Cluster the significant genes

sc.cluster.trend() clusters the genes of the gene lists of sc.filter() by the coefficients of their selected models or by their pseudo-bulk counts, as see.genes() of maSigPro does, with hierarchical clustering, k-means or Gaussian mixture models (mclust).

Usage

sc.cluster.trend(
  scmpObj,
  geneSet = "intersect",
  cluster_by = "coeff",
  cluster_method = "hclust",
  hclust.agglo.method = "ward.D",
  kmeans.iter.max = 500,
  hclust.distance = "cor",
  includeInflu = TRUE,
  mclust.k = FALSE,
  k = 9,
  fill_na = "zero",
  use_dim = "col"
)

Arguments

scmpObj

A ScMaSigPro object.

geneSet

The genes to cluster: the name of a list of sc.filter(), "intersect", the genes in every list, or "union", the genes in any list.

cluster_by

What to cluster: the coefficients of the selected models ("coeff") or the pseudo-bulk counts ("counts").

cluster_method

"hclust", "kmeans" or "Mclust", which needs the suggested package mclust.

hclust.agglo.method

The agglomeration method of stats::hclust().

kmeans.iter.max

The maximum number of iterations of stats::kmeans().

hclust.distance

The distance of "hclust": "cor", one minus the correlation of two genes, or a method of stats::dist().

includeInflu

Whether to keep the genes with influential observations (see showInflu()).

mclust.k

Whether "Mclust" chooses the number of clusters instead of taking k.

k

The number of clusters.

fill_na

How missing values are filled before clustering: with "zero", or the "mean" or "median" of their column or row (use_dim).

use_dim

Whether fill_na takes the mean or median by column ("col") or by row ("row").

Value

The object with the cluster of each gene in the Significant slot (class Significant).

References

Conesa A, Nueda MJ, Ferrer A, Talón M (2006). maSigPro: a method to identify significantly differential expression profiles in time-course microarray experiments. Bioinformatics 22(9), 1096-1102. doi:10.1093/bioinformatics/btl056

Scrucca L, Fop M, Murphy TB, Raftery AE (2016). mclust 5: clustering, classification and density estimation using Gaussian finite mixture models. The R Journal 8(1), 289-317. doi:10.32614/RJ-2016-021

See also

plotTrendCluster(), which plots the clusters.

Other workflow steps: sc.filter(), sc.p.vector(), sc.restruct(), sc.set.poly(), sc.squeeze(), sc.t.fit()

Author

Priyansh Srivastava spriyansh29@gmail.com, Ana Conesa and Maria Jose Nueda, mj.nueda@ua.es

Examples

data("scmp.ob", package = "scMaSigPro")
# Cluster the pseudo-bulk counts of the genes of every list
scmp <- sc.cluster.trend(scmp.ob,
    geneSet = "union", cluster_by = "counts", k = 3
)
table(scmpResults(scmp)$cluster)
#> 
#>  1  2  3 
#> 12 12 11