PASCCluster: Cluster analysis

Description Usage Arguments Details Value Author(s) Examples

View source: R/PASCCA.R

Description

Distances of all gene pairs obtained by the function PASCCA, then the distance matrix is further used for clustering by the function PASCCluster. We adopted the widely-used clustering method, hierarchical clustering, to cluster genes, which was implemented by the R function using hclust default parameters.

Usage

1

Arguments

dist

a dissimilarity matrix as produced by the function PASCCA.

nc

umeric scalar (OR a vector) with the number of clusters the tree should be cut into.

plot

plot clustering tree of a hierarchical clustering if the value is TRUE (default: FALSE)

Details

This function PASCCluster has three parameters, dist nc and plot. Based on hierarchical clustering, dist is a distance matrix, nc is the number of clusters and plot is used to plot a cluster denfrogram.

Value

PASCCluster returns a list, including an object of class [stats::hclust] which describes the tree produced by the clustering process and a vector with group memberships by [stats::cutree]. Besides, when the parameter plot is TRUE, it will generate the a dendrogram.

Author(s)

Yuqi Long, Wenbin Ye

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
 ##Loading example data
 data(polyA_example_data2)

 ##Data preprocessing
 pre_data <- PAprocess(data2,log=TRUE)

##Getting information of the samples
sample_name <- colnames(pre_data)[3:ncol(pre_data)]
sample_name <- strsplit(sample_name,"\\d$")
sample_name <- paste("",lapply(sample_name,"[[",1),sep="");
table(sample_name)
##Getting the number of repetitions per sample
sample_replicates <- as.numeric(table(sample_name))
sample_replicates <- sample_replicates[order(sample_replicates,decreasing = TRUE)]

##Calculationg PASCCA distance matrix
gene_dist <- PASCCA(pre_data, alpha = 0.05,
                   repli=sample_replicates,
                   tissues=length(unique(sample_name)),
                   tiss=sum(sample_replicates==sample_replicates[1]))

##Hierarchical clustering
gene_cluster <- PASCCluster(gene_dist,nc=5,plot = TRUE)

BMILAB/PASCCA documentation built on Nov. 20, 2020, 11:32 p.m.