norm - Normalizing Count Matrices

Count normalization strategies.

deseq2 normalization

Normalize the provided counts matrix using the method as implemented in the R package DESeq2. Briefly, each sample is divided by a size factor calculated as the median ratio of each gene count divided by the geometric mean count across all samples. The implementation here is a python port of the R version, and is roughly equivalent to the following R code:

library(DESeq2)

counts <- as.matrix(read.table(counts.fn,row.names=1))
colData <- data.frame(name=seq(ncol(counts)))

dds <- DESeqDataSetFromMatrix(
    countData=counts,
    colData=colData,
    design = ~ 1
)

dds <- estimateSizeFactors(dds)
write.table(counts(dds,normalized=TRUE),norm.counts.fn)

Usage:

Perform counts normalization on the given counts matrix using the method
implemented in the DESeq2 package.

Usage:
    detk-norm deseq2 [options] <counts_fn>

Options:
    -h --help                    Print out this help
    -o FILE --output=FILE        Destination of normalized output in CSV format [default: stdout]
    --size-factors=FILE          Write out the size factors found by the DESeq2
                                 method to two column tab separated file where
                                 the first column is sample name and the second
                                 column is the size factor

library size normalization

Normalize each counts column by the sum of total counts in that column. Usage:

Perform library size normalization on the columns of the given counts matrix.
Counts in each column are divided by the sum of each column.

Usage:
    detk-norm library [options] <counts_fn>

Options:
    -o FILE --output=FILE        Destination of normalized output in CSV format [default: stdout]

fpkm normalization

Normalize each gene count according to the Fragments Per Kilobase per Million reads normalization procedure as described here. Briefly, each count is divided first by the length of the gene in bases divided by 1000, and then divided by the number of reads in the sample divided by one million.

In order to normalize each gene by its effective gene length, detk must be provided the lengths for every gene/feature identifier found in the counts file. These lengths should be supplied in the form of a two-column character delimited text file (tabs, commas, whatever, etc, detk sniffs the format) where the first column is the gene identifier and the second column is the gene length in bases.

• Every gene in the counts file must have an entry in the lengths file
• The lengths file may have unused gene lengths
• The order of genes between files do not have to match

Usage:

Perform Fragments Per Kilobase per Million normalization on the given counts
file. <lengths_fn> should be a delimited file with two columns, the first
being the name of one of the rows in the counts file and the second is the
effective length of the gene/sequence/etc to use in the normalization.

*Note:* Program will throw an error and exit if there are genes/sequences
in the counts file that are not found in the lengths file.

The order of names in the counts and lengths files do *not* have to be the
same.

Usage:
    detk-norm fpkm [options] <counts_fn> <lengths_fn>

Options:
    -o FILE --output=FILE  Destination of normalized output in CSV format [default: stdout]