summary_Aclustering: Create a table that summarizes A-clustering result

In jennyjyounglee/AclustsCCA: A Cluster Based Sparse Canonical Correlation Analayis to Test Associations Between A Multi-Pollutant Mixture and High-Dimentional DNA Methylation

Create a table that summarizes A-clustering result

Description

This function creates a table that summarizes A-clustering result.

Usage

summary_Aclustering(clusters.list, annot)

Arguments

clusters.list

A list of clusters with CpG sites, each item is a cluster that contains a set of probes

annot

A preloaded annotation file that includes columns the below name. For more information, IlluminaHumanMethylation450kanno.ilmn12.hg19. IlmnID: a character vector of IlmnID CHR: a numeric vector of chromosome containing the CpG Coordinate_37: a numeric vector of chromosomal coordinates of the CpG Islands_Name: a character vector of chromosomal coordinates of the CpG Island Relation_to_Island: a character vector of the location of the CpG relative to the CpG island UCSC_RefGene_Name: a character vector of Target gene name(s)

Value

The function returns a table of summary characteristics:

• Total number of sites used for the analysis

• Number of singletons (a cluster with one CpG site)

• Number of clusters (a cluster with at least two sites)

• Number of CpG sites that are clustered

• Number of CpG sites in cluster (min, median, max)

• Base-pair distance between extremes (min, median, max)

• (1) Clusters associated with a single refernece gene

• (2) Clusters associated with a single CpG Island

• (3) Number of resort regions each cluster is associated with (min, median, max)

• (3-1) Clusters associated with a CpG resort : at least one resort regions

• (3-2) Clusters associated with a CpG resort : at least two resort regions

Examples

data(annot)
data(sample.data)
DATA.Y <- sample.data$DATA.Y # row: subjects (n), column: CpG sites (q)

all.clusters.list <- Aclust::assign.to.clusters(betas = t(DATA.Y),annot = annot, dist.type = "spearman", method = "average", thresh.dist = 0.2,max.dist = 1000,bp.thresh.dist = 999)
summary_Aclustering(clusters.list=all.clusters.list,annot=annot)

jennyjyounglee/AclustsCCA documentation built on June 15, 2022, 7:45 p.m.