knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path = "README-" )
2020/12/13: Version 1.1.0 released!
2019/03/18: Version 1.0.0 released!
Any suggestions on the package are welcome! For technical problems, please report to Issues. For suggestions and comments on the method, please contact Dr. Vivian Li (vivian.li@rutgers.edu) or Dr. Jessica Li (jli@stat.ucla.edu).
The package is not on CRAN yet. For installation please use the following codes in R
install.packages("devtools") library(devtools) install_github("Vivianstats/scDesign")
scDesign
has three main functions:
design_data
for simulation of scRNA-seq datadesign_sep
for scRNA-seq experimental design assuming two cell states are sequenced independetlydesign_joint
for scRNA-seq experimental design assuming two cell states are sequenced togetherFor detailed usage, please refer to the package manual or vignette.
design_data
design_data
simulates additional scRNA-seq data by estimating gene expression parameters from a real scRNA-seq dataset. When ngroup = 1
, it each time simulates a single dataset based on user-specified total read number S
and cell number ncell
.
realcount1 = readRDS(system.file("extdata", "astrocytes.rds", package = "scDesign")) simcount1 = design_data(realcount = realcount1, S = 1e7, ncell = 1000, ngroup = 1, ncores = 1) realcount1[1:3, 1:3] #> GSM1657885 GSM1657932 GSM1657938 #> 1/2-SBSRNA4 0 0 0 #> A2M 0 0 34 #> A2ML1 0 0 25 simcount1[1:3, 1:3] #> cell1 cell2 cell3 #> gene1 0 0 0 #> gene2 0 0 68 #> gene3 0 0 1
When ngroup > 1
, it simulates ngroup
datasets following a specified differentiation path.
simdata = design_data(realcount = realcount1, S = rep(1e7,3), ncell = rep(100,3), ngroup = 3, pUp = 0.03, pDown = 0.03, fU = 3, fL = 1.5, ncores = 1) # simdata is a list of three elements names(simdata) #> [1] "count" "genesUp" "genesDown" # count matrix of the cell state 2 simdata$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
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$precision #> p_thre 64vs64 128vs128 256vs256 512vs512 1024vs1024 2048vs2048 #> 0.01 0.332 0.272 0.178 0.121 0.084 0.056 #> 0.001 0.448 0.361 0.231 0.147 0.097 0.063 #> 1e-04 0.532 0.434 0.282 0.175 0.11 0.07 #> 1e-05 0.599 0.491 0.331 0.203 0.124 0.076 #> 1e-06 0.649 0.534 0.375 0.231 0.138 0.083
design_sep
also saves the analysis results to a txt file and a set of power analysis plots.
design_joint
design_joint
assists experimental design by selecting the optimal (total) cell number for a cell population that contains the two cell states of interest, so that the subsequent DE analysis becomes most accurate based on the user-specified criterion. It assumes that cells from the two states are prepared in the same library and sequenced together.
# candidate cell numbers for experimental design ncell = round(2^seq(9,13,1)) prlist = design_joint(realcount1, realcount2, prop1 = 0.2, prop2 = 0.15, ncell = ncell, de_method = "ttest", ncores = 10) # returns a list of five elements names(prlist) #> precision recall TN F1 F2 prlist$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 0.099
design_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
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