Calcualate the expression levels and expression levels similarity matrices using the count matrix

This function generates an average similarity (correlation/inverse distance) coefficient for every sliding window, for each sample in the expression matrix. That is done by comparing the distribution of genes in each window across samples.

calculate_expression_similarity_counts(
  expression.matrix,
  similarity.measure = "correlation_pearson",
  n.elements.per.window = NULL,
  n.step = NULL,
  n.step.fraction = 0.05,
  ...
)

Arguments

expression.matrix	the expression matrix, can be normalized or not
similarity.measure	one of the correlation or distance metrics to be used, defaults to pearson correlation; list of all methods in get_methods_correlation_distance
n.elements.per.window	number of elements to have in a window, default 10% of the number of rows
n.step	step size to slide across, default 1% of n.elements.per.window
n.step.fraction	an alternative way to specify the step size, as a fraction of the window length; default is 5%
...	arguments passed on to other methods

Value

A list with three elements: the first element is the expression matrix, as supplied; the other two are the expression levels matrix and expression levels similarity matrix; they have the same # of columns as the expression matrix, and n.elements.per.window * n.step rows.

See also

calculate_expression_similarity_transcript

Examples

calculate_expression_similarity_counts(
    expression.matrix = matrix(1:100, ncol = 5),
    similarity.measure = "correlation_pearson",
    n.elements.per.window = 3)
#> 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
#> $expression.matrix
#>       [,1] [,2] [,3] [,4] [,5]
#>  [1,]    1   21   41   61   81
#>  [2,]    2   22   42   62   82
#>  [3,]    3   23   43   63   83
#>  [4,]    4   24   44   64   84
#>  [5,]    5   25   45   65   85
#>  [6,]    6   26   46   66   86
#>  [7,]    7   27   47   67   87
#>  [8,]    8   28   48   68   88
#>  [9,]    9   29   49   69   89
#> [10,]   10   30   50   70   90
#> [11,]   11   31   51   71   91
#> [12,]   12   32   52   72   92
#> [13,]   13   33   53   73   93
#> [14,]   14   34   54   74   94
#> [15,]   15   35   55   75   95
#> [16,]   16   36   56   76   96
#> [17,]   17   37   57   77   97
#> [18,]   18   38   58   78   98
#> [19,]   19   39   59   79   99
#> [20,]   20   40   60   80  100
#> 
#> $expression.levels
#>       [,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
#> 
#> $expression.levels.similarity
#>       [,1] [,2] [,3] [,4] [,5]
#>  [1,]    1    1    1    1    1
#>  [2,]    1    1    1    1    1
#>  [3,]    1    1    1    1    1
#>  [4,]    1    1    1    1    1
#>  [5,]    1    1    1    1    1
#>  [6,]    1    1    1    1    1
#>  [7,]    1    1    1    1    1
#>  [8,]    1    1    1    1    1
#>  [9,]    1    1    1    1    1
#> [10,]    1    1    1    1    1
#> [11,]    1    1    1    1    1
#> [12,]    1    1    1    1    1
#> [13,]    1    1    1    1    1
#> [14,]    1    1    1    1    1
#> [15,]    1    1    1    1    1
#> [16,]    1    1    1    1    1
#> [17,]    1    1    1    1    1
#> [18,]    1    1    1    1    1
#>