[R] Remove highly correlated variables from a data frame or matrix

[Ana Marija]

if it is of any help my correlation matrix (calc.rho) was done here,
under LDmatrix tab https://ldlink.nci.nih.gov/?tab=ldmatrix
and dataset of 246 is bellow

rs56192520
rs3764410
rs145984817
rs1807401
rs1807402
rs35350506
rs2089177
rs12325677
rs62064624
rs62064631
rs2349295
rs2174369
rs7218554
rs62064634
rs4360974
rs4527060
rs6502526
rs6502527
rs9900318
rs8069906
rs9908521
rs9908336
rs9908870
rs9895995
rs7211086
rs9905280
rs8073305
rs8072086
rs4312350
rs4313843
rs8069610
rs883504
rs8072394
rs4280293
rs4465638
rs12602378
rs9899059
rs6502530
rs4380085
rs6502532
rs4792798
rs4792799
rs4316813
rs148563931
rs74751226
rs8068857
rs8069441
rs77397878
rs75339756
rs4608391
rs79569548
rs4275914
rs11870422
rs8075751
rs11658904
rs138437542
rs80344434
rs7222311
rs7221842
rs7223686
rs78013597
rs74965036
rs78063986
rs118106233
rs117345712
rs113004656
rs9898995
rs4985718
rs9893911
rs79110942
rs7208929
rs12601453
rs4078062
rs75129280
rs76664572
rs78961289
rs146364798
rs76715413
rs4078534
rs79457460
rs74369938
rs76423171
rs74668400
rs75146120
rs1135237
rs9914671
rs117759512
rs4985696
rs16961340
rs17794159
rs4247118
rs78572469
rs12601193
rs2349646
rs2090018
rs12601424
rs4985701
rs8064550
rs2271521
rs2271520
rs11078374
rs4985702
rs1124961
rs11652674
rs3924340
rs112450164
rs7208973
rs9910857
rs78574480
rs8072184
rs12602196
rs6502563
rs3744135
rs148779543
rs77689691
rs41319048
rs117340532
rs78647096
rs77712968
rs16961396
rs80054920
rs7206981
rs4985740
rs3803762
rs77103270
rs7207485
rs77342773
rs3826304
rs3744126
rs7210879
rs7211576
rs117967362
rs75978745
rs6502564
rs9894565
rs36079048
rs8076621
rs7218795
rs3803761
rs12602675
rs7208065
rs4985705
rs8080386
rs8065832
rs2018781
rs1736221
rs1736220
rs1736217
rs1708620
rs1708619
rs1736216
rs76319098
rs1736215
rs1736214
rs1708617
rs12602831
rs12602871
rs1736213
rs1736212
rs76045368
rs34518797
rs11078378
rs8079562
rs8065774
rs8066090
rs41337846
rs1736209
rs1736208
rs12949822
rs76246042
rs12600635
rs55689224
rs1736207
rs1708626
rs1736206
rs9896078
rs16961474
rs1708627
rs1736205
rs1708628
rs7220577
rs2294155
rs1736204
rs1736203
rs1736202
rs12937908
rs1736200
rs1708623
rs1708624
rs9894884
rs9901894
rs9903294
rs2472689
rs1630656
rs111478970
rs3182911
rs7219012
rs9890657
rs12453455
rs12947291
rs150267386
rs16961493
rs11652745
rs9907107
rs8070574
rs4985759
rs3866959
rs7219248
rs6502568
rs7220275
rs12450037
rs7225876
rs9892352
rs4985760
rs6502569
rs1029830
rs2012954
rs1029832
rs2270180
rs8072402
rs7221553
rs145597919
rs150772017
rs2041393
rs6502578
rs11078382
rs9912109
rs12601631
rs11869054
rs11869079
rs9912599
rs7220057
rs9896970
rs34121330
rs34668117
rs67773570
rs242252
rs955893
rs28583584
rs9944423
rs7217764
rs11651957
rs73978990
rs8071007
rs56044345
rs17804843


On Fri, Nov 15, 2019 at 12:03 PM Ana Marija <sokovic.anamarija using gmail.com> wrote:
>
> HI Peter,
>
> Thank you for getting back to me and shedding light on this. I see
> your point, doing Jim's method:
>
> > keeprows<-apply(calc.rho,1,function(x) return(sum(x>0.8)<3))
> > ro246.lt.8<-calc.rho[keeprows,keeprows]
> > ro246.lt.8[ro246.lt.8 == 1] <- NA
> > (mmax <- max(abs(ro246.lt.8), na.rm=TRUE))
> [1] 0.566
>
> Which is good in general, correlations in my matrix  should not be
> exceeding 0.8. I need to run Mendelian Rendomization on it later on so
> I can not be having there highly correlated SNPs. But with Jim's
> method I am only left with 17 SNPs (out of 246) and that means that
> both pairs of highly correlated SNPs are removed and it would be good
> to keep one of those highly correlated ones.
>
> I tried to do your code:
> > tree = hclust(1-calc.rho, method = "average")
> Error in if (is.na(n) || n > 65536L) stop("size cannot be NA nor
> exceed 65536") :
>   missing value where TRUE/FALSE needed
>
> Please advise.
>
> Thanks
> Ana
>
> On Thu, Nov 14, 2019 at 7:37 PM Peter Langfelder
> <peter.langfelder using gmail.com> wrote:
> >
> > I suspect that you want to identify which variables are highly
> > correlated, and then keep only "representative" variables, i.e.,
> > remove redundant ones. This is a bit of a risky procedure but I have
> > done such things before as well sometimes to simplify large sets of
> > highly related variables. If your threshold of 0.8 is approximate, you
> > could simply use average linkage hierarchical clustering with
> > dissimilarity = 1-correlation, cut the tree at the appropriate height
> > (1-0.8=0.2), and from each cluster keep a single representative (e.g.,
> > the one with the highest mean correlation with other members of the
> > cluster). Something along these lines (untested)
> >
> > tree = hclust(1-calc.rho, method = "average")
> > clusts = cutree(tree, h = 0.2)
> > clustLevels = sort(unique(clusts))
> > representatives = unlist(lapply(clustLevels, function(cl)
> > {
> >   inClust = which(clusts==cl);
> >   rho1 = calc.rho[inClust, inClust, drop = FALSE];
> >   repr = inClust[ which.max(colSums(rho1)) ]
> >   repr
> > }))
> >
> > the variable representatives now contains indices of the variables you
> > want to retain, so you could subset the calc.rho matrix as
> > rho.retained = calc.rho[representatives, representatives]
> >
> > I haven't tested the code and it may contain bugs, but something along
> > these lines should get you where you want to be.
> >
> > Oh, and depending on how strict you want to be with the remaining
> > correlations, you could use complete linkage clustering (will retain
> > more variables, some correlations will be above 0.8).
> >
> > Peter
> >
> > On Thu, Nov 14, 2019 at 10:50 AM Ana Marija <sokovic.anamarija using gmail.com> wrote:
> > >
> > > Hello,
> > >
> > > I have a data frame like this (a matrix):
> > > head(calc.rho)
> > >             rs9900318 rs8069906 rs9908521 rs9908336 rs9908870 rs9895995
> > > rs56192520      0.903     0.268     0.327     0.327     0.327     0.582
> > > rs3764410       0.928     0.276     0.336     0.336     0.336     0.598
> > > rs145984817     0.975     0.309     0.371     0.371     0.371     0.638
> > > rs1807401       0.975     0.309     0.371     0.371     0.371     0.638
> > > rs1807402       0.975     0.309     0.371     0.371     0.371     0.638
> > > rs35350506      0.975     0.309     0.371     0.371     0.371     0.638
> > >
> > > > dim(calc.rho)
> > > [1] 246 246
> > >
> > > I would like to remove from this data all highly correlated variables,
> > > with correlation more than 0.8
> > >
> > > I tried this:
> > >
> > > > data<- calc.rho[,!apply(calc.rho,2,function(x) any(abs(x) > 0.80))]
> > > > dim(data)
> > > [1] 246   0
> > >
> > > Can you please advise,
> > >
> > > Thanks
> > > Ana
> > >
> > > But this removes everything.
> > >
> > > ______________________________________________
> > > R-help using r-project.org mailing list -- To UNSUBSCRIBE and more, see
> > > https://stat.ethz.ch/mailman/listinfo/r-help
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> > > and provide commented, minimal, self-contained, reproducible code.