Run the noisyR pipeline

Calls one of noisyr_counts or noisyr_transcript, with the specified parameters. See the individual function documentation for more details and required arguments: noisyr_counts, noisyr_transcript

noisyr(approach.for.similarity.calculation = c("counts", "transcript"), ...)

Arguments

approach.for.similarity.calculation	which approach to use for the similarity calculation; defaults to counts
...	arguments to be passed on to noisyr_counts or noisyr_transcript; see their documentation for more details and required arguments

Value

For the counts approach, the denoised expression matrix. For the transcript approach, the numeric vector of noise thresholds per sample. For more details, see their respective documentation.

Examples

noisyr(approach.for.similarity.calculation = "counts",
       expression.matrix = matrix(1:100, ncol = 5))
#> >>> noisyR counts approach pipeline <<<
#> The input matrix has 20 rows and 5 cols
#>     number of genes: 20
#>     number of samples: 5
#> Calculating the number of elements per window
#>     the number of elements per window is 2
#>     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