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This never creates the full residual matrix and can be used to determine highly variable genes.
get_residual_var( vst_out, umi, residual_type = "pearson", res_clip_range = c(-sqrt(ncol(umi)), sqrt(ncol(umi))), min_variance = vst_out$arguments$min_variance, cell_attr = vst_out$cell_attr, bin_size = 256, verbosity = vst_out$arguments$verbosity, verbose = NULL, show_progress = NULL )
A vector of residual variances (after clipping)
The output of a vst run
The UMI count matrix that will be used
What type of residuals to return; can be 'pearson' or 'deviance'; default is 'pearson'
Numeric of length two specifying the min and max values the residuals will be clipped to; default is c(-sqrt(ncol(umi)), sqrt(ncol(umi)))
Lower bound for the estimated variance for any gene in any cell when calculating pearson residual; default is vst_out$arguments$min_variance
Data frame of cell meta data
Number of genes to put in each bin (to show progress)
An integer specifying whether to show only messages (1), messages and progress bars (2) or nothing (0) while the function is running; default is 2
Deprecated; use verbosity instead
# \donttest{ vst_out <- vst(pbmc, return_cell_attr = TRUE) res_var <- get_residual_var(vst_out, pbmc) # }
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