coseqRun: Co-expression analysis

In andreamrau/coseq: Co-Expression Analysis of Sequencing Data

Description

Function for primary code to perform co-expression analysis, with or without data transformation, using mixture models. The output of coseqRun is an S4 object of class coseqResults.

Usage

coseqRun(
  y,
  K,
  conds = NULL,
  normFactors = "TMM",
  model = "kmeans",
  transformation = "logclr",
  subset = NULL,
  meanFilterCutoff = 50,
  modelChoice = ifelse(model == "kmeans", "DDSE", "ICL"),
  parallel = FALSE,
  BPPARAM = bpparam(),
  seed = NULL,
  ...
)

Arguments

y	(n x q) matrix of observed counts for n observations (genes) and q variables (samples). In nearly all cases, n > q.
K	Number of clusters (a single value or a vector of values)
conds	Vector of length q defining the condition (treatment group) for each variable (column) in y
normFactors	The type of estimator to be used to normalize for differences in library size: (“TC” for total count, “UQ” for upper quantile, “Med” for median, “DESeq” for the normalization method in the DESeq package, and “TMM” for the TMM normalization method (Robinson and Oshlack, 2010). Can also be a vector (of length q) containing pre-estimated library size estimates for each sample, or “none” if no normalization is required.
model	Type of mixture model to use (“Poisson” or “Normal”), or alternatively “kmeans” for a K-means algorithm
transformation	Transformation type to be used: “voom”, “logRPKM” (if geneLength is provided by user), “arcsin”, “logit”, “logMedianRef”, “profile”, “logclr”, “clr”, “alr”, “ilr”, or “none”
subset	Optional vector providing the indices of a subset of genes that should be used for the co-expression analysis (i.e., row indices of the data matrix y. For the generic function coseq, the results of a previously run differential analysis may be used to select a subset of genes on which to perform the co-expression analysis. If this is desired, subset.index can also be an object of class DESeqResults (from the results function in DESeq2).
meanFilterCutoff	Value used to filter low mean normalized counts if desired (by default, set to a value of 50)
modelChoice	Criterion used to select the best model. For Gaussian mixture models, “ICL” (integrated completed likelihood criterion) is currently supported. For Poisson mixture models, “ICL”, “BIC” (Bayesian information criterion), and a non-asymptotic criterion calibrated via the slope heuristics using either the “DDSE” (data-driven slope estimation) or “Djump” (dimension jump) approaches may be used. See the HTSCluster package documentation for more details about the slope heuristics approaches.
parallel	If FALSE, no parallelization. If TRUE, parallel execution using BiocParallel (see next argument BPPARAM). A note on running in parallel using BiocParallel: it may be advantageous to remove large, unneeded objects from the current R environment before calling the function, as it is possible that R's internal garbage collection will copy these files while running on worker nodes.
BPPARAM	Optional parameter object passed internally to bplapply when parallel=TRUE. If not specified, the parameters last registered with register will be used.
seed	If desired, an integer defining the seed of the random number generator. If NULL, a random seed is used.
...	Additional optional parameters.

Value

An S4 object of class coseqResults whose assays contain a SimpleList object, where each element in the list corresponds to the conditional probabilities of cluster membership for each gene in each model. Meta data (accessible via metatdata include the model used (either Normal or Poisson), the transformation used on the data, the transformed data using to estimate model (tcounts), the normalized profiles for use in plotting (y_profiles), and the normalization factors used in the analysis (normFactors).

Author(s)

Andrea Rau

Examples

## Simulate toy data, n = 300 observations
set.seed(12345)
countmat <- matrix(runif(300*4, min=0, max=500), nrow=300, ncol=4)
countmat <- countmat[which(rowSums(countmat) > 0),]
conds <- rep(c("A","B","C","D"), each=2)

## Run the K-means for K = 2,3,4 with logCLR transformation
## The following are equivalent:
run <- coseqRun(y=countmat, K=2:15)
run <- coseq(object=countmat, K=2:15, transformation="logclr", model="kmeans")

## Run the Normal mixture model for K = 2,3,4 with arcsine transformation
## The following are equivalent:
run <- coseqRun(y=countmat, K=2:4, iter=5, transformation="arcsin", model="Normal")
run <- coseq(object=countmat, K=2:4, iter=5, transformation="arcsin", model="Normal")

andreamrau/coseq documentation built on July 25, 2021, 10:17 a.m.