Normalize expression matrix by accounting for library size. Uses sctransform.
sct_normalize(exp, as_sparse = TRUE, verbose = TRUE)Gene x cell expression matrix.
Convert exp to sparse matrix.
Print messages.
Normalised expression matrix.
cortex_mrna <- ewceData::cortex_mrna()
#> see ?ewceData and browseVignettes('ewceData') for documentation
#> loading from cache
exp_sct_normed <- EWCE::sct_normalize(exp = cortex_mrna$exp[1:300, ])
#> Loading required namespace: sctransform
#> Converting to sparse matrix.
#> Calculating cell attributes from input UMI matrix: log_umi
#> Variance stabilizing transformation of count matrix of size 300 by 3005
#> Model formula is y ~ log_umi
#> Get Negative Binomial regression parameters per gene
#> Using 300 genes, 3005 cells
#>
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#> Found 2 outliers - those will be ignored in fitting/regularization step
#> Second step: Get residuals using fitted parameters for 300 genes
#>
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#> Calculating gene attributes
#> Wall clock passed: Time difference of 3.82128 secs
#> Computing corrected UMI count matrix
#>
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