README.md

In Vivianstats/scDesign: A statistical simulator for rational scRNA-seq experimental design

scDesign: a statistical simulator for rational scRNA-seq experimental design

Wei Vivian Li 2020-12-13

Latest News

2020/12/13: Version 1.1.0 released!

2019/03/18: Version 1.0.0 released!

Introduction

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 ([e-mail]) or Dr. Jessica Li ([e-mail]).

Installation

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")

Quick start

scDesign has three main functions:

• design_data for simulation of scRNA-seq data
• design_sep for scRNA-seq experimental design assuming two cell states are sequenced independetly
• design_joint for scRNA-seq experimental design assuming two cell states are sequenced together

For 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.

Citation

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

Vivianstats/scDesign documentation built on Dec. 17, 2020, 8:04 a.m.