makeMAList

(factor) factor orgnaizing columns of 'mat' (if <code>useF</code> contains the default 'R' and 'G', they should also be part of <code>MAfac</code>)

MAfac

(character) two specific factor-leves of <code>MAfac</code> that will be used/extracted

useF

(logical) tell if data is already log2 (will be considered when computing M and A values)

isLog

silent

(character) allows easier tracking of message(s) produced

callFrom

<code>makeMAList</code> extracts sets of data-pairs (like R &amp; G series) and makes MA objects as <code>MA-List object</code> (eg for ratio oriented analysis).
The grouping of columns as sets of replicate-measurements is done according to argumnet <code>'MAfac'</code>.
The output is fully compatible to functions of package <a href="https://bioconductor.org/packages/release/bioc/html/limma.html">limma</a> (Bioconductor).

The efficient treatment and convenient analysis of experimental high-throughput (omics) data gets facilitated through this collection of diverse functions. Several functions address advanced object-conversions, like manipulating lists of lists or lists of arrays, reorganizing lists to arrays or into separate vectors, merging of multiple entries, etc.  Another set of functions provides speed-optimized calculation of standard deviation (sd), coefficient of variance (CV) or standard error of the mean (SEM) for data in matrixes or means per line with respect to additional grouping (eg n groups of replicates). Other functions facilitate dealing with non-redundant information, by indexing unique, adding counters to redundant or eliminating lines with respect redundancy in a given reference-column, etc. Help is provided to identify very closely matching numeric values to generate (partial) distance matrixes for very big data in a memory efficient manner or to reduce the complexity of large data-sets by combining very close values. Many times large experimental datasets need some additional filtering, adequate functions are provided. Batch reading (or writing) of sets of files and combining data to arrays is supported, too. Convenient data normalization is supported in various different modes, parameter estimation via permutations or boot-strap as well as flexible testing of multiple pair-wise combinations using the framework of 'limma' is provided, too.

Wolfgang Raffelsberger

wrMisc

Analyze Experimental High-Throughput (Omics) Data

makeMAList function

<code>makeMAList</code> extracts sets of data-pairs (like R &amp; G series) and makes MA objects as <code>MA-List object</code> (eg for ratio oriented analysis).
The grouping of columns as sets of replicate-measurements is done according to argumnet <code>'MAfac'</code>.
The output is fully compatible to functions of package <a href='https://bioconductor.org/packages/release/bioc/html/limma.html'>limma</a> (Bioconductor).

makeMAList: make MA-List object

Description

Usage

Arguments

Value

See Also

Examples