 NA NA 0.2 NA 2.2 NA 101.7 1801.2 1899 NA NA NA
STR1_v_STR1-2v2 0 4 134.6 0 0.1 0 0 619.2 0 2508.4 65 103.8 14.2 0
STR1_v_STR1-5v5 0 0.6 0 0.2 0.2 0.2 0.4 1037.9 95.3 3708.8 0.4 6.1 0.3 0
STR1_v_STR1-10v10 0.2 1.1 0 67.8 6.6 0 7.5 1300.4 106 4766.9 0 24.1 0 2
STR1_v_STR1-20v20 6.1 0.4 0 153.9 1.8 0 2719.1 1538.5 137.1 6035.4 0 144.9 0 1.6
SBRsplit_v_SBRsplit-1v1 NA 53.5 NA NA 0 NA 0 NA 12.7 0 0 NA NA NA
SBRsplit_v_SBRsplit-2v2 0 43.9 0 0 0 0 0 372 0 0 0 0.1 30.8 0
SBRsplit_v_SBRsplit-5v5 0 0.8 0 0 0 0 0 1357.4 0 0.7 0 0 0 0
SBRsplit_v_SBRsplit-10v10 0 1 0 0 0 0 0 2498.7 0 0.6 0 0 0 0
SBRsplit_v_SBRsplit-20v20 0 1 0 0 0 0 0 3929.8 0 1 0 0 0 0
Tab.4  Average numbers of differential expression genes detected — adjusted p-value (FDR)<0.05.
Fig.1  Mean intersection numbers of top 1,000 DEG between different methods of experiment SBR_v_UBR.

Rows and columns stand for methods, and each cell of each table is a mean intersection number of top 1,000 DEG (ranked ascending with p-value) detected by the methods of row and column corresponding to respectively in 20 trials with random sampling. Sample numbers are 1 vs 1, 2 vs 2, 5 vs 5, 10 vs 10, 20 vs 20 and 40 vs 40 respectively from A to F. When sample number equals to 1, the table size is 6×6 because only monocle, DESeq, baySeq, Cuffdiff, DEGseq and TSPM could do the DE analysis of one sample versus one sample. And when sample number equals to 2, 5, 10, 20 or 40, the table size is 14×14 of all the 14 methods.

Fig.2  Consistency heatmaps.

(A) Mean intersection numbers of top 1,000 DEG between different sample numbers. Rows and columns stand for sample sizes, and each cell of every table is a mean intersection number of the top 1,000 DEG (ranked ascending with p-value) detected by the specific method of the sample sizes row and column corresponding to respectively in 20 trials with random sampling. The method is edgeR, DESeq and DEGseq from left to right. For each method of each experiment, the table size is 4×4 or 5×5, which depends on the method whether could do the DE analysis of one sample versus one sample. (B) Mean consistency measure. The mean intersection percentage between the top 1,000 DEG of sample number 20 and the union of the top 1,000 DEG of sample numbers 2, 5 and 10. Rows stand for different experiments and columns stand for different methods. The number in each cell of the table is calculated by three steps: get the union of the top 1,000 DEG of sample number 2, 5 and 10 of a specific method and specific experiment; get the intersection of the union last step get with the top 1,000 DEG of sample number 20, and counted the number of genes in the intersection and divide it by 1,000 to get the percentage of intersection; repeat these steps on all the 20 replicated experiments in each setting and get the average. (C) Mean consistency measure averaged over experiments. This table is a column average of the table in Figure 2B.

Fig.3  Reproducibility heatmaps.

(A) Reproducibility of each method in each set of experiments of different sample sizes. Every table stands for a different set of experiments. In each table, rows stand for different sample sizes and columns stand for different methods. NA means that the method could not conduct the analysis of one sample versus one sample. (B) Mean reproducibility over experiments of each method. This table is an average of the six tables in Figure 3A. Rows stand for different sample sizes and columns stand for different methods. NA means that the method could not conduct the analysis of one sample versus one sample. (C) Mean reproducibility over experiments and sample numbers of each method. This table is a column average of the last four rows of the table in Figure 3B.

Fig.4  ROC-like curves of every method.

The methods are monocle, DESeq, Cuffdiff, DEGseq, TSPM, SCDE, D3E, BPSC, edgeR, NBPSeq, limma and ballgown from A to H respectively. For every method, sample number is 1, 2, 5, 10, 20 from left to right. Some methods don’t have the figure of one sample versus one sample because of nonsupport of 1 vs 1 comparison of the methods.

RNA-Seq,RNA sequencing
scRNA-seq,single-cell RNA sequencing
DE,differential expression
DEG,differentially expressed genes
LPS,lipopolysaccharide
FDR,false discovery rate
ROC,receiver operating characteristic
NDE,non-differentially expressed
GEO,Gene Expression Omnibus
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