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Removing Unwanted Variation in Machine Learning for Personalized Medicine with Johann Gagnon-Bartsch and Laurent Jacob European Marie Curie Network for MLPM. Barcelona, 20 May 2016 1

  1. 
 
 
 Removing Unwanted Variation in 
 Machine Learning for Personalized Medicine 
 with Johann Gagnon-Bartsch and Laurent Jacob 
 
 European Marie Curie Network for MLPM. Barcelona, 20 May 2016 1 Photo:&Bernard&Gagnon&

  2. Apology, Motivation and 
 Declaration of Conflict of Interest 2& SBA73&from&Sabadell,&Catalunya&

  3. Over&500,000&thyroid&nodule&fine&needle&aspiraFon&&& (FNA)&procedures&were&performed&in&the&US&in&2011.&& FNA&samples&can&be&challenging&to&interpret&and&produce& indeterminate&results&in&15%&to&30%&of&cases .& Guidelines&recommended&that&most&of&these&paFents&undergo&a&& diagnosFc&thyroid&surgery&to&assess&whether&the&nodules&are&benign&or& &malignant.&70%Q80%&of&the&Fme,&the&nodules&prove&to&be&benign.& The&Afirma&Gene&Expression&Classifier&(GEC),&helps&physicians&reduce&the&& number&of&surgeries&by&preoperaFvely&idenFfying&benign&nodules&& among&those&that&were&classified&by&cytopathology&as&indeterminate.& &

  4. Over&500,000&thyroid&nodule&fine&needle&aspiraFon&&& (FNA)&procedures&were&performed&in&the&US&in&2011.&& FNA&samples&can&be&challenging&to&interpret&and&produce& indeterminate&results&in&15%&to&30%&of&cases .& & Guidelines&recommended&that&most&of&these&paFents&undergo&a&& I’m&on&the&ScienFfic&Advisory&Board&of&Veracyte&& diagnosFc&thyroid&surgery&to&assess&whether&the&nodules&are&benign&or& and&receive&money&from&them.&& &malignant.&70%Q80%&of&the&Fme,&the&nodules&prove&to&be&benign.& & & The&Afirma&Gene&Expression&Classifier&(GEC),&helps&physicians&reduce&the&& #&of&avoidable&surgeries&by&preoperaFvely&idenFfying&benign&nodules&& among&those&that&were&classified&by&cytopathology&as&indeterminate.& &

  5. Introduction to our RUV methods 10&

  6. 
 The problem 
 High-dimensional (e.g. omic or fMRI) data can be affected by unwanted variation. For example, batch effects due to time, space, equipment, operators, reagents, sample source, sample quality, environmental conditions,… the list goes on … 11

  7. Artifact can overwhelm biology PC2& !batch!1! !batch!2! Sample&principal& component&scores& PC1& Gene&expression&data.&Adapted&from&Lazar&C& et#al.## Brief&Bioinform& 2013#

  8. Some scientific goals sought using 
 gene expression microarrays 
 Differential Expression Classification Clustering & Unwanted&variaFon&can&reduce&precision&and&add&bias&& (via&confounding),&leading&to&false&posiFves&and&false&& negaFves,&&poor&classifiers&and&arFficial&clusters.& & 13

  9. Aim for today To discuss some new ways of • identifying and removing (i.e. adjusting for) unwanted factors, when the goal is classification , and • telling whether or not it helped. 14

  10. “Our” model (brief refs later) m (10s-1,000s) samples, n (10s of 1,000s) genes, k ( ≤ m-p) UV factors Y m × n = X m × p β p × n + W m × k α k × n + ε m × n where Y is a matrix of gene expression measurents, observed, X carries the factors of interest, observed in a training set, unobserved in a test set β are gene coefficients, unobserved, W carries unwanted variation factors, unobserved, α are gene coefficients, unobserved, ε are errors, unobserved . 15&

  11. Concrete example With our Afirma-T example, we could put x i =-1 if sample i is benign, x i = +1 if sample i is malignant. The w i for this example could capture batch effects in reagents, in chips, processing dates, operators, and other things (remember: we’re treating them as unobserved. 16&

  12. Our model in pictures β α# n# n# Y# X# ε# W# m# m#p# m# m# n# n# k# y ij ######=#########x i β j ###########+###########w i α j ##########+########ε ij# The& ε ij# are&all& (0,#σ 2 j ) ,&uncorrelated # with&each&other&and&all&else.& We&resist&the&temptaFon&to&make&assumpFons&about&the& {α j }.#

  13. Our goal: classification That is, we have y but don’t know X (or W) for our test and target set samples. Before we get there, we’ll discuss estimating β as we would in a training set with known X . 18&

  14. Our model, 2 Y m × n = X m × p β p × n + W m × k α k × n + ε m × n Initial goal: to estimate β Note: W unobserved, o/w standard linear model “Our” strategy: use factor analysis to estimate W 19&

  15. Some ways of dealing with these and related problems with microarrays • Standard linear regression (many) • EB linear regression (ComBat, Johnson et al , 2007) • Naïve factor analysis ( SVD, several ) • Bayes (Lucas et al, 2006, Stegle et al , 2008) • Surrogate Variable Analysis (Leek & Storey, 2007) • Mixed model analysis (Kang et al, 2008, Listgarten et al, 2012) 20&

  16. Identifiability: we don’t know the correlation of W ( k=1 ) with X Two&samples& x 1 #=#w 1 #=#1# x 2 #=#x,#w 2 #=w# Dots&are&genes& & (y Ij ,y 2j# )#=#( β j + α j + ε 1j , x β j + w α j + ε 2j ) 21&

  17. We might have genes j not affected by X (y Ij ,y 2j# )#=#( α j + ε 1j , w α j + ε 2j ) 22&

  18. We might have genes j not affected by X (y Ij ,y 2j# )#=#( α j + ε 1j , w α j + ε 2j ) 23&

  19. We might have genes j not affected by X Nega,ve!controls :&genes&whose&expression&is&not&associated& with&the&biological&factors&of&interest&embodied&in #X# (y Ij ,y 2j# )#=#( α j + ε 1j , w α j + ε 2j ) 24&

  20. “Our” solution: Use control genes Negative controls: Assume β j = 0. 0# α c# Y c# ε c# PosiFve&controls:&Assume&& β j #≠#0.# & “controls” in this context means “controls w.r.t. differential expression” 25&

  21. 
 
 Using the negative controls c 
 Y c = W α c + ε c Just do a factor analysis on the negative controls! Examples of negative controls • housekeeping (HK) genes, • spiked-in controls • suitable empirical controls This works! 26&

  22. Introducing the two-step: RUV-2 1. Do a factor analysis on Y c to estimate W. 2. Then regress Y on X and W ^ , the estimated W, to get an estimate of β adjusted for W ^ . There are many ways to do the factor analysis, but we just use SVD: Write Y c# =#UΛV T #,## then&put& W ^ #=#U (k) ## (first#k#columns) & Issues: choice of k, and can we do better? Yes: RUV-4 27&

  23. Introducing RUV-inv We&start&with&RUVQ4&(UCB&Stat&Tech&Rep&820),&and&put&&&& & k=mN1# &(the&largest&&possible&value&when& p=1 ).&&We&don’t&& need&an&SVD,&and&we&find& & β RUV − inv = [ X t ( Y c Y c ˆ t ) − 1 X ] − 1 X t ( Y c Y c t ) − 1 Y This&is&the&generalized&least&squares&esFmator&using&&a& covariance&matrix&based&on&data&from&the&negaFve&control& genes&(others&use&all&genes),&but&we&esFmate&SEs&differently.& 28&

  24. A microarray experiment with central 
 retina tissue from the rd1 mouse: 4 times x 3 rd1# is&a&mouse&model&of& rePniPs#pigmentosa:# loss&of&rod& photoreceptors,&followed&by&that&of&cone&photoreceptors& Light&blue:&2&months& Dark&blue:&4&months& &&&&&&&&&&&&Principal&component&2&&& Purple:&6&months& Red:&&8&months& & Very!severe!! & batch!effects! & Ideally&we&would&have&& seen&4&Fght&groups&of&& 3& ! ,& ! ,& ! &and& ! &resp.& Principal&component&1&&

  25. Removing severe batch effects • Initially no significantly downregulated retinal genes were found between 2 and 8 months (left volcano plot on the next slide). • Using RUV-inv (right plot), we were able to find several significantly down-regulated retinal, even cone-specific genes, which were later confirmed. 30&

  26. Standard analysis Green!dots :&genes& expressed&in&the&reFna& Q log 10# (pNvalue)# log 2 (fold#change)#8m/2m############### 31& #

  27. Standard analysis Analysis with RUVinv Green!dots :&genes& expressed&in&the&reFna& Q log 10# (pNvalue)# Q log 10# (pNvalue)# log 2 (fold#change)#8m/2m#############log 2 (fold#change)#8m/2m## 32& #

  28. Are there any questions? 33&

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