# High Dimensional Data Analysis - Biostatistics

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High dimensional data analysisCavan ReillyOctober 16, 2019

Data miningData mining is the process of searching through large data sets forassociations.If one uses conventional statistical testing methods then one isguaranteed to find some sort of association if one searches hardenough.However these associations frequently won’t be found in anotherindependent sample.Many methods have been developed for this process, and the fieldof inquiry that seeks to develop and apply these tools is referred toas machine learning.

Data miningWe will consider 3 appraoches to high dimensional data analysishere:Irandom forestsIlogic regressionImultivariate adaptive regression splinesHowever there are many other algorithms that have the samepurpose.Hence our treatment is selective and far from comprehensive.

Random forestsRandom forests generate a collection of trees.The result from applying random forests is not a final, best tree,but is a guide to which explanatory variables have the greatestimpact on the outcome.Thus it tells us about variable importance rather than the exactnature of the relationship between the set of predictors and thetrait under investigation.

Random forestsHere are the steps of the algorithm.1. Randomly select about 1/3 of the data with replacement andcall it the out of bag data (OOB data), call the rest of thedata the learning sample (LS).2. Use the LS data to fit a tree without any pruning. However,at each split only use a randomly chosen set of predictors(about 1/3 of the data).3. Use the OOB data to determine the impurity at all terminalnodes, sum these and call this the tree impurity.4. For each predictor used to construct the tree, permute thesubject indicators, recompute the tree impurity, and find thedifference between this tree impurity and the tree impurityfrom the previous step to get the variable importance for eachpredictor.

Random forestsOne then repeats the whole process many times and at the endcomputes the average variable importance for each predictor.By only selecting a subset of variables to use at each split thealgorithm potentially avoids problems due to high levels of LDamong SNPs.By permuting each predictor used and recomputing the treeimpurity the algorithm can assess if a predictor is important for theoverall classification process in a nonparametric fashion.In the R package randomForest, variable importance is reported asthe average over all iterations divided by its standard error.This package also reports the mean of the total node impuritybased on splits involving each predictor.

Random forests in RHere we again set up the Virco data set and examine how all ofthe mutations predict the difference in the fold change for NFVand IDV.We need to get rid of missing data before using the randomForestfunction as it doesn’t allow missing data. library(randomForest) Trait - NFV.Fold - IDV.Fold VircoGeno - data.frame(virco[,substr(names(virco),1,1) "P"]! "-") Trait.c - Trait[!is.na(Trait)] VircoGeno.c - VircoGeno[!is.na(Trait),] RegRF - randomForest(VircoGeno.c, Trait.c, importance TRUE)

Random forests in RFirst we examine the output, then generate a plot to see which arethe important variables. RegRFCall:randomForest(x VircoGeno.c, y Trait.c, importance TRUE)Type of random forest: regressionNumber of trees: 500No. of variables tried at each split: 33Mean of squared residuals: 5547.21% Var explained: 15.9 varImpPlot(RegRF,main "")

34P46P48P98P32P18P37P3P19P30P97P70P76 62P46P82P90P10P94P13P58P3P1P47P57P41P95P30P2P12P70 0e 002e 054e 05IncNodePurity

Random forests in RThe plot indicates that the 5 most important variables are P35,P36, P54, P63 and P73.Recall that the regression based approach identified P35, P46,P54, P58 and P73, so P36 and P63 are novel.Not clear if any of these effects are statistically significant, andwhile one can inspect the individual trees using getTree it is notclear how one would use that information.

Missing data and random forestsIt is not uncommon to have missing genotype data, especially instudies examining many variants.In our example we had to dispose of 90 observations due tomissingness, which was almost 10% of the sample.A simple fix for this is to simply substitute the most commonlyobserved SNP for missing values.This should be conducted within ethnic groups as its validity relieson Hardy Weinberg equilibrium.

Missing data and random forests in RThe randomForest package has functions that allow one to dothis. Trait - NDRM.CH[Race "Caucasian" & !is.na(Race) & !is.na(NDRM.CH)]NamesAkt1Snps - names(fms)[substr(names(fms),1,4) "akt1"]FMSgeno - fms[,is.element(names(fms),NamesAkt1Snps)][Race "Caucasian" & !is.na(Race) & !is.na(NDRM.CH),]dim(FMSgeno)[1] 777 24So we have 777 subjects out of 1154 Caucasians underconsideration at this point.

Missing data and random forests in RLet’s next examine the proportion of missing data for our SNPs. 3)akt1 g15129aakt1 g14803takt1 t22932c0.0690.0670.021akt1 c10744t c12886t akt1 t10726c t12868c akt1 t10598a t12740a0.0710.0750.071akt1 c9756a c11898takt1 t8407gakt1 a7699g0.0660.0690.067akt1 c6148t c8290takt1 c6024t c8166takt1 c5854t c7996t0.0210.0660.021akt1 g288cakt1 g1780a g363aakt1 c832g c3359g0.0210.0210.021akt1 g2347t g205takt1 g2375a g233aakt1 g4362c0.0660.0660.069akt1 c15676takt1 a15756takt1 g20703a0.0210.0210.021akt1 g22187aakt1 a22889gakt1 g23477a0.0710.0210.021

Missing data and random forests in RWe then use the single imputation approach. Here are genotypesat one SNP before and after imputation. FMSgenoRough - na.roughfix(FMSgeno) table(FMSgeno "akt1 t22932c")CC TC TT3 55 665 table(FMSgenoRough "akt1 t22932c")CC TC TT3 55 719Now use the randomForest function on the imputed data set. RandForRough - randomForest(FMSgenoRough,Trait, importance TRUE)

Missing data and random forests in RHere is the output that we previously viewed graphically. RandForRough "importance"[order(RandForRough "importance"[,1], decreasing TRUE),]%IncMSE IncNodePurity26555.977akt1 t10726c t12868c 234.365556akt1 t8407g197.08446615232.624130.83595223870.986akt1 g288cakt1 g14803t122.21607419274.661akt1 g15129a118.01670714555.595akt1 c6148t c8290t111.19622516282.021akt1 a15756t109.98308223851.596akt1 a22889g99.80194026576.974akt1 c832g c3359g95.81314511138.666akt1 t10598a t12740a 94.83504317957.746akt1 g2347t g205t85.28384617545.905akt1 g22187a82.32892822391.77663.1215318805.539akt1 c6024t c8166takt1 a7699g62.9140528162.597akt1 g23477a54.09692319348.334akt1 c9756a c11898t49.6168648575.560akt1 g2375a g233a49.14084117405.400

Missing data and random forests in Rakt1akt1akt1akt1akt1akt1akt1c5854t c7996tc15676tg4362cc10744t c12886tg1780a 66.5574253.5202947.07728478.77016526.413

Missing data and random forestsThere are more sophisticated approaches available than singleimputation.The proximity score between 2 individuals is the proportion of treesfor which those 2 subjects end up in the same terminal node, wedenote this pij for subjects i and j.A better imputation algorithm starts by fitting a random forestusing single imputation.The proximity scores are then calculated.

Missing data and random forestsThen, if genotype data was missing for subject i, for each possiblegenotype, we compute the average proximity score of subjects withthat genotype to subject i.We then assign the genotype with the highest average proximityscore.Then repeat fitting random forests and imputing.After a while fit the final random forest.It’s not clear if this process converges to a final answer or howmany times one should repeat the process.

Missing data and random forests in RWe return to the previous example and use some things we’vealready set up. FMSgenoMI - rfImpute(FMSgeno, Trait) Out-of-bag Tree MSE %Var(y) 300 1260111.05 Out-of-bag Tree MSE %Var(y) 300 1241109.40 Out-of-bag Tree MSE %Var(y) 300 1257110.82 Out-of-bag Tree MSE %Var(y) 300 1253110.46 Out-of-bag Tree MSE %Var(y) 300 1241109.39 RandForFinal - randomForest(FMSgenoMI[,-1], Trait, importance TRUE)

Missing data and random forests in RThe results have the same top SNP, but with a higher level ofimportance. RandForFinal "importance"[order(RandForFinal "importance"[,1], decreasing TRUE),]%IncMSE IncNodePurity26379.565akt1 t10726c t12868c 275.972895215.23960318653.158akt1 t8407gakt1 g288c125.97020123006.316akt1 g14803t118.48459618590.184akt1 g15129a112.63466814430.548akt1 c6148t c8290t110.09868114128.753akt1 a15756t108.35079825081.994akt1 a22889g102.88868427519.912akt1 g2347t g205t99.87409518860.208akt1 t10598a t12740a 99.66804015841.751akt1 g22187a87.85963723192.396akt1 c832g c3359g86.29512511718.348akt1 a7699g80.2722639685.794akt1 c6024t c8166t71.1932258303.793akt1 g2375a g233a56.54555519601.788akt1 g23477a54.65723519967.984

Missing data and random forests in Rakt1akt1akt1akt1akt1akt1akt1c5854t c7996tc9756a c11898tc15676tg4362cc10744t c12886tt22932cg1780a g363aakt1 669215.80419136.1582903.4925.58804531654.745

Haplotype importanceWe also may be interested in assessing the impact of haplotypes onthe trait.As we’ve already discussed how the EM algorithm can be used tocompute the posterior probability of subjects having varioushaplotypes, we can use those ideas to generate haplotypes that wethen supply to a random forest.There is a package called mirf that has functions that allow on touse multiple imputation to sample haplotypes, compute variableimportance measures via random forests, then average over themultiply imputed data sets.But as of the time of the drafting of these notes the packagedoesn’t appear to be supported.

Logic regressionIn logic regression we model the mean of the trait as depending ona sequence of Boolean expressions multiplied by parameters thatindicate the effect of the expression on the mean.A Boolean expression is a sequence of “and”s, “or”s and “not”sthat results in a binary outcome.For example, consider 4 variants that are binary and labeled v1 ,v2 ,v3 and v4 , and suppose that the most common variant is codedas 0 with the less common variant coded 1.Then (v1 0 and v2 0) or (v3 1 and not v4 1) describes acomplicated multilocus haplotype.Associated with this haplotype is a parameter that gives the effectof this haplotype on the mean.

Logic regressionExcept for cases where there are a very small number of variantsunder investigation, the number of possible expressions is too largeto explicitly consider each.Hence in practice, researchers have used either a greedy searchalgorithm or simulated annealing.A greedy search algorithm is an algorithm that always tries toincrease the value it is trying to optimize.Simulated annealing is a very general algorithm that sometimesaccepts losing progress towards the goal of optimizing something.Due to this property, simulated annealing can avoid getting stuckat a local optimum unlike greedy search algorithms.

Logic regression in RFirst we set up the data and remove missing data, then we run alogic regression and create a plot. library(LogicReg) Trait - NFV.Fold - IDV.Fold VircoGeno - data.frame(virco[,substr(names(virco),1,1) "P"]! "-") Trait.c - Trait[!is.na(Trait)] VircoGeno.c - VircoGeno[!is.na(Trait),] VircoLogicReg - logreg(resp Trait.c, bin VircoGeno.c, select 1)

tree 1 out of 1Parameter 544.4273orandP94P58P25

Logic regression in RWe can also examine the output. VircoLogicRegscore 77.475548 -544 * (((not P94) and (not P25)) or (not P58))Note that running the algorithm multiple times gives differentanswers as it uses a randomized search algorithm (simulatedannealing).This implies that we are not using enough iterations, check thehelp file for logreg for extensive treatment of monitoring theprogress of the algorithm.

Logic regression in RWe can also specify multiple trees in the model as follows. VircoLogicRegMult - logreg(resp Trait.c, bin VircoGeno.c, select 2, ntrees 2, nleaves 8)The number of trees in these models is 2The model size is 8The best model with 2 trees of size 8 has a score of 32.4689 plot(VircoLogicRegMult)

tree 1 out of 2 total size is 8Parameter 1575.0385orororP35P77ororP36P84P55P73

Logic regression in RHere is the description of the model. VircoLogicRegMult2 trees with 8 leaves: score is 32.4691580 -1580 * ((((not P35) or P77) or (P36 or P55)) or (P84 or (not P73))) 682 * (P25 and P15)There is also an MCMC based implementation that samples trees.This method determines the importance of a variable bydetermining how frequently it is included in the model.

Logic regression in RWe again use the virco data set but now determine whichmutations in the protease region predict Saquinavir fold change. Trait - SQV.Fold VircoGeno - data.frame(virco[,substr(names(virco),1,1) "P"]! "-") Trait.c - Trait[!is.na(Trait)] VircoGeno.c - VircoGeno[!is.na(Trait),] VircoLogicRegMCMC - logreg(resp Trait.c, bin VircoGeno.c, select 7)This can take a while.

Logic regression in RWe then plot the output to examine which locations are important.plot(sort(VircoLogicRegMCMC single), xlab "Sorted SNPs", ylab "Number of selected models")

1400 1200 1000 800Number of selected models 0204060Sorted SNPs80100

Logic regression in RTo determine the identity of the top 5 scoring loci, we print thenames of the mutations from least to most important. names(VircoGeno)[order(VircoLogicRegMCMC single)][1] "P28" "P98" "P8" "P5" "P99" "P27" "P22" "P70"[13] "P64" "P52" "P56" "P42" "P38" "P40" "P72" "P9"[25] "P76" "P88" "P23" "P14" "P86" "P97" "P34" "P96"[37] "P24" "P10" "P59" "P68" "P46" "P92" "P11" "P95"[49] "P7" "P43" "P36" "P29" "P15" "P73" "P62" "P30"[61] "P81" "P57" "P6" "P66" "P35" "P1" "P26" "P90"[73] "P58" "P2" "P93" "P78" "P50" "P82" "P21" "P84"[85] "P75" "P61" "P33" "P16" "P17" "P25" "P41" "P89""P67""P20""P19""P80""P53""P4"[97] "P94" "P54" "P48"So the top scoring loci are P48, P54, P74, P85 and P51""P79""P65""P3""P47""P13""P74"

Logic regression in RHowever there is extensive variability-when you rerun there will bea different set of top scoring mutations.To use this in practice one needs to increase the number ofiterations using a command like the following prior to running this.mymccontrol - logreg.mc.control(nburn 250000,niter 500000)then one would issue the command VircoLogicRegMCMC - logreg(resp Trait.c, bin VircoGeno.c, select 7, mc.control mymccontrol)

Logic regression in RThis would run a half million iterations rather than the default of25000 which evidently is not enough for this problem.It would take a long time to run this job, but that’s not uncommonwhen using MCMC.The best way to determine if one has run enough iterations is torun the job twice and see if the results agree to within the desiredlevel of precision.For a problem like this that means we should choose enoughiterations so that the top few most important loci are the same for2 different runs.

Multivariate adaptive regression splinesMultivariate adaptive regression splines (MARS) is a method,similar to CART, for building models that explain the variation in atrait.The primary difference is that MARS seeks to build additivemodels based on the set of predictor variables and their statisticalinteractions with one another.The algorithm does this recursively, where at each stage, itincorporates the covariate or pairwise interaction among covariates,that is the best predictor.After it finishes building up the model, it does some backwardelimination to get rid of terms which it deems to be overfitting.

Multivariate adaptive regression splines in RIt is easy to implement using the earth package in R.Here we install and load the package then set up some data fromthe virco data set to conduct the analysis.We create binary predictors as this is what the algorithm isdesigned for, but we can encode SNP data using multiple indicatorvariables. install.packages("earth") library(earth) Trait - NFV.Fold - IDV.Fold VircoGeno - data.frame(virco[,substr(names(virco),1,1) "P"]! "-") Trait.c - Trait[!is.na(Trait)] VircoGeno.c - VircoGeno[!is.na(Trait),]

Multivariate adaptive regression splines in RThen we use the earth function with degree set to 2 to look for2-way interactionsVircoMARS - earth(Trait.c ., data VircoGeno.c,degree 2) summary(VircoMARS) Call: earth(formula Trait.c .,data VircoGeno.c, degree 35TRUE37.86730P76TRUE-32.35013P1TRUE * P25TRUE-784.98992P1TRUE * P73TRUE-31.73965P10TRUE * P35TRUE28.78594P10TRUE * P73TRUE71.69144P15TRUE * P35TRUE-32.09370

Multivariate adaptive regression splines in .4768180.14481254.3088838.92757-41.63952Selected 40 of 100 terms, and 23 of 99 predictorsImportance: P25TRUE, P1TRUE, P35TRUE, P36TRUE, P73TRUE, P54TRUE, P94TRUE, .Number of terms at each degree of interaction: 1 3 36GCV 5173.102RSS 4081272GRSq 0.2173564RSq 0.3660587So the algorithm originally selected 40 terms and 23 remain afterpruning.

Multivariate adaptive regression splines in RWe can see the order of importance of these terms as follows. evimp(VircoMARS)nsubsetsgcvP25TRUE39 100.0P1TRUE38 96.3P35TRUE36 81.8P36TRUE36 81.8P73TRUE36 81.8P54TRUE34 72.3P94TRUE34 72.3P10TRUE34 71.8P84TRUE34 71.8P77TRUE33 70.0P72TRUE31 7.187.487.487.481.081.080.680.679.073.326.114.6

Multivariate adaptive regression splines in RNote that each of these machine learning approaches identifiesdifferent subsets of mutations, although some sites are frequentlyseen.

High dimensional data analysis Cavan Reilly October 16, 2019. Table of contents Data mining Random forests Missing data Logic regression Multivariate adaptive regression splines. Data mining Data mining is the proce

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