Wei Vivian Li 2020-12-13
2020/12/13: Version 1.1.0 released!
2019/03/18: Version 1.0.0 released!
Any suggestions on the package are welcome! For technical problems, please report to Issues. For suggestions and comments on the method, please contact Dr. Vivian Li (vivian.li@rutgers.edu) or Dr. Jessica Li (jli@stat.ucla.edu).
The package is not on CRAN yet. For installation please use the following codes in R
install.packages("devtools")
library(devtools)
install_github("Vivianstats/scDesign")scDesign has three main functions:
design_datafor simulation of scRNA-seq datadesign_sepfor scRNA-seq experimental design assuming two cell states are sequenced independetlydesign_jointfor scRNA-seq experimental design assuming two cell states are sequenced together
For detailed usage, please refer to the package manual or vignette.
design_data simulates additional scRNA-seq data by estimating gene expression parameters from a real scRNA-seq dataset. When ngroup = 1, it each time simulates a single dataset based on user-specified total read number S and cell number ncell.
realcount1 = readRDS(system.file("extdata", "astrocytes.rds", package = "scDesign"))
simcount1 = design_data(realcount = realcount1, S = 1e7, ncell = 1000, ngroup = 1, ncores = 1)
realcount1[1:3, 1:3]
#> GSM1657885 GSM1657932 GSM1657938
#> 1/2-SBSRNA4 0 0 0
#> A2M 0 0 34
#> A2ML1 0 0 25
simcount1[1:3, 1:3]
#> cell1 cell2 cell3
#> gene1 0 0 0
#> gene2 0 0 68
#> gene3 0 0 1When ngroup > 1, it simulates ngroup datasets following a specified differentiation path.
simdata = design_data(realcount = realcount1, S = rep(1e7,3), ncell = rep(100,3), ngroup = 3,
pUp = 0.03, pDown = 0.03, fU = 3, fL = 1.5, ncores = 1)
# simdata is a list of three elements
names(simdata)
#> [1] "count" "genesUp" "genesDown"
# count matrix of the cell state 2
simdata$count[[2]][1:3, 1:3]
#> C2_1 C2_2 C2_3
#> gene1 132 0 0
#> gene2 6 2 6
#> gene3 0 0 0
# up-regulated genes from state 1 to state 2
simdata$genesUp[[2]][1:3]
#> [1] "gene1655" "gene614" "gene6057"
# down-regulated genes from state 1 to state 2
simdata$genesDown[[2]][1:3]
#> [1] "gene1958" "gene4631" "gene4888"design_sep assists experimental design by selecting the optimal cell numbers for the two cell states in scRNA-seq, so that the subsequent DE analysis becomes most accurate based on the user-specified criterion. It assumes that cells from the two states are prepared as two separate libraries and sequenced independently.
realcount1 = readRDS(system.file("extdata", "astrocytes.rds", package = "scDesign"))
realcount2 = readRDS(system.file("extdata", "oligodendrocytes.rds", package = "scDesign"))
# candidate cell numbers for experimental design
ncell = cbind(2^seq(6,11,1), 2^seq(6,11,1))
prlist = design_sep(realcount1, realcount2, ncell = ncell, de_method = "ttest", ncores = 10)
# returns a list of five elements
names(prlist)
#> precision recall TN F1 F2
prlist$precision
#> p_thre 64vs64 128vs128 256vs256 512vs512 1024vs1024 2048vs2048
#> 0.01 0.332 0.272 0.178 0.121 0.084 0.056
#> 0.001 0.448 0.361 0.231 0.147 0.097 0.063
#> 1e-04 0.532 0.434 0.282 0.175 0.11 0.07
#> 1e-05 0.599 0.491 0.331 0.203 0.124 0.076
#> 1e-06 0.649 0.534 0.375 0.231 0.138 0.083design_sep also saves the analysis results to a txt file and a set of power analysis plots.
design_joint assists experimental design by selecting the optimal (total) cell number for a cell population that contains the two cell states of interest, so that the subsequent DE analysis becomes most accurate based on the user-specified criterion. It assumes that cells from the two states are prepared in the same library and sequenced together.
# candidate cell numbers for experimental design
ncell = round(2^seq(9,13,1))
prlist = design_joint(realcount1, realcount2, prop1 = 0.2, prop2 = 0.15,
ncell = ncell, de_method = "ttest", ncores = 10)
# returns a list of five elements
names(prlist)
#> precision recall TN F1 F2
prlist$recall
#> 512 1024 2048 4096 8192
#> 0.01 0.315 0.33 0.259 0.176 0.111
#> 0.001 0.235 0.281 0.24 0.169 0.108
#> 1e-04 0.176 0.236 0.22 0.162 0.105
#> 1e-05 0.133 0.198 0.2 0.155 0.102
#> 1e-06 0.103 0.166 0.181 0.147 0.099design_joint also saves the analysis results to a txt file and a set of power analysis plots.
Li, Wei Vivian, and Jingyi Jessica Li. "A statistical simulator scDesign for rational scRNA-seq experimental design." Bioinformatics 35, no. 14 (2019): i41-i50. Link