Please wait a minute...
购物车 申请加入邮件组 English
高级检索 图表检索
Quantitative Biology

ISSN 2095-4689

ISSN 2095-4697(Online)

CN 10-1028/TM

邮发代号 80-971

热点文章  |  下载排行  |  浏览排行
在线投稿 免费RSS订阅
Quantitative Biology  2016, Vol. 4 Issue (4): 243-260   https://doi.org/10.1007/s40484-016-0089-7
  本期目录
 
 
 
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
 全文: PDF(3763 KB)   HTML
关键词 ：      
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.
Key words： single-cell    RNA-Seq    differential expression
收稿日期: 2016-07-13      出版日期: 2016-12-01
PACS:     
基金资助: 
Corresponding Author(s): Xuegong Zhang   
 引用本文:   
.  [J]. Quantitative Biology, 2016, 4(4): 243-260.
Zhun Miao, Xuegong Zhang. Differential expression analyses for single-cell RNA-Seq: old questions on new data. Quant. Biol., 2016, 4(4): 243-260.
 链接本文:  
https://academic.hep.com.cn/qb/CN/10.1007/s40484-016-0089-7
https://academic.hep.com.cn/qb/CN/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  
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  
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  
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  
Fig.1  
Fig.2  
Fig.3  
Fig.4  
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
Tab.1  
1 Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L. and Wold, B. (2008) Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat. Methods, 5, 621–628
https://doi.org/10.1038/nmeth.1226 pmid: 18516045
2 Stegle, O., Teichmann, S. A. and Marioni, J. C. (2015) Computational and analytical challenges in single-cell transcriptomics. Nat. Rev. Genet., 16, 133–145
https://doi.org/10.1038/nrg3833 pmid: 25628217
3 Shapiro, E., Biezuner, T. and Linnarsson, S. (2013) Single-cell sequencing-based technologies will revolutionize whole-organism science. Nat. Rev. Genet., 14, 618–630
https://doi.org/10.1038/nrg3542 pmid: 23897237
4 Macaulay, I. C. and Voet, T. (2014) Single cell genomics: advances and future perspectives. PLoS Genet., 10, e1004126
https://doi.org/10.1371/journal.pgen.1004126 pmid: 24497842
5 Tang, F., Lao, K. and Surani, M. A. (2011) Development and applications of single-cell transcriptome analysis. Nat. Methods, 8, S6–S11
pmid: 21451510
6 Kanter, I. and Kalisky, T. (2015) Single cell transcriptomics: methods and applications. Front. Oncol., 5, 53
https://doi.org/10.3389/fonc.2015.00053 pmid: 25806353
7 Kolodziejczyk, A. A., Kim, J. K., Svensson, V., Marioni, J. C. and Teichmann, S. A. (2015) The technology and biology of single-cell RNA sequencing. Mol. Cell, 58, 610–620
https://doi.org/10.1016/j.molcel.2015.04.005 pmid: 26000846
8 Sandberg, R. (2014) Entering the era of single-cell transcriptomics in biology and medicine. Nat. Methods, 11, 22–24
https://doi.org/10.1038/nmeth.2764 pmid: 24524133
9 Saliba, A. E., Westermann, A. J., Gorski, S. A. and Vogel, J. (2014) Single-cell RNA-seq: advances and future challenges. Nucleic Acids Res., 42, 8845–8860
https://doi.org/10.1093/nar/gku555 pmid: 25053837
10 Anders, S. and Huber, W. (2010) Differential expression analysis for sequence count data. Genome Biol., 11, R106
https://doi.org/10.1186/gb-2010-11-10-r106 pmid: 20979621
11 Robinson, M. D., McCarthy, D. J. and Smyth, G. K. (2010) edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics, 26, 139–140
https://doi.org/10.1093/bioinformatics/btp616 pmid: 19910308
12 Wang, L., Feng, Z., Wang, X., Wang, X. and Zhang, X. (2010) DEGseq: an R package for identifying differentially expressed genes from RNA-seq data. Bioinformatics, 26, 136–138
https://doi.org/10.1093/bioinformatics/btp612 pmid: 19855105
13 Kharchenko, P. V., Silberstein, L. and Scadden, D. T. (2014) Bayesian approach to single-cell differential expression analysis. Nat. Methods, 11, 740–742
https://doi.org/10.1038/nmeth.2967 pmid: 24836921
14 Trapnell, C., Cacchiarelli, D., Grimsby, J., Pokharel, P., Li, S., Morse, M., Lennon, N. J., Livak, K. J., Mikkelsen, T. S. and Rinn, J. L. (2014) The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol., 32, 381–386
https://doi.org/10.1038/nbt.2859 pmid: 24658644
15 Delmans, M. and Hemberg, M. (2016) Discrete distributional differential expression (D3E)—a tool for gene expression analysis of single-cell RNA-seq data. BMC Bioinformatics, 17, 110
https://doi.org/10.1186/s12859-016-0944-6 pmid: 26927822
16 Vu, T. N., Wills, Q. F., Kalari, K. R., Niu, N., Wang, L., Rantalainen, M. and Pawitan, Y. (2016) Beta-Poisson model for single-cell RNA-seq data analyses. Bioinformatics, 32, 2128–2135
https://doi.org/10.1093/bioinformatics/btw202 pmid: 27153638
17 Finak, G., McDavid, A., Yajima, M., Deng, J., Gersuk, V., Shalek, A. K., Slichter, C. K., Miller, H. W., McElrath, M. J., Prlic, M., (2015) MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data. Genome Biol., 16, 278
https://doi.org/10.1186/s13059-015-0844-5 pmid: 26653891
18 Wu, L., Zhang, X., Zhao, Z., Wang, L., Li, B., Li, G., Dean, M., Yu, Q., Wang, Y., Lin, X., (2015) Full-length single-cell RNA-seq applied to a viral human cancer: applications to HPV expression and splicing analysis in HeLa S3 cells. Gigascience, 4, 51
https://doi.org/10.1186/s13742-015-0091-4 pmid: 26550473
19 Freeman, B. T., Jung, J. P. and Ogle, B. M. (2015) Single-cell RNA-seq of bone marrow-derived mesenchymal stem cells reveals unique profiles of lineage priming. PLoS One, 10, e0136199
https://doi.org/10.1371/journal.pone.0136199 pmid: 26352588
20 Avraham, R., Haseley, N., Brown, D., Penaranda, C., Jijon, H. B., Trombetta, J. J., Satija, R., Shalek, A. K., Xavier, R. J., Regev, A., (2015) Pathogen cell-to-cell variability drives heterogeneity in host immune responses. Cell, 162, 1309–1321
https://doi.org/10.1016/j.cell.2015.08.027 pmid: 26343579
21 Blakeley, P., Fogarty, N. M. E., Valle, I. D., Wamaitha, S. E., Hu, T. X., Elder, K., Snell, P., Christie, L., Robson, P. and Niakan, K. K. (2015) Defining the three cell lineages of the human blastocyst by single-cell RNA-seq. Development, 142, 3613
https://doi.org/10.1242/dev.131235
22 Fan, X., Zhang, X., Wu, X., Guo, H., Hu, Y., Tang, F. and Huang, Y. (2015) Single-cell RNA-seq transcriptome analysis of linear and circular RNAs in mouse preimplantation embryos. Genome Biol., 16, 148
https://doi.org/10.1186/s13059-015-0706-1 pmid: 26201400
23 Tasic, B., Menon, V., Nguyen, T. N., Kim, T. K., Jarsky, T., Yao, Z., Levi, B., Gray, L. T., Sorensen, S. A., Dolbeare, T., (2016) Adult mouse cortical cell taxonomy revealed by single cell transcriptomics. Nat. Neurosci., 19, 335–346
https://doi.org/10.1038/nn.4216 pmid: 26727548
24 Hardcastle, T. J. and Kelly, K. A. (2010) baySeq: empirical Bayesian methods for identifying differential expression in sequence count data. BMC Bioinformatics, 11, 422
https://doi.org/10.1186/1471-2105-11-422 pmid: 20698981
25 Di, Y., Schafer, D. W., Cumbie, J. S. and Chang, J. H. (2011) The NBP negative binomial model for assessing differential gene expression from RNA-Seq. Stat. Appl. Genet. Mol. Biol., 10, 1–28
https://doi.org/10.2202/1544-6115.1637
26 Trapnell, C., Hendrickson, D. G., Sauvageau, M., Goff, L., Rinn, J. L. and Pachter, L. (2013) Differential analysis of gene regulation at transcript resolution with RNA-seq. Nat. Biotechnol., 31, 46–53
https://doi.org/10.1038/nbt.2450 pmid: 23222703
27 Auer, P. L. and Doerge, R. W. (2011) A two-stage Poisson model for testing RNA-Seq data. Stat. Appl. Genet. Mol. Biol., 10 doi: 10.2202/1544-6115.1627
28 Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W. and Smyth, G. K. (2015) limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res., 43, e47
https://doi.org/10.1093/nar/gkv007 pmid: 25605792
29 Frazee, A. C., Pertea, G., Jaffe, A. E., Langmead, B., Salzberg, S. L. and Leek, J. T. (2014) Flexible analysis of transcriptome assemblies with Ballgown. Biorxiv: http://dx.doi.org/10.1101/003665
30 Li, J. and Tibshirani, R. (2013) Finding consistent patterns: a nonparametric approach for identifying differential expression in RNA-Seq data. Stat. Methods Med. Res., 22, 519–536
https://doi.org/10.1177/0962280211428386 pmid: 22127579
31 Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis, G.Durbin, R., and the 1000 Genome Project Data Processing Subgroup. (2009) The sequence alignment/map format and SAMtools. Bioinformatics, 25, 2078–2079
https://doi.org/10.1093/bioinformatics/btp352 pmid: 19505943
32 Shalek, A. K., Satija, R., Shuga, J., Trombetta, J. J., Gennert, D., Lu, D., Chen, P., Gertner, R. S., Gaublomme, J. T., Yosef, N., (2014) Single-cell RNA-seq reveals dynamic paracrine control of cellular variation. Nature, 510, 363–369
pmid: 24919153
33 Brunskill, E. W., Park, J. S., Chung, E., Chen, F., Magella, B. and Potter, S. S. (2014) Single cell dissection of early kidney development: multilineage priming. Development, 141, 3093–3101
https://doi.org/10.1242/dev.110601 pmid: 25053437
34 Kimmerling, R. J., Lee Szeto, G., Li, J. W., Genshaft, A. S., Kazer, S. W., Payer, K. R., de Riba Borrajo, J., Blainey, P. C., Irvine, D. J., Shalek, A. K., (2016) A microfluidic platform enabling single-cell RNA-seq of multigenerational lineages. Nat. Commun., 7, 10220
https://doi.org/10.1038/ncomms10220 pmid: 26732280
35 Su, Z., Łabaj, P. P., Li, S., Thierry-Mieg, J., Thierry-Mieg, D., Shi, W., Wang, C., Schroth, G. P., Setterquist, R. A., and Thompson, J. F. (2014) A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium. Nat. Biotechnol., 32, 903–914
https://doi.org/10.1038/nbt.2957 pmid: 25150838
36 Tan, P. K., Downey, T. J., Spitznagel, E. L. Jr, Xu, P., Fu, D., Dimitrov, D. S., Lempicki, R. A., Raaka, B. M. and Cam, M. C. (2003) Evaluation of gene expression measurements from commercial microarray platforms. Nucleic Acids Res., 31, 5676–5684.
https://doi.org/10.1093/nar/gkg763 pmid: 14500831
37 Shi, L., Shi, L., Reid, L. H., Jones, W. D., Shippy, R., Warrington, J. A., Baker, S. C., Collins, P. J., de Longueville, F., Kawasaki, E. S., (2006) The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements. Nat. Biotechnol., 24, 1151–1161
https://doi.org/10.1038/nbt1239 pmid: 16964229
38 Trapnell, C., Pachter, L. and Salzberg, S. L. (2009) TopHat: discovering splice junctions with RNA-Seq. Bioinformatics, 25, 1105–1111
https://doi.org/10.1093/bioinformatics/btp120 pmid: 19289445
39 Anders, S., Pyl, P.T., Huber, W (2015) HTSeq–a Python framework to work with high-throughput sequencing data. Bioinformatics 2015, 31, 166–169
[1] .  [J]. Quant. Biol., 2017, 5(1): 99-104.
[2] .  [J]. Quant. Biol., 2017, 5(1): 90-98.
[3] .  [J]. Quant. Biol., 2017, 5(1): 76-89.
[4] .  [J]. Quant. Biol., 2017, 5(1): 67-75.
[5] .  [J]. Quant. Biol., 2017, 5(1): 55-66.
[6] .  [J]. Quant. Biol., 2017, 5(1): 42-54.
[7] .  [J]. Quant. Biol., 2017, 5(1): 25-41.
[8] .  [J]. Quant. Biol., 2017, 5(1): 3-24.
[9] .  [J]. Quant. Biol., 2016, 4(4): 283-301.
[10] .  [J]. Quant. Biol., 2016, 4(4): 270-282.
[11] .  [J]. Quant. Biol., 2016, 4(4): 261-269.
[12] .  [J]. Quant. Biol., 2016, 4(4): 320-330.
[13] .  [J]. Quant. Biol., 2016, 4(4): 310-319.
[14] .  [J]. Quant. Biol., 2016, 4(4): 302-309.
[15] .  [J]. Quant. Biol., 2016, 4(3): 226-239.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed