There is no individual estimation step using SPsimSeq as the estimation is combined with simulation step.
library(simmethods)
library(SingleCellExperiment)
# Load data
ref_data <- simmethods::dataThe reference data contains 160 cells and 4000 genes, if we simulate datasets with default parameters and then we will obtain a new data which has the same size as the reference data.
simulate_result <- simmethods::SPsimSeq_simulation(
ref_data = ref_data,
other_prior = NULL,
return_format = "SCE",
seed = 111
)
#
# nCells: 160
# nGenes: 4000
# nGroups: 1
# de.prob: 0.1
# fc.group: 2
# nBatches: 1
# Warning in max(abs(logR)): no non-missing arguments to max; returning -InfSCE_result <- simulate_result[["simulate_result"]]
dim(SCE_result)
# [1] 4000 160head(colData(SCE_result))
# DataFrame with 6 rows and 3 columns
# cell_name group batch
# <character> <character> <character>
# Cell1 Cell1 Group1 Batch1
# Cell2 Cell2 Group1 Batch1
# Cell3 Cell3 Group1 Batch1
# Cell4 Cell4 Group1 Batch1
# Cell5 Cell5 Group1 Batch1
# Cell6 Cell6 Group1 Batch1head(rowData(SCE_result))
# DataFrame with 6 rows and 1 column
# gene_name
# <character>
# Gene1 Gene1
# Gene2 Gene2
# Gene3 Gene3
# Gene4 Gene4
# Gene5 Gene5
# Gene6 Gene6simulate_result <- simmethods::SPsimSeq_simulation(
ref_data = ref_data,
other_prior = list(nCells = 500,
nGenes = 1000),
return_format = "SCE",
seed = 111
)
# nCells: 500
# nGenes: 1000
# nGroups: 1
# de.prob: 0.1
# fc.group: 2
# nBatches: 1
# Warning in max(abs(logR)): no non-missing arguments to max; returning -InfSCE_result <- simulate_result[["simulate_result"]]
dim(SCE_result)
# [1] 1000 500The number of groups simulated by SPsimSeq is determined by the group information used in simulation step. If the group information of two cell states is provided, then the simulated dataset will contain two groups. SPsimSeq also provides other parameters related to DEGs such as the proportion of DEGs (de.prob) and the fold change of DGEs (fc.group).
For demonstration, we will simulate two groups using the learned parameters. We can set de.prob = 0.2 to simulate 20% genes as DEGs.
group_condition <- as.numeric(simmethods::group_condition)
simulate_result <- simmethods::SPsimSeq_simulation(
ref_data = ref_data,
other_prior = list(group.condition = group_condition,
de.prob = 0.2,
fc.group = 4),
return_format = "list",
seed = 111
)
# nCells: 160
# nGenes: 4000
# nGroups: 2
# de.prob: 0.2
# fc.group: 4
# nBatches: 1
# Warning in max(abs(logR)): no non-missing arguments to max; returning -Inf
# Note: The number of DE genes detected in the source data is 5 and the number of DE genes required to be included in the simulated data is 800. Therefore, candidiate DE genes are sampled with replacement.col_data <- simulate_result[["simulate_result"]][["col_meta"]]
table(col_data$group)
#
# Group1 Group2
# 80 80## gene information
gene_info <- simulate_result[["simulate_result"]][["row_meta"]]
### the proportion of DEGs
table(gene_info$de_gene)[2]/nrow(gene_info) ## de.prob = 0.2
# yes
# 0.2Users can simulate batches when batch.condition parameter is activated and just input the numeric vectors that specify the batch labels of cells.
set.seed(111)
ref_data <- scater::mockSCE(ncells = 160,
ngenes = 4000)
ref_data <- SingleCellExperiment::counts(ref_data)For demonstration, we will simulate two batches.
simulate_result <- simmethods::SPsimSeq_simulation(
ref_data = ref_data,
other_prior = list(batch.condition = sample(1:3, 160, replace = TRUE)),
return_format = "list",
seed = 111
)
# nCells: 160
# nGenes: 4000
# nGroups: 1
# de.prob: 0.1
# fc.group: 2
# nBatches: 3## cell information
col_data <- simulate_result[["simulate_result"]][["col_meta"]]table(col_data$batch)
#
# Batch1 Batch2 Batch3
# 50 58 52