Package 'POWSC'

Title: Simulation, power evaluation, and sample size recommendation for single cell RNA-seq
Description: Determining the sample size for adequate power to detect statistical significance is a crucial step at the design stage for high-throughput experiments. Even though a number of methods and tools are available for sample size calculation for microarray and RNA-seq in the context of differential expression (DE), this topic in the field of single-cell RNA sequencing is understudied. Moreover, the unique data characteristics present in scRNA-seq such as sparsity and heterogeneity increase the challenge. We propose POWSC, a simulation-based method, to provide power evaluation and sample size recommendation for single-cell RNA sequencing DE analysis. POWSC consists of a data simulator that creates realistic expression data, and a power assessor that provides a comprehensive evaluation and visualization of the power and sample size relationship.
Authors: Kenong Su [aut, cre], Hao Wu [aut]
Maintainer: Kenong Su <[email protected]>
License: GPL-2
Version: 1.13.0
Built: 2024-07-24 05:15:05 UTC
Source: https://github.com/bioc/POWSC

Help Index

sample data for POWSC

Description

This dataset is obtained from GEO under accession number GSE29087. It is generated to profile the transcriptomes for 92 single cells consisting of mouse embryonic fibroblast (MEF) and embryonic stem (ES) cells (Islam et al., 2011). The average sequencing depth for the dataset is around half a million.

Usage

es_mef_sce

Format

A singlecellexperiment object contain the expressin data with two cell types

References

Islam, S., Kjällquist, U., Moliner, A., Zajac, P., Fan, J.-B., Lönnerberg, P., and Linnarsson, S. (2011). Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq. Genome research, 21(7), 1160–1167.

Estimate characterized parameters for a given scRNA-seq data (SingleCellExperiment object or a count matrix).

Description

These parameters include four gene-wise parameters and two cell-wise parameters.

Usage

Est2Phase(sce, low.prob = 0.99)

Arguments

sce

SingleCellExperiment object with assays(sce)[[1]] is the count matrix or input directly
low.prob

lower bound probability for phase I

Value

a list of needed estimated parameters

Examples

data("es_mef_sce")
sce = es_mef_sce[, colData(es_mef_sce)$cellTypes == "fibro"]
set.seed(123)
rix = sample(1:nrow(sce), 500)
sce = sce[rix, ]
estParas = Est2Phase(sce)

plot the result use visualization.

Description

plot the result use visualization.

Usage

plot_POWSC(POWSCobj, Form = c("I", "II"), Cell_Type = c("PW", "Multi"))

Arguments

POWSCobj

a POWSC object from runPOWSC
Form

choose from "I" or "II".
Cell_Type

choose from "PW" or "Multi"

Value

for multiple comparison cases, return the pheatmap; for the pairwise comparison, return the ggplot object.

Examples

data("es_mef_sce")
sce = es_mef_sce[, colData(es_mef_sce)$cellTypes == "fibro"]
set.seed(12)
rix = sample(1:nrow(sce), 500)
sce = sce[rix, ]
est_Paras = Est2Phase(sce)
sim_size = c(100, 200) # A numeric vector
pow_rslt = runPOWSC(sim_size = sim_size, est_Paras = est_Paras,per_DE=0.05, DE_Method = "MAST", Cell_Type = "PW") # Note, using our previous developed tool SC2P is faster.
plot_POWSC(pow_rslt, Form="II", Cell_Type = "PW") # Alternatively, we can use Form="I")

Run DE analysis by using MAST. Here we output two result tables corresponding to two forms of DE genes. These parameters include four gene-wise parameters and two cell-wise parameters.

Description

Run DE analysis by using MAST. Here we output two result tables corresponding to two forms of DE genes. These parameters include four gene-wise parameters and two cell-wise parameters.

Usage

Power_Cont(
  DErslt,
  simData,
  alpha = 0.1,
  delta = 0.5,
  strata = c(0, 10, 2^(seq_len(4)) * 10, Inf)
)

Arguments

DErslt

is from the DE analysis by MAST
simData

is the corresponding simulated scRNA-seq dataset (SingCellExperiment)
alpha

is the cutoff for the fdr which can be modified
delta

or the lfc is the cutoff (=0.5) used to determined the high DE genes for Form II
strata

can be modified by the user. By default, it is (0, 10], (10, 20], (20, 40], (40, 80], (80, Inf]

Value

a list of metrics for power analysis such as: stratified targeted power and marginal power.

Examples

data("es_mef_sce")
sce = es_mef_sce[, colData(es_mef_sce)$cellTypes == "fibro"]
set.seed(123)
rix = sample(1:nrow(sce), 500)
sce = sce[rix, ]
estParas = Est2Phase(sce)
simData = Simulate2SCE(n=500, estParas1 = estParas, estParas2 = estParas)
DErslt = runDE(simData$sce)
Cont_pow = Power_Cont(DErslt, simData)

Run DE analysis by using MAST. Here we output two result tables corresponding to two forms of DE genes. These parameters include four gene-wise parameters and two cell-wise parameters.

Description

Run DE analysis by using MAST. Here we output two result tables corresponding to two forms of DE genes. These parameters include four gene-wise parameters and two cell-wise parameters.

Usage

Power_Disc(
  DErslt,
  simData,
  alpha = 0.1,
  delta = 0.1,
  strata = seq(0, 1, by = 0.2)
)

Arguments

DErslt

is from the DE analysis by MAST
simData

is the corresponding simulated scRNA-seq dataset (SingCellExperiment)
alpha

is the cutoff for the fdr which can be modified
delta

or the zero ratio change is the cutoff (=0.1) used to determined the high DE genes for Form II
strata

can be modified by the user. By default, it is (0, 0.2], (0.2, 0.4], (0.4, 0.6], (0.6, 0.8], (0.8, 1]

Value

a list of metrics for power analysis such as: stratified targeted power and marginal power.

Examples

data("es_mef_sce")
sce = es_mef_sce[, colData(es_mef_sce)$cellTypes == "fibro"]
set.seed(123)
rix = sample(1:nrow(sce), 500)
sce = sce[rix, ]
estParas = Est2Phase(sce)
simData = Simulate2SCE(n=1000, estParas1 = estParas, estParas2 = estParas)
DErslt = runDE(simData$sce)
Disc_pow = Power_Disc(DErslt, simData)

A wrapper function for calling DE genes. This contains two methods: MAST and SC2P

Description

A wrapper function for calling DE genes. This contains two methods: MAST and SC2P

Usage

runDE(sce, DE_Method = c("MAST", "SC2P"))

Arguments

sce

is a simulated scRNA-seq dataset with two-group conditions, e.g., treatment vs control.
DE_Method

is a string chosen from "MAST" or "SC2P".

Value

a list of three tables: the first table summaries the DE result for both forms of DE genes. cont table represents the result for continous case. disc table shows the result for discontinous case.

Examples

data("es_mef_sce")
sce = es_mef_sce[, colData(es_mef_sce)$cellTypes == "fibro"]
set.seed(123)
rix = sample(1:nrow(sce), 500)
sce = sce[rix, ]
estParas = Est2Phase(sce)
simData = Simulate2SCE(n=100, estParas1 = estParas, estParas2 = estParas)
sim_sce = simData$sce
DErslt = runDE(sim_sce)

Run DE analysis by using MAST. Here we output two result tables corresponding to two forms of DE genes. These parameters include four gene-wise parameters and two cell-wise parameters.

Description

Run DE analysis by using MAST. Here we output two result tables corresponding to two forms of DE genes. These parameters include four gene-wise parameters and two cell-wise parameters.

Usage

runMAST(sce)

Arguments

sce

is a simulated scRNA-seq dataset with two-group conditions, e.g., treatment vs control.

Value

a list of three tables: the first table summaries the DE result for both forms of DE genes. cont table represents the result for continous case. disc table shows the result for discontinous case.

Estimate characterized parameters for a given scRNA-seq data (SingleCellExperiment object or a count matrix).

Description

These parameters include four gene-wise parameters and two cell-wise parameters.

Usage

runPOWSC(
  sim_size = c(50, 100, 200, 800, 1000),
  per_DE = 0.05,
  est_Paras,
  DE_Method = c("MAST", "SC2P"),
  Cell_Type = c("PW", "Multi"),
  multi_Prob = NULL,
  alpha = 0.1,
  disc_delta = 0.1,
  cont_delta = 0.5
)

Arguments

sim_size

a list of numbers
per_DE

the percentage of the DE genes.
est_Paras

the template parameter estimated from one cell type.
DE_Method

is a string chosen from "MAST" or "SC2P".
Cell_Type

is a string corresponding to the 1st scenario: same cell type comparison, and 2nd scenario: multiple cell types.
multi_Prob

is the mixture cell proportions which sum up to 1. If not summing up to 1, then the package will internally do the normalization procedure.
alpha

is the cutoff for the fdr which can be modified
disc_delta

or the zero ratio change is the cutoff (=0.1) used to determined the high DE genes for Form II.
cont_delta

or the lfc is the cutoff (=0.5) used to determined the high DE genes for Form II.

Value

POWSC object

Examples

data("es_mef_sce")
sce = es_mef_sce[, colData(es_mef_sce)$cellTypes == "fibro"]
set.seed(12)
rix = sample(1:nrow(sce), 500)
sce = sce[rix, ]
est_Paras = Est2Phase(sce)
sim_size = c(100, 200) # A numeric vector
pow_rslt = runPOWSC(sim_size = sim_size, est_Paras = est_Paras,per_DE=0.05, DE_Method = "MAST", Cell_Type = "PW") # Note, using our previous developed tool SC2P is faster.

Run DE analysis by using SC2P. Here we output two result tables corresponding to two forms of DE genes.

Description

Run DE analysis by using SC2P. Here we output two result tables corresponding to two forms of DE genes.

Usage

runSC2P(sce)

Arguments

sce

is a simulated scRNA-seq dataset with two-group conditions, e.g., treatment vs control.

Value

a list of three tables: the first table summaries the DE result for both forms of DE genes. cont table represents the result for continous case. disc table shows the result for discontinous case.

sample data for GSE67835

Description

This dataset is obtained from GEO under accession number GSE67835. It includes 7 patients about 466 cells to capture the cellular complexity of the adult and fetal human brain at a whole transcriptome level. Healthy adult temporal lobe tissue was obtained from epileptic patients during temporal lobectomy for medically refractory seizures.

Usage

sce

Format

A singlecellexperiment object contain the expressions data with three cell types about patient AS_7

Source

https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE67835

References

Darmanis, Spyros, et al. "A survey of human brain transcriptome diversity at the single cell level." Proceedings of the National Academy of Sciences 112.23 (2015): 7285-7290.

Simulate the data for two-group comparison; e.g., treatment v.s. control It simulates the DE changes in two forms corresponding two types of DE genes

Description

Simulate the data for two-group comparison; e.g., treatment v.s. control It simulates the DE changes in two forms corresponding two types of DE genes

Usage

Simulate2SCE(n = 100, perDE = 0.05, estParas1, estParas2)

Arguments

n

the number of total cells for two groups
perDE

percentage of DE genes
estParas1

the set of parameters corresponding to cell type I
estParas2

the set of parameters corresponding to cell type II

Value

a list of metrics recording the changes in the generated data: such as the DE gene indices for Form I and II DE genes, and simulated expression data in singlecellexperiment format.

Examples

data("es_mef_sce")
sce = es_mef_sce[, colData(es_mef_sce)$cellTypes == "fibro"]
set.seed(123)
rix = sample(1:nrow(sce), 500)
sce = sce[rix, ]
estParas = Est2Phase(sce)
simData = Simulate2SCE(n=100, estParas1 = estParas, estParas2 = estParas)

Simulate the data for multiple-group comparisons; e.g., different cell types in blood It simulates the DE changes in two forms corresponding two types of DE genes

Description

Simulate the data for multiple-group comparisons; e.g., different cell types in blood It simulates the DE changes in two forms corresponding two types of DE genes

Usage

SimulateMultiSCEs(
  n = 1000,
  estParas_set,
  multiProb,
  delta1 = 0.1,
  delta2 = 0.5
)

Arguments

n

the number of total cells for multiple groups; e.g., 1000, 2000, and etc.
estParas_set

a set of parameters corresponding to different cell types.
multiProb

a vector of probilities correponding to each cell type. It is not necessary to sum up to 1 because POWSC will normalize internally.
delta1

the minimum of expression change used to determine the Form I DE.
delta2

the minimum of log fold change used to determine the Form II DE.

Value

a list of simulated datasets. Each dataset corresponds to a pair-wise comparison including a series of metrics such as the DE gene indices for Form I and II DE genes, and simulated expression data in singlecellexperiment format.

Examples

data("es_mef_sce")
set.seed(123)
rix = sample(1:nrow(es_mef_sce), 500)
es_mef_sce = es_mef_sce[rix, ]
sce1 = es_mef_sce[, colData(es_mef_sce)$cellTypes == "fibro"]
estParas1 = Est2Phase(sce1)
sce2 = es_mef_sce[, colData(es_mef_sce)$cellTypes == "stemCell"]
estParas2 = Est2Phase(sce2)
estParas_set = list(celltype1 = estParas1, celltype2 = estParas1, celltype3 =estParas2)
multiProb = c(0.2, 0.3, 0.5)
simData = SimulateMultiSCEs(n=200, estParas_set = estParas_set, multiProb = multiProb)

summary of the result

Description

summary of the result

Usage

summary_POWSC(POWSCobj, Form = c("I", "II"), Cell_Type = c("PW", "Multi"))

Arguments

POWSCobj

a POWSC object from runPOWSC
Form

choose from "I" or "II".
Cell_Type

choose from "PW" or "Multi".

Value

return the summary of the power including stratified, marginal, and overall power.

Examples

data("es_mef_sce")
sce = es_mef_sce[, colData(es_mef_sce)$cellTypes == "fibro"]
set.seed(12)
rix = sample(1:nrow(sce), 500)
sce = sce[rix, ]
est_Paras = Est2Phase(sce)
sim_size = c(100, 200) # A numeric vector
pow_rslt = runPOWSC(sim_size = sim_size, est_Paras = est_Paras,per_DE=0.05, DE_Method = "MAST", Cell_Type = "PW") # Note, using our previous developed tool SC2P is faster.
summary_POWSC(pow_rslt, Form="II", Cell_Type = "PW")