Run the noisyR pipeline for the count matrix approach

Calls the functions to run each of the three steps of the pipeline (similarity calculation, noise quantification, noise removal), with the specified parameters. See the individual function documentation for more details and required arguments. Required steps: calculate_expression_similarity_counts, calculate_noise_threshold. remove_noise_from_matrix. Optional steps: optimise_window_length, calculate_noise_threshold_method_statistics

noisyr_counts(
  expression.matrix,
  n.elements.per.window = NULL,
  optimise.window.length.logical = FALSE,
  similarity.threshold = 0.25,
  method.chosen = "Boxplot-IQR",
  ...
)

Arguments

expression.matrix	the expression matrix used as input for the similarity calculation; this argument is required
n.elements.per.window	number of elements to have in a window passed to calculate_expression_similarity_counts(); default 10% of the number of rows
optimise.window.length.logical	whether to call optimise_window_length to try and optimise the value of n.elements.per.window
similarity.threshold, method.chosen	parameters passed on to calculate_noise_threshold; they can be single values or vectors; if they are vectors optimal values are computed by calling calculate_noise_threshold_method_statistics and minimising the coefficient of variation across samples; all possible values for method.chosen can be viewed by get_methods_calculate_noise_threshold
...	arguments to be passed on to individual pipeline steps

Value

The denoised expression matrix.

See also

noisyr, noisyr_transcript

Examples

noisyr_counts(
    expression.matrix = matrix(1:100, ncol = 5),
    similarity.measure = "correlation_pearson",
    n.elements.per.window = 3)
#> >>> noisyR counts approach pipeline <<<
#> The input matrix has 20 rows and 5 cols
#>     number of genes: 20
#>     number of samples: 5
#>     the number of elements per window is 3
#>     the step size is 1
#>     the selected similarity metric is correlation_pearson
#>   Working with sample 1
#>   Working with sample 2
#>   Working with sample 3
#>   Working with sample 4
#>   Working with sample 5
#> Calculating noise thresholds for 5 samples...
#>     similarity.threshold = 0.25
#>     method.chosen = Boxplot-IQR
#> Denoising expression matrix...
#>     removing noisy genes
#>     adjusting matrix
#> >>> Done! <<<
#>       [,1] [,2] [,3] [,4] [,5]
#>  [1,]    2   22   42   62   82
#>  [2,]    3   23   43   63   83
#>  [3,]    4   24   44   64   84
#>  [4,]    5   25   45   65   85
#>  [5,]    6   26   46   66   86
#>  [6,]    7   27   47   67   87
#>  [7,]    8   28   48   68   88
#>  [8,]    9   29   49   69   89
#>  [9,]   10   30   50   70   90
#> [10,]   11   31   51   71   91
#> [11,]   12   32   52   72   92
#> [12,]   13   33   53   73   93
#> [13,]   14   34   54   74   94
#> [14,]   15   35   55   75   95
#> [15,]   16   36   56   76   96
#> [16,]   17   37   57   77   97
#> [17,]   18   38   58   78   98
#> [18,]   19   39   59   79   99
#> [19,]   20   40   60   80  100
#> [20,]   21   41   61   81  101