Calculate matrix stats

Usage

matrix_stats(
  matrix,
  row_stats = c("none", "nonzero", "mean", "variance"),
  col_stats = c("none", "nonzero", "mean", "variance"),
  threads = 0L
)

Arguments

matrix: Input matrix object
row_stats: Which row statistics to compute
col_stats: Which col statistics to compute
threads: Number of threads to use during execution

Value

List of row_stats: matrix of n_stats x n_rows, col_stats: matrix of n_stats x n_cols

Details

The statistics will be calculated in a single pass over the matrix, so this method is desirable to use for efficiency purposes compared to the more standard rowMeans or colMeans if multiple statistics are needed. The stats are ordered by complexity: nonzero, mean, then variance. All less complex stats are calculated in the process of calculating a more complicated stat. So to calculate mean and variance simultaneously, just ask for variance, which will compute mean and nonzero counts as a side-effect

Examples

mat <- matrix(rpois(100, lambda = 5), nrow = 10)
rownames(mat) <- paste0("gene", 1:10)
colnames(mat) <- paste0("cell", 1:10)
mat <- mat %>% as("dgCMatrix") %>% as("IterableMatrix")

## By default, no row or column stats are calculated
res_none <- matrix_stats(mat)
res_none
#> $row_stats
#>      gene1 gene2 gene3 gene4 gene5 gene6 gene7 gene8 gene9 gene10
#> 
#> $col_stats
#>      cell1 cell2 cell3 cell4 cell5 cell6 cell7 cell8 cell9 cell10
#> 

## Request row variance (automatically computes mean and nonzero too)
res_row_var <- matrix_stats(mat, row_stats = "variance")
res_row_var
#> $row_stats
#>              gene1     gene2    gene3     gene4     gene5     gene6     gene7
#> nonzero  10.000000 10.000000 10.00000 10.000000 10.000000 10.000000 10.000000
#> mean      6.000000  5.200000  5.40000  4.800000  5.700000  5.800000  7.000000
#> variance  5.555556  1.733333 10.93333  3.288889  6.677778  3.511111  5.555556
#>              gene8     gene9    gene10
#> nonzero  10.000000 10.000000 10.000000
#> mean      4.200000  3.500000  4.800000
#> variance  3.288889  3.388889  5.288889
#> 
#> $col_stats
#>      cell1 cell2 cell3 cell4 cell5 cell6 cell7 cell8 cell9 cell10
#> 

## Request both row variance and column variance
res_both_var <- matrix_stats(
  mat = mat,
  row_stats = "variance",
  col_stats = "mean"
)
res_both_var
#> $row_stats
#>              gene1     gene2    gene3     gene4     gene5     gene6     gene7
#> nonzero  10.000000 10.000000 10.00000 10.000000 10.000000 10.000000 10.000000
#> mean      6.000000  5.200000  5.40000  4.800000  5.700000  5.800000  7.000000
#> variance  5.555556  1.733333 10.93333  3.288889  6.677778  3.511111  5.555556
#>              gene8     gene9    gene10
#> nonzero  10.000000 10.000000 10.000000
#> mean      4.200000  3.500000  4.800000
#> variance  3.288889  3.388889  5.288889
#> 
#> $col_stats
#>         cell1 cell2 cell3 cell4 cell5 cell6 cell7 cell8 cell9 cell10
#> nonzero  10.0  10.0  10.0    10  10.0  10.0  10.0  10.0  10.0   10.0
#> mean      4.5   4.9   6.5     5   4.3   5.1   5.8   5.4   5.4    5.5
#>