summarize_mat: Summarize information in the supplementary matrix

Description

Summarize information in the supplementary matrix according to physical location into a new matrix with the same dimensions as the main matrix

Usage

summarize_mat(mat_main,ann_main,mat_supp,ann_supp,n_limit=50,extend=100000,method="pca")

Arguments

mat_main

The main matrix or data frame. Rows are features (genes/peaks/etc) and cols are samples (conditions/replicates)

ann_main

ann_main is a data frame that contains the genomic locations of features in mat_main. It must have the same number of rows as mat_main, and must have columns named as "chr", "start" and "end".

mat_supp

The supplementary matrix or data frame. Rows are features (genes/peaks/etc) and cols are samples (conditions/replicates)

ann_supp

ann_supp os a data frame that contains the genomic locations of features in mat_supp. It must have the same number of rows as mat_supp, and must have columns named as "chr", "start" and "end".

n_limit

The most number of closet features in the supplemenatry matrix that can be used for summarization for each feature in the main matrix

extend

The genomic features in the supplemenatry matrix that are no farther away than extend bp from the feature in question in the main matrix will be used for summarization

method

Which method to summarize the information in the supplementary matrix when there are >1 neighboring row vectors associated with the row vector in the main matrix. "pca" (default) or "max". In the "max" method, the row vector of these neighboring vectors with the highest correlation with the row vector in the main matrix is used. In the "pca" method, PCA is caculated for these row vectors and the first principal component is used.

Value

A modified matrix with the same dimensions as the main matrix

Details

The main matrix and supplementary matrix must have the same columns corresponding to conditions or replicates. They have different features on rows that can be linked by physical location on genomes. The basic assumption is that one feature's variation in the main matrix is correlated with nearby feature(s)' principal variation in the supplementary matrix.

Examples

Run this code

  data(mancie_example,package="MANCIE")
  sum_DNase=summarize_mat(exp,ann_exp,DNase,ann_DNase) 
  lev_exp=mancie(exp,sum_DNase)