m6ALogisticModel: Linear model analysis on RNA modification data.

Description Usage Arguments Details Value Examples

reduce_correlated_rows is a function to define a set of metrics to subset the rows that are highly correlated with its nearby features on genomic scale.

1
2
3

reduce_correlated_rows(SE, cor_method = "spearman", cor_cut_off = 0.8,
  bin_width = 101, reduction_method = "maxSum",
  information_matrix = NULL)

`SE`	A `SummarizedExperiment` with rowRanges being the GRanges of row feaures, and an assay matrix with at least one collumn.
`cor_method`	The method to define correlations between rows of the assay matrix, can be one in "spearman" and "pearson".
`cor_cut_off`	The correlation cut off threshold used to group 2 nearby rows, default is 0.8.
`bin_width`	The bin width to define the closed/neighbooring row features, default is 101.
`reduction_method`	The decision criteria for the grouped correlated rows: "maxSum" : keep the closed and correlated row features with the highest row sums. "maxMad" : keep the closed and correlated row features with the highest row Median Absolute Deviation. "maxInfo": keep the closed and correlated row features with highest row total information defined by `information_matrix`. "random": keep one of the correlated row features randomly.
`information_matrix`	The information matrix used when argument `reduction_method` = "maxInfo".

The correlation between closed row features are caculated, the rows have mutually correlated neighboors are grouped together, only one of the member in the group will be kept using one of the metric defined obove.

The output is a SummarizedExperiment object with subsetted rows compared with the input.