getAdjustedMat

Get Adjusted Matrix with scMerge2 parameter estimated

getAdjustedMat(
  exprsMat,
  fullalpha,
  ctl = rownames(exprsMat),
  adjusted_means = NULL,
  ruvK = 20,
  return_subset_genes = NULL
)

Arguments

exprsMat

A gene (row) by cell (column) matrix to be adjusted.

fullalpha

A matrix indicates the estimated alpha returned by scMerge2().

ctl

A character vector of negative control. It should have a non-empty intersection with the rows of exprsMat.

adjusted_means

A rowwise mean of the gene by cell matrix

ruvK

An integer indicates the number of unwanted variation factors that are removed, default is 20.

return_subset_genes

An optional character vector of indicates the subset of genes will be adjusted.

Value

Returns the adjusted matrix will be return.

Author

Yingxin Lin

Examples

## Loading example data
data('example_sce', package = 'scMerge')
## Previously computed stably expressed genes
data('segList_ensemblGeneID', package = 'scMerge')
## Running an example data with minimal inputs
library(SingleCellExperiment)
#> Loading required package: SummarizedExperiment
#> Loading required package: MatrixGenerics
#> Loading required package: matrixStats
#> 
#> Attaching package: ‘MatrixGenerics’
#> The following objects are masked from ‘package:matrixStats’:
#> 
#>     colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse,
#>     colCounts, colCummaxs, colCummins, colCumprods, colCumsums,
#>     colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,
#>     colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
#>     colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
#>     colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
#>     colWeightedMeans, colWeightedMedians, colWeightedSds,
#>     colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
#>     rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,
#>     rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,
#>     rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,
#>     rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
#>     rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,
#>     rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
#>     rowWeightedSds, rowWeightedVars
#> Loading required package: GenomicRanges
#> Loading required package: stats4
#> Loading required package: BiocGenerics
#> 
#> Attaching package: ‘BiocGenerics’
#> The following objects are masked from ‘package:stats’:
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#>     IQR, mad, sd, var, xtabs
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#>     lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin,
#>     pmin.int, rank, rbind, rownames, sapply, setdiff, sort, table,
#>     tapply, union, unique, unsplit, which.max, which.min
#> Loading required package: S4Vectors
#> 
#> Attaching package: ‘S4Vectors’
#> The following object is masked from ‘package:utils’:
#> 
#>     findMatches
#> The following objects are masked from ‘package:base’:
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#>     I, expand.grid, unname
#> Loading required package: IRanges
#> Loading required package: GenomeInfoDb
#> Loading required package: Biobase
#> Welcome to Bioconductor
#> 
#>     Vignettes contain introductory material; view with
#>     'browseVignettes()'. To cite Bioconductor, see
#>     'citation("Biobase")', and for packages 'citation("pkgname")'.
#> 
#> Attaching package: ‘Biobase’
#> The following object is masked from ‘package:MatrixGenerics’:
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#>     rowMedians
#> The following objects are masked from ‘package:matrixStats’:
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#>     anyMissing, rowMedians
scMerge2_res <- scMerge2(exprsMat = logcounts(example_sce),
batch = example_sce$batch,
ctl = segList_ensemblGeneID$mouse$mouse_scSEG,
return_matrix = FALSE)
#> [1] "Cluster within batch"
#> Warning: You're computing too large a percentage of total singular values, use a standard svd instead.
#> Warning: You're computing too large a percentage of total singular values, use a standard svd instead.
#> [1] "Normalising data"
#> [1] "Constructing pseudo-bulk"
#> Dimension of pseudo-bulk expression: [1] 1047  131
#> [1] "Identifying MNC using pseudo-bulk:"

#> [1] "Running RUV"
cosineNorm_mat <- batchelor::cosineNorm(logcounts(example_sce))
adjusted_means <- DelayedMatrixStats::rowMeans2(cosineNorm_mat)
newY <- getAdjustedMat(cosineNorm_mat, scMerge2_res$fullalpha,
              ctl = segList_ensemblGeneID$mouse$mouse_scSEG,
              ruvK = 20,
              adjusted_means = adjusted_means)
assay(example_sce, "scMerge2") <- newY

example_sce = scater::runPCA(example_sce, exprs_values = 'scMerge2')                                       
scater::plotPCA(example_sce, colour_by = 'cellTypes', shape = 'batch')