transform - Count Transformation

Transformations of the distribution of counts in a matrix.

plog

de_toolkit.transform.plog(count_obj, pseudocount=1, base=10)

Logarithmic transform of a counts matrix with fixed pseudocount, i.e. $log(x+c)$

Parameters:count_obj (CountMatrix object) – count matrix object Returns:log transformed counts dataframe with the same dimensionality as input counts Return type:pandas.DataFrame

Command line usage:

Usage:
    detk-transform plog [options] <count_fn>

Options:
    -c N --pseudocount=N   The pseudocount to use when taking the log transform [default:1]
    -b B --base=B          The base of the log to use [default: 10]
    -o FILE --output=FILE  Destination of primary output [default: stdout]

rlog

Command line interface to the DESeq2 Regularized log (rlog) transformation. As in the originating package, the default behavior is to perform a blind transformation, i.e. without respect to an experimental design:

detk-transform rlog norm_counts.csv > rlog_norm_counts.csv

Roughly equivalent to the following R code:

library(DESeq2)

cnts <- as.matrix(read.csv("norm_counts.csv",row.names=1))
fakeColData <- # fake column data...

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

dds <- rlog(dds,blind=True)
write.csv(assay(dds),out.fn)

To perform a non-blind transformation, a formula and column data file may be provided:

detk-transform rlog norm_counts.csv "counts ~ AgeOfDeath + Status" column_data.csv > rlog_norm_counts_nonblind.csv

This invocation is roughly equivalent to the following R code:

library(DESeq2)

cnts <- as.matrix(read.csv("norm_counts.csv",row.names=1))
colData <- read.csv("column_data.csv",header=T,as.is=T,row.names=1)

dds <- DESeqDataSetFromMatrix(countData = cnts,
    colData = colData,
    design = ~ AgeOfDeath + Status
)

dds <- rlog(dds,blind=False)
write.csv(assay(dds),out.fn)

vst

Command line interface to the DESeq2 Regularized log (vst) transformation:

detk-transform vst norm_counts.csv > vst_norm_counts.csv

Roughly equivalent to the following R code:

library(DESeq2)

cnts <- as.matrix(read.csv("norm_counts.csv",row.names=1))
fakeColData <- # fake column data...

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

dds <- vst(dds)
write.csv(assay(dds),out.fn)