z-transform-sample: transform from raw counts to Poisson-lognormal-based metrics
In texmexseq: Treatment Effect eXplorer for Microbial Ecology eXperiments (using Sequence Counts)

Description Usage Arguments Details Value Author(s) See Also Examples

Functions for transforming raw read counts into rescaled reads (z) and theoretical cumulative distribution function values (f).

    z.transform.sample(n)
    z.transform.table(otu, ignore='otu')
    f.transform.sample(n)
    f.transform.table(otu, ignore='otu')

`n`	vector of observed counts
`otu`	data frame OTU table
`ignore`	columns that should not be transform (e.g., OTU IDs) have names that match this pattern (i.e., those columns are excluded using `dplyr`'s `-matches(ignore)`

z.transform.sample fits the Poisson lognormal distribution to the count data and uses that fit to transform those raw read counts into rescaled reads.

z.transform.table performs the same function on an OTU table, which should have one sample per column and, potentially, an ID column that matches the ignore option (e.g., 'otu' will match OTU or OTU_ID). This function is just for convenience: it applies z.transform.sample to each column (that does not match the ignore option) and packages the result into a data frame. Because it uses a dplyr function, the rownames will be lost.

f.transform.sample and f.transform.table are analogous to the z functions, only they return theoretical cumulative distribution function values.

The resulting tables could be used on their own for analysis, but texmexseq is designed to slice that data into smaller “quads” (using quad.table).

`z.transform.sample`	returns a vector of transformed values
`z.transform.table`	returns a data frame of transformed values

Scott Olesen swo@mit.edu

texmex.fit quad.table

# make up a table of data
sim.data <- function() rpoilog(100, 1.0, 1.0, condS=TRUE)
sample1 <- sim.data()

# transform it
hist(f.transform.sample(sample1))

# make up a table of data
sample2 <- sim.data()
otu.ids <- paste('otu', seq(1, 100), sep='')
otu.table <- data.frame(OTU_ID=otu.ids, sample1=sample1, sample2=sample2)

# make a new table from those fitted values
f.table <- f.transform.table(otu.table)
hist(f.table$sample1)

Loading required package: dplyr

Attaching package: 'dplyr'

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

Loading required package: ggplot2
Warning message:
In value[[3L]](cond) : fit 1 failed
Warning message:
In value[[3L]](cond) : fit 1 failed