This is the primary user interface for the coseq
package.
Generic S3 functions are implemented to perform coexpression or coabudance analysis of
highthroughput sequencing data, with or without data transformation, using mixture models.
The supported classes are matrix
, data.frame
, DESeqDataSet
,
DGEList
, DGEExact
, DGEGLM
, and DGELRT
. The output of coseq
is an S3 object of class coseq
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18  coseq(y, K, subset = NULL, model = "Normal", transformation = "none",
norm = "TMM", meanFilterCutoff = NULL, modelChoice = "ICL",
parallel = FALSE, BPPARAM = bpparam(), ...)
## S3 method for class 'matrix'
coseq(y, K, subset = NULL, model = "Normal",
transformation = "none", norm = "TMM", meanFilterCutoff = NULL,
modelChoice = "ICL", parallel = FALSE, BPPARAM = bpparam(), ...)
## S3 method for class 'data.frame'
coseq(y, K, subset = NULL, model = "Normal",
transformation = "arcsin", norm = "TMM", meanFilterCutoff = NULL,
modelChoice = "ICL", parallel = FALSE, BPPARAM = bpparam(), ...)
## S3 method for class 'DESeqDataSet'
coseq(y, K, subset = NULL, model = "Normal",
transformation = "arcsin", norm = "TMM", meanFilterCutoff = NULL,
modelChoice = "ICL", parallel = FALSE, BPPARAM = bpparam(), ...)

y 
(n x q) matrix of observed counts for n observations and q variables 
K 
Number of clusters (a single value or a vector of values) 
subset 
Optional vector providing the indices of a subset of
genes that should be used for the coexpression analysis (i.e., row indices
of the data matrix 
model 
Type of mixture model to use (“ 
transformation 
Transformation type to be used: “ 
norm 
The type of estimator to be used to normalize for differences in
library size: (“ 
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,
“ 
parallel 
If 
BPPARAM 
Optional parameter object passed internally to 
... 
Additional optional parameters. 
An S3 object of class coseq
containing the following:
results 
Object of class 
model 
Model used, either 
transformation 
Transformation used on the data 
tcounts 
Transformed data using to estimate model 
y_profiles 
Normalized profiles for use in plotting 
matrix
: Perform coseq analysis for matrix
class
data.frame
: Perform coseq analysis for data.frame
class
DESeqDataSet
: Perform coseq analysis for DESeqDataSet
class from DESeq2
package
Andrea Rau
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19  ## 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 Normal mixture model for K = 2,3,4
run_arcsin < coseq(y=countmat, K=2:4, iter=5, transformation="arcsin")
## Plot and summarize results
plot(run_arcsin)
summary(run_arcsin)
## Compare ARI values for all models (no plot generated here)
ARI < compareARI(run_arcsin, plot=FALSE)
## Compare ICL values for models with arcsin and logit transformations
run_logit < coseq(y=countmat, K=2:4, iter=5, transformation="logit")
compareICL(list(run_arcsin, run_logit))

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.
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