Tools to analyze results of fabia

Share:

Description

These tools allow to analyze results of the package fabia. They can be used to identify IBD segment regions and for adjusting the parameters of extractIBDsegments and hapFabia such as ps (top L values for extraction), psZ (top Z values for extraction), inteA (length of histogram bins).

plotL plots the loadings of a fabia result that are above a threshold either as points, histogram or by a smooth scatter plot.

topLZ returns largest L or Z values of a fabia result, where thresholds are given either by a quantile or by a value.

histL supplies a histogram of the loadings obtained by fabia.

Usage

1
2
3
4
5
6
plotL(res,n=1,p=NULL,w=NULL,type="points",
   intervv=500,off=0,t="p",cex=1)

histL(res,n=1,p=NULL,w=NULL,intervv=500,off=0)

topLZ(res,n=1,LZ="L",indices=TRUE,p=NULL,w=NULL)

Arguments

res

fabia result; instance of the class Factorization.

n

the number of the bicluster to consider.

p

the quantile threshold above which values are returned (p or w must be given).

w

the value threshold above which values are returned (p or w must be given).

type

the type of the plot: type=c("points","histogram","smoothScatter").

intervv

length of the interval bins for histograms.

off

offset of the interval bins from zero for histograms.

t

points type for the plot.

cex

size of the points for the plot.

LZ

"L" for loadings L or "Z" for factors Z.

indices

if TRUE (default) indices are given and otherwise values.

Details

plotL plots the loadings of a fabia result that are above a threshold either as points, histogram or by a smooth scatter plot. Thresholds can be given by a quantile or by a value.

topLZ returns largest L or Z indices/values of a fabia result. Thresholds are given by quantile or by a value.

histL computes histogram of the loadings obtained by fabia.

Implementation in R.

Value

plotL: nothing.

topLZ: vector of indices or values depending on the logical parameter indices.

histL: object of class histogram.

Author(s)

Sepp Hochreiter

References

S. Hochreiter et al., ‘FABIA: Factor Analysis for Bicluster Acquisition’, Bioinformatics 26(12):1520-1527, 2010.

See Also

IBDsegment-class, IBDsegmentList-class, analyzeIBDsegments, compareIBDsegmentLists, extractIBDsegments, findDenseRegions, hapFabia, hapFabiaVersion, hapRes, chr1ASW1000G, IBDsegmentList2excel, identifyDuplicates, iterateIntervals, makePipelineFile, matrixPlot, mergeIBDsegmentLists, mergedIBDsegmentList, plotIBDsegment, res, setAnnotation, setStatistics, sim, simu, simulateIBDsegmentsFabia, simulateIBDsegments, split_sparse_matrix, toolsFactorizationClass, vcftoFABIA

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
data(res)

plotL(res,n=1,p=0.95,w=NULL,type="histogram",
   intervv=50,off=0,t="p",cex=1)
plotL(res,n=1,p=0.95,w=NULL,type="points",
   intervv=50,off=0,t="p",cex=1)
plotL(res,n=1,p=NULL,w=0.5,type="points",
   intervv=50,off=0,t="p",cex=1)
plotL(res,n=1,p=0.95,w=NULL,type="smooth",
   intervv=50,off=0,t="p",cex=1)
plotL(res,n=1,p=NULL,w=0.5,type="smooth",
   intervv=50,off=0,t="p",cex=1)

topLZ(res,n=1,LZ="L",indices=TRUE,p=0.95,w=NULL)
topLZ(res,n=1,LZ="L",indices=TRUE,p=NULL,w=0.95)

topLZ(res,n=1,LZ="Z",indices=TRUE,p=0.95,w=NULL)
topLZ(res,n=1,LZ="Z",indices=TRUE,p=NULL,w=0.4)

topLZ(res,n=1,LZ="L",indices=FALSE,p=0.95,w=NULL)
topLZ(res,n=1,LZ="L",indices=FALSE,p=NULL,w=0.95)

topLZ(res,n=1,LZ="Z",indices=FALSE,p=0.95,w=NULL)
topLZ(res,n=1,LZ="Z",indices=FALSE,p=NULL,w=0.4)

h1 <- histL(res,n=1,p=0.9,w=NULL,intervv=50,off=0)
print(h1$counts)
h1 <- histL(res,n=1,p=NULL,w=0.5,intervv=50,off=0)
print(h1$counts)

Want to suggest features or report bugs for rdrr.io? Use the GitHub issue tracker.