Cluster the counts or coefficients.

Source: R/sc.cluster.trend.R

This function clusters the counts or coefficients to visualize the collective trends later.

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,
  verbose = FALSE,
  fill_na = "zero",
  use_dim = "col"
)

Arguments

scmpObj

An object of class ScMaSigPro.

geneSet

Specify the gene set to be used for clustering. (Default is "intersect")

cluster_by

Whether to use counts or coefficients for clustering.

cluster_method

Clustering method for data partioning. Currently "hclust", "kmeans" and "Mclust" are supported.

hclust.agglo.method

Aggregation Method. (Default is "ward.D")

kmeans.iter.max

Maximum number of iterations when cluster.method is kmeans

hclust.distance

Distance measurement.(Default is "cor")

includeInflu

Whether to include genes with influential observations.

mclust.k

TRUE for computing the optimal number of clusters with Mclust algorithm. (Default is FALSE)

k

Number of clusters for data partioning.

verbose

Print detailed output in the console. (Default is TRUE)

fill_na

Fill the NAs (if Present), by zero, mean or median.

use_dim

Whether to use rows or columns for taking mean or median while filling the NAs with `fill_na`.

Value

An object of class ScMaSigPro, with updated `Significant` slot with clusters.

References

Conesa, A., Nueda M.J., Alberto Ferrer, A., Talon, T. 2006. maSigPro: a Method to Identify Significant Differential Expression Profiles in Time-Course Microarray Experiments. Bioinformatics 22, 1096-1102

See also

`maSigPro::see.genes()`

Author

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