deconvDigitalDLSorterObj | R Documentation |
Deconvolute bulk gene expression samples (bulk RNA-Seq). This function
requires a DigitalDLSorter
object with a trained Deep Neural Network
model (trained.model
slot) and the new bulk RNA-Seq samples to
be deconvoluted in the deconv.data
slot. See
?loadDeconvData
for more details.
deconvDigitalDLSorterObj( object, name.data, batch.size = 128, normalize = TRUE, scaling = "standarize", simplify.set = NULL, simplify.majority = NULL, verbose = TRUE )
object |
|
name.data |
Name of the data stored in the |
batch.size |
Number of samples per gradient update. If not specified,
|
normalize |
Normalize data before deconvolution ( |
scaling |
How to scale data before training. It may be:
|
simplify.set |
List specifying which cell types should be compressed into a new label whose name will be the list item. See examples for details. If provided, results are stored in a list with 'raw' and 'simpli.set' results. |
simplify.majority |
List specifying which cell types should be
compressed into the cell type with the highest proportion in each sample.
Unlike |
verbose |
Show informative messages during the execution. |
This function is intended for users who have built a devonvolution model
using their own single-cell RNA-Seq data. If you want to use a pre-trained
model to deconvolute your samples, see ?deconvDigitalDLSorter
.
DigitalDLSorter
object with
deconv.results
slot. The resulting information is a data frame with
samples (i) as rows and cell types (j) as columns. Each entry
represents the proportion of j cell type in i sample. If
simplify.set
or/and simpplify.majority
are provided, the
deconv.results
slot will contain a list with raw and simplified
results.
Torroja, C. and Sánchez-Cabo, F. (2019). digitalDLSorter: A Deep Learning algorithm to quantify immune cell populations based on scRNA-Seq data. Frontiers in Genetics 10, 978. doi: doi: 10.3389/fgene.2019.00978
trainDigitalDLSorterModel
DigitalDLSorter
## Not run: set.seed(123) sce <- SingleCellExperiment::SingleCellExperiment( assays = list( counts = matrix( rpois(30, lambda = 5), nrow = 15, ncol = 20, dimnames = list(paste0("Gene", seq(15)), paste0("RHC", seq(20))) ) ), colData = data.frame( Cell_ID = paste0("RHC", seq(20)), Cell_Type = sample(x = paste0("CellType", seq(6)), size = 20, replace = TRUE) ), rowData = data.frame( Gene_ID = paste0("Gene", seq(15)) ) ) DDLS <- loadSCProfiles( single.cell.data = sce, cell.ID.column = "Cell_ID", gene.ID.column = "Gene_ID" ) probMatrixValid <- data.frame( Cell_Type = paste0("CellType", seq(6)), from = c(1, 1, 1, 15, 15, 30), to = c(15, 15, 30, 50, 50, 70) ) DDLS <- generateBulkCellMatrix( object = DDLS, cell.ID.column = "Cell_ID", cell.type.column = "Cell_Type", prob.design = probMatrixValid, num.bulk.samples = 50, verbose = TRUE ) # training of DDLS model tensorflow::tf$compat$v1$disable_eager_execution() DDLS <- trainDigitalDLSorterModel( object = DDLS, on.the.fly = TRUE, batch.size = 15, num.epochs = 5 ) # simulating bulk RNA-Seq data countsBulk <- matrix( stats::rpois(100, lambda = sample(seq(4, 10), size = 100, replace = TRUE)), nrow = 40, ncol = 15, dimnames = list(paste0("Gene", seq(40)), paste0("Bulk", seq(15))) ) seBulk <- SummarizedExperiment(assay = list(counts = countsBulk)) DDLS <- loadDeconvData( object = DDLS, data = seBulk, name.data = "Example" ) # simplify arguments simplify <- list(CellGroup1 = c("CellType1", "CellType2", "CellType4"), CellGroup2 = c("CellType3", "CellType5")) DDLS <- deconvDigitalDLSorterObj( object = DDLS, name.data = "Example", simplify.set = simplify, simplify.majority = simplify ) ## End(Not run)
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