seekDEseq: differential expression, using the DESeq2
In Solatar/seeqR:

Description Usage Arguments Details Value Examples

Imports: DESeq2, AnnotationDbi, org.Hs.eg.db, org.Mm.eg.db, biomaRt

seekDEseq(
  Counts,
  Conditions,
  Batches = NA,
  comparison = NA,
  shrink = T,
  exclude = NA,
  Species = "NA",
  id_in = "symbol",
  id_out = "entrezid",
  cond0,
  cond1,
  batchCorrection = T,
  getBiotype = F,
  translate = T
)

`Counts`	table with raw counts, Entrez IDs as rownames, and sample names as colnames
`Conditions`	vector of integers (1,2), indicating which column belongs to which condition. Must be in the same order as the columns. Note that the calculation is condition2/condition1, so a negative log2FC means that the gene is downregulated in condition2.
`Batches`	vector of integers (1,2,3,etc.), indicating which column belongs to which batch. Must be in the same order as the columns. Is used to remove batch effects. Note that two batches must have at least one shared condition.
`comparison`	leave this empty at first, then use one of the comparisons that is given as an output from the first run
`exclude`	vector of integers (1,2,3,etc.), indicating which columns should be excluded from the analysis
`Species`	string, either "human" or "mouse". Is used to generate gene symbols from Entrez IDs.
`id_in`	gene ID provided in the Counts file
`id_out`	gene ID for the output
`cond0`	integer or character same as on of the conditions. Indicates that all samples in the condition are of the control condition (e.g. wt, untreated, etc.) by which the 2nd condition will be divided to get the log2FC. Mixing up condition 1 and 2 will result in opposite signs.
`cond1`	integer or character same as on of the conditions. Indicates that all samples in the condition are of the test condition (e.g. ko, treated, etc.) which will be divided by the control condition.
`getBiotype`	boolean, gets the biotype (miRNA, lncRNA, etc.) for all significantly regulated genes
`translate`	boolean, if TRUE, translates gene IDs from id_in to id_out

indirect batch effect removal: if cond1 and cond2 have differnt batches (all samples of cond1 are from batch1 and all samples of cond2 are from batch2) the batch effect can still be removed if there is a third condition that has samples from batch 1 & 2. These samples have to be in the count matrix and their condition and batches have to be given. DESeq2 will remove the batch effect based on their counts.

A list with 3 items: 1) A data.frame where each row is a gene, and each column is a parameter of information about it (log2FC, symbol, etc.). 2) A DESeq results object from which the data.frame was calculated. 3) A DESeq object from which the DESeq results object calculated.

myCounts <- sqCounts
myCondition <- c(0,0,0,2,2,2,2,3,3,3,3)
myBatch <- c(1,1,1,1,1,2,2,2,2,2,2)
myDE <- seekDEseq(myCounts, Conditions=myCondition, Batches=myBatch, Species="mouse", cond0=0, cond1=3)