ps.cluster: Function to compute the prediction strength of a clustering...

Description Usage Arguments Value Author(s) References Examples

Description

This function computes the prediction strength of a clustering model as published in R. Tibshirani and G. Walther 2005.

Usage

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ps.cluster(cl.tr, cl.ts, na.rm = FALSE)

Arguments

cl.tr

Clusters membership as defined by the original clustering model, i.e. the one that was not fitted on the dataset of interest.

cl.ts

Clusters membership as defined by the clustering model fitted on the dataset of interest.

na.rm

TRUE if missing values should be removed, FALSE otherwise.

Value

ps

the overall prediction strength (minimum of the prediction strengths at cluster level).

ps.cluster

Prediction strength for each cluster

ps.individual

Prediction strength for each sample.

Author(s)

Benjamin Haibe-Kains

References

R. Tibshirani and G. Walther (2005) "Cluster Validation by Prediction Strength", Journal of Computational and Graphical Statistics, 14(3):511–528.

Examples

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## load SSP signature published in Sorlie et al. 2003
data(ssp2003)
## load NKI data
data(nkis)
## SP2003 fitted on NKI
ssp2003.2nkis <- intrinsic.cluster(data=data.nkis, annot=annot.nkis,
  do.mapping=TRUE, std="robust",
  intrinsicg=ssp2003$centroids.map[ ,c("probe", "EntrezGene.ID")],
  number.cluster=5, mins=5, method.cor="spearman",
  method.centroids="mean", verbose=TRUE)
## SP2003 published in Sorlie et al 2003 and applied in VDX
ssp2003.nkis <- intrinsic.cluster.predict(sbt.model=ssp2003,
  data=data.nkis, annot=annot.nkis, do.mapping=TRUE, verbose=TRUE)
## prediction strength of sp2003 clustering model
ps.cluster(cl.tr=ssp2003.2nkis$subtype, cl.ts=ssp2003.nkis$subtype,
  na.rm = FALSE)

Example output

Loading required package: survcomp
Loading required package: survival
Loading required package: prodlim
Loading required package: mclust
Package 'mclust' version 5.4.1
Type 'citation("mclust")' for citing this R package in publications.
Loading required package: limma
Loading required package: biomaRt
Loading required package: iC10
Loading required package: pamr
Loading required package: cluster
Loading required package: iC10TrainingData
Loading required package: AIMS
Loading required package: e1071
Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

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

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following object is masked from 'package:limma':

    plotMA

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

    IQR, mad, sd, var, xtabs

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

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, cbind, colMeans, colSums, colnames, do.call,
    duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
    lapply, lengths, mapply, match, mget, order, paste, pmax, pmax.int,
    pmin, pmin.int, rank, rbind, rowMeans, rowSums, rownames, sapply,
    setdiff, sort, table, tapply, union, unique, unsplit, which,
    which.max, which.min

Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.

76/500 probes are used for clustering
robust standardization of the gene expressions
76/500 probes are used for clustering
no standardization of the gene expressions
the labels of the clusters differ
$ps
[1] 0.2683983

$ps.cluster
    Basal      Her2      LumA      LumB    Normal 
0.8433333 0.7523810 0.6966049 0.2683983 1.0000000 

$ps.individual
   NKI_123    NKI_327    NKI_291    NKI_370    NKI_178    NKI_176    NKI_102 
0.82500000 0.05000000 1.00000000 0.82500000 1.00000000 0.82500000 0.82500000 
   NKI_110    NKI_124    NKI_305     NKI_28    NKI_308    NKI_221    NKI_146 
0.23809524 0.82500000 0.82500000 1.00000000 0.85714286 0.82500000 0.82500000 
   NKI_126    NKI_315    NKI_174    NKI_288    NKI_368    NKI_249    NKI_130 
0.10000000 0.82500000 0.23809524 0.85714286 0.82500000 0.85714286 0.85714286 
   NKI_331    NKI_182    NKI_142    NKI_113    NKI_114    NKI_333     NKI_72 
0.10000000 1.00000000 0.23809524 0.00000000 0.82500000 0.38095238 0.82500000 
   NKI_246    NKI_158    NKI_231    NKI_165    NKI_346    NKI_129    NKI_238 
0.38095238 0.07142857 0.82500000 0.38095238 0.82500000 0.82500000 0.91666667 
   NKI_133    NKI_261    NKI_285    NKI_307    NKI_339    NKI_336    NKI_141 
0.82500000 0.82500000 0.82500000 0.91666667 0.82500000 0.82500000 0.85714286 
   NKI_361     NKI_17      NKI_2     NKI_19    NKI_292    NKI_354    NKI_311 
0.82500000 0.82500000 0.82500000 0.82500000 0.82500000 0.82500000 0.05000000 
    NKI_91    NKI_394     NKI_44     NKI_57    NKI_321    NKI_226    NKI_260 
0.91666667 0.10000000 0.91666667 0.91666667 0.85714286 0.91666667 0.82500000 
   NKI_264     NKI_13     NKI_80    NKI_186    NKI_243    NKI_254    NKI_252 
0.82500000 0.19047619 0.91666667 0.91666667 0.82500000 0.23809524 0.82500000 
   NKI_397    NKI_300    NKI_257     NKI_26    NKI_272    NKI_366    NKI_236 
0.82500000 0.82500000 0.85714286 0.82500000 0.82500000 0.04761905 0.38095238 
   NKI_106    NKI_330    NKI_293    NKI_345    NKI_356    NKI_375    NKI_170 
0.91666667 0.91666667 0.82500000 0.82500000 0.82500000 0.38095238 0.82500000 
    NKI_29     NKI_45     NKI_41    NKI_294    NKI_377    NKI_355      NKI_6 
0.82500000 0.10000000 0.82500000 0.10000000 0.91666667 0.10000000 0.82500000 
   NKI_273    NKI_103    NKI_161    NKI_269    NKI_209    NKI_169    NKI_150 
0.19047619 0.91666667 0.82500000 0.91666667 0.82500000 0.04761905 0.19047619 
   NKI_334    NKI_301    NKI_325    NKI_364    NKI_215    NKI_282    NKI_374 
0.82500000 0.82500000 0.82500000 0.85714286 0.91666667 0.82500000 0.23809524 
   NKI_220     NKI_99    NKI_274    NKI_128    NKI_403    NKI_181    NKI_208 
0.82500000 1.00000000 0.82500000 0.82500000 0.10000000 0.38095238 0.85714286 
   NKI_111    NKI_229    NKI_306    NKI_344    NKI_309    NKI_159    NKI_195 
0.19047619 0.19047619 0.82500000 0.91666667 0.82500000 0.82500000 0.91666667 
   NKI_136     NKI_56    NKI_116    NKI_256    NKI_287    NKI_109    NKI_144 
0.38095238 0.82500000 0.82500000 0.82500000 0.82500000 0.85714286 0.91666667 
     NKI_4     NKI_23     NKI_24    NKI_105     NKI_69     NKI_43    NKI_164 
0.23809524 1.00000000 0.91666667 0.82500000 0.38095238 0.85714286 0.91666667 
   NKI_119    NKI_210     NKI_34    NKI_277    NKI_192     NKI_93    NKI_217 
0.91666667 0.85714286 0.05000000 0.82500000 0.05000000 0.07142857 0.38095238 
   NKI_283    NKI_278     NKI_75    NKI_227    NKI_322    NKI_268    NKI_118 
0.82500000 0.82500000 0.91666667 0.82500000 0.05000000 0.91666667 0.82500000 
   NKI_177    NKI_284    NKI_371    NKI_275     NKI_54    NKI_235    NKI_337 
0.91666667 0.82500000 0.00000000 0.82500000 0.82500000 0.82500000 0.10000000 
    NKI_37    NKI_360    NKI_189 
0.10000000 1.00000000 0.85714286 

genefu documentation built on Nov. 1, 2018, 2:25 a.m.