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wrMisc (version 1.8.1)

Analyze Experimental High-Throughput (Omics) Data

Description

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.

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Version

Install

install.packages('wrMisc')

Monthly Downloads

1,813

Version

1.8.1

License

GPL-3

Maintainer

Wolfgang Raffelsberger

Last Published

January 24th, 2022

Functions in wrMisc (1.8.1)

Express difference as ppm

Linear rescaling of numeric vertor or matrix

Append vectors or lists, without duplcating common elements

checkGrpOrderSEM

Check order of multiple groups including non-overlapping SEM-margins

Compare means of two vectors by permutation test

Check how multiple groups of data separate or overlap based on mean +/- sd

sd for each column

contribToContigPerFrag

Characterize individual contribution of single edges in tree-structures

checkSimValueInSer

Check for similar values in series

Complete list of arrays for same dimensions

combinatIntTable

Planing for making all multiplicative combinations

colorAccording2

Transform numeric values to color-gradient

Correct mixed slash and backslash in file path

countCloseToLimits

Count from two vectors number of values close within given limits

combineByEitherFactor

Create factor-like column regrouping data regrouping simultaneaously by two factors

extrNumericFromMatr

Extract numeric part of matrix or data.frame

correctToUnique

Correct vector to unique

Eliminate close (overlapping) points (in x & y space)

get coordinates of values/points in matrix according to filtering condition

Extract specific text

Connect edges to form tree and extract all possible branches

cbind to non-redundant

difference in ppm between numeric values

Compute matrix of differences for all pairwise combinations of numeric vector

get1stOfRepeatedByCol

Get first of repeated by column

extractLast2numericParts

Extract last two numeric parts from character vector

linRegrParamAndPVal

Fit linear regression, return parameters and p-values

listBatchReplace

Replacements in list

cleanReplicates

Replace most distant values by NA

Organize data as separate list-entries

Filter a three-dimensional array of numeric data

getValuesByUnique

Print matrix-content as plot

Standard error of median for each column by bootstrap

Transform columns of matrix to list of vectors

matchSampToPairw

Match names to concatenated pairs of names

closeMatchMatrix

Reorganize results of search for close (similar) values in matrix-view

checkVectLength

Check length of vector

convColorToTransp

Assign new transparency to given colors

Convert matrix of integer to matrix of x-times repeated column-names

Cut character-vector at multiple sites

checkStrictOrder

Check for strict (ascencing or descending) order

Get all combinations with TRUE from each column

combineReplFromListToMatr

Combine replicates from list to matrix

Equal character-length number

findSimilFrom2sets

Find similar numeric values from two vectors/matrixes

Number of fragments after cut at specific character(s)

Count number of non-numeric characters

Advanced paste-collapse

Multiple moderated pair-wise t-tests from limma

cutArrayInCluLike

Cut 3-dim array in list of matrixes (or arrays) similar to organizing into clusters

countSameStartEnd

Count same start- and end- sites of edges (or fragments)

Flexible extraction of columns

Filter for unique elements

Extract just one series, ie channel, of list of arrays

findUsableGroupRange

Select groups within given range

Convert ulr-name for reading in raw-mode

combineRedBasedOnCol

Combine/reduce redundant lines based on specified column

combineOverlapInfo

Find and combine points located very close in x/y space

multiCharReplace

Multiple replacement of entire character elements in simple vector, matrix or data.frame

Cut numeric vector to n groups (ie convert to factor)

Filter for unique elements

filterLiColDeList

Filter lines(rows) and/or columns from all suitable elements of list

Convert matrix (eg with redundant) row-names to data.frame

htmlSpecCharConv

Html special character conversion

Exclude extreme values (based on distance to mean)

Convert vector to numeric

firstOfRepeated

Find first of repeated elements

Fuse annotation matrix to initial matrix

listGroupsByNames

Organize values into list and sort by names

regrMultBy1or2point

Rescaling of multiple data-sets according to reference data using regression

Filter lines of matrix for max number of NAs

Normalize by adjusting exponent

fuseCommonListElem

Fuse content of list-elements with redundant (duplicated) names

Filter nodes & edges for extracting networks This function allows extracting and filtering network-data based on fixed threshold () and add sandwich-nodes (nodes inter-connecting initial nodes) out of node-based queries.

Non-redundant lines of matrix

nonAmbiguousNum

make numeric vector non-ambiguous (ie unique)

make MA-List object

Fuse pairs to generate cluster-names

Find repeated elements

Find close numeric values between two vectors

Make non-redundant matrix

Run lm on segmented data (from clustering)

matchNamesWithReverseParts

Value Matching with optional reversing of sub-parts of non-matching elements

Calculate all ratios between x and y

Extended version of merge for multiple objects (even without rownames)

Merge Named Vectors

Minimum distance/difference between values

nonAmbiguousMat

Transform matrix to non-ambiguous matrix (in respect to given column)

Distance of categorical data (Jaccard,Rand and adjusted Rand index)

rankToContigTab

Contingenty tables for fit of ranking

Moderated pair-wise t-test from limma

System-date (compressed format)

Summarize columns (as median,mean,min,last or other methods)

Scale data to given minimum and maxiumum

searchDataPairs

Search duplicated data over multiple columns, ie pairs of data

Convert ratio to ppm

Merge selected columns out of 2 matrix or data.frames

uniqCountReport

Report number of unique and redundant elements (optional figure)

(upper) pairwise x,y combinations

Normal random number generation with close fit to expected mean and sd

Convert p-values to lfdr

Filter matrix to keep only first of repeated lines

firstOfRepLines

Reduce to first occurance of repeated lines

Transform (factor) levels into index

Test multiple starting levels for linear regression model, select best and plot

Read batch of csv-files

pairsAsPropensMatr

Convert pairs of node-names to propensity matrix (non-oriented) Numerous network query tools produce a listing of pairs of nodes (with one pair of nodes per line). Using this function such a matrix (or data.frame) can be combined to this more comprehensive view as propensity-matrix. Note, this has been primarily developed for undireted interaction networks, the resulting propensity-matrix does not show any orientation any more. In a number of applications (eg in protein-protein interaction networks, PPI) the resulting matrix may be rather sparse.

Read tabular content of files with variable number of columns

nonredDataFrame

Filter for unique elements

readTabulatedBatch

Batch reading of tabulated (text-) files This function allows batch reading of multiple tabulated text files. The files can be designed specifically, alternatively all files from a given directory can be read. If package data.table is available, fast reading of files will be performed using the function fread.

sd of median for each row by bootstrap

Per line and per group sd-values

SEM for each row

Count number of NAs per row and group of columns

Simple Multi-to-Multi Matching of (Concatenated) Terms

organizeAsListOfRepl

(re)organize data of (3-dim) array as list of replicates

Normalize data in various modes

CV of replicate plates (list of matrixes)

numPairDeColNames

Extract pair of numeric values from vector or column-names

Number of fragments after cut at specific character(s) within size-range

treatTxtDuplicates

Locate duplicates in text and make non-redundant

Estimate mode (most frequent value)

Standardize (scale) data

Read batch of Excel xlsx-files

Partial unlist of lists of lists

Standard eror of median by boot-strap

Count number of NAs per sub-set of columns

Remove lines of matrix redundant /duplicated for 1st and 2nd column

Pairwise x,y combinations

Reduce table by aggregating smaller groups

Partial distance matrix (focus on closest)

Write (and convert) csv files

Check for values within range of reference

searchLinesAtGivenSlope

Search points forming lines at given slope

2-factorial limma-style t-test

rowMeans with destinction of groups (of columns, eg groups of replicates)

Simple figure showing line from start- to end-sites of edges (or fragments) defined by their start- and end-sites simpleFragFig draws figure showing start- and end-sites of edges (or fragments)

sortBy2CategorAnd1IntCol

Sort matrix by two categorical and one integer columns

singleLineAnova

2-factorial Anova on single line of data

regrBy1or2point

Rescaling according to reference data using linear regression.

Make single vector gray-gradient

sampNoDeMArrayLM

Locate sample index from index or name of pair-wise comparisons in list or MArrayLM-object

Replace NAs by low values

Reorganize matrix according to clustering-output

sd for each row (fast execution)

t.test on all individual values against all other values

Print matrix-content as plot

addBeforFileExtension

Add text before file-extension