transform.biom: Apply mathematical transformations to BIOM data

Description

Prepare an object of class biom for further analysis by applying selected transformations with specified parameters.

Usage

# S3 method for biom
transform(`_data`, ...)
t_ColCenter(x, ...)
t_ColScale(x, ...)
t_Log(x, ...)
t_NA2Zero(x, ...)
t_Threshold(x, entry.min=2, row.min=2, col.min=2)

Arguments

_data

an object (biom)

a matrix

entry.min

minimum to retain an entry (numeric)

row.min

minimum sum to retain a row (numeric)

col.min

minimum sum to retain a column (numeric)

…

transformations to apply and arguments to them

Value

Complete technical documentation is forthcoming. For the current preliminary release, please refer to the examples provided.

Details

Complete technical documentation is forthcoming. For the current preliminary release, please refer to the examples provided.

Examples

Run this code

# NOT RUN {
####  simple log-transform
transform (xx1, t_Log)

####  additional filters
transform (xx1, t_NA2Zero, t_Threshold, t_Log)

####  what is lost with more stringent filtering of low-abundance annotations
yy <- transform (xx2, t_NA2Zero, t_Threshold, t_Log)
zz <- transform (xx2, t_NA2Zero, t_Threshold=list(entry.min=5, row.min=10), t_Log)
setdiff (rownames (yy), rownames (zz))

####  each sample centered around zero; scaling columnwise by standard deviation
transform (xx4, t_NA2Zero, t_Threshold, t_Log, t_ColCenter, t_ColScale)

####  defining a new transformation that indicates presence / absence
t_Indicator <- function (x, ...) { ifelse (x,1,0) }
transform (xx1, t_Threshold = list(entry.min=5), t_Indicator)
# }