# Fisher’s exact test failure can lead to biased results

Robersy Sanchez

Department of Biology. Pennsylvania State University, University Park, PA 16802

Email: rus547@psu.edu

Fisher’s exact test is a statistical significance test used in the analysis of contingency tables. Although this test is routinely used even though, it has been full of with controversy for over 80 years. Herein, the case of its application analyzed is scrutinized with specific examples.

# Overwiew

The statistical significance of the difference between two bisulfite sequence from control and treatment groups at each CG site can be evaluated with Fisher’s exact test. This is a statistical test used to determine if there are nonrandom associations between two categorical variables.

Let there exist two such (categorical) variables $X$ and $Y$, where $X$ stands for two groups of individuals: control and treatment, and $Y$ be a two states variable denoting the methylation status, carrying the number of times that a cytosine site is found methylated ($^{m}CG$) and non-methylated ($CG$), respectively.

This information can be summarized in a $2 \times 2$ table, a $2 \times 2$ matrix in which the entries $a_{ij}$ represent the number of observations in which $x=i$ and $y=j$. Calculate the row and column sums $R_i$ and $C_j$, respectively, and the total sum:

$N=\sum_iR_i=\sum_jC_j$

of the matrix:

$Y = ^mCG$ $Y = CG$ $R_i$
Control $a_{11}$ $a_12$ $a_{11}+a_{12}$
Treatment $a_{21}$ $a_22$ $a_{21}+a_{22}$
$C_i$ $a_{11}+a_{21}$ $a_{12}+a_{22}$ $a_{11}+a_{12}+a_{21}+a_{22} = N$

Then the conditional probability of getting the actual matrix, given the particular row and column sums, is given by the formula:

$P_{cutoff}=\frac{R_1!R_2!}{N!\prod_{i,j}a_{ij}!}C_1!C_2!$

# Optimal cutpoint for the methylation signal

## Detection of the methylation signal withMethyl-IT

The discrimination of the methylation signal from the stochastic methylation background resultant from the standard (non-stressful) biological processes is a critical step for the genome-wide methylation analysis. Such a discrimination requires for the knowledge of the probability distribution of the information divergence of methylation levels and a proper evaluation of the classification performance of differentially methylated positions (DMPs) into two classes: DMPs from control and DMPs from treatment.

# Background

The probability of extreme methylation changes occurring spontaneously in a control population by the stochastic fluctuations inherent to biochemical processes and DNA maintenance (1), requires the discrimination of this background variation from a biological treatment signal. The stochasticity of the the methylation process derives from the stochasticity inherent to biochemical processes (23). There are fundamental physical reasons to acknowledge that biochemical processes are subject to noise and fluctuations (45). So, regardless constant environment, statistically significant methylation changes can be found in control population with probability greater than zero and proportional to a Boltzmann factor (6).

Natural signals and those generated by human technology are not free of noise and, as mentioned above, the methylation signal is no exception. Only signal detection based approaches are designed to filter out the signal from the noise, in natural and in human generated signals.

The stochasticity of methylation regulatory machinery effects is presumed to reflect system heterogeneity; cells from the same tissue are not necessarily in the same state, and therefore, corresponding cytosine sites differ in their methylation status. Consequently, overall organismal response is conveyed as a statistical outcome that distinguishes the regulatory methylation signal from statistical background “noise”. Estimation of optimal cutoff value for the signal is an additional step to remove any remaining potential methylation background noise with probability $0 ≤ \alpha ≤ 0.05$. We define as a methylation signal (DMP) each cytosine site with Hellinger Divergence values above the cutpoint (shown in (7)).

As a result, a differentially methylated position (DMP) as a cytosine position with high probability to be altered in methylation due to a treatment effect, distinct from spontaneous variation detected in the control population.

Note: This example was made with the MethylIT version 0.3.2 available at https://github.com/genomaths/MethylIT.

## Data generation

For the current example on methylation analysis with Methyl-IT we will use simulated data. Read-count matrices of methylated and unmethylated cytosine are generated with MethylIT.utils function simulateCounts. A standard analysis of this dataset is given in the web page: Methylation analysis with Methyl-IT

library(MethylIT)
library(MethylIT.utils)

bmean <- function(alpha, beta) alpha/(alpha + beta)
alpha.ct <- 0.09
alpha.tt <- 0.2

# The number of cytosine sites to generate
sites = 50000
# Set a seed for pseudo-random number generation
set.seed(124)
control.nam <- c("C1", "C2", "C3")
treatment.nam <- c("T1", "T2", "T3")

# Reference group
ref0 = simulateCounts(num.samples = 4, sites = sites, alpha = alpha.ct, beta = 0.5,
size = 50, theta = 4.5, sample.ids = c("R1", "R2", "R3", "R4"))
# Control group
ctrl = simulateCounts(num.samples = 3, sites = sites, alpha = alpha.ct, beta = 0.5,
size = 50, theta = 4.5, sample.ids = control.nam)
# Treatment group
treat = simulateCounts(num.samples = 3, sites = sites, alpha = alpha.tt, beta = 0.5,
size = 50, theta = 4.5, sample.ids = treatment.nam)

# Reference sample
ref = poolFromGRlist(ref0, stat = "mean", num.cores = 4L, verbose = FALSE)


## Methylation level divergences

Total variation distance and Hellinger divergence are computed with estimateDivergence function

divs <- estimateDivergence(ref = ref, indiv = c(ctrl, treat), Bayesian = TRUE,
num.cores = 6L, percentile = 1, verbose = FALSE)


To get some statistical description about the sample is useful. Here, empirical critical values for the probability distribution of $H$ and $TV$ can is obtained using quantile function from the R package stats.

critical.val <- do.call(rbind, lapply(divs, function(x) {
x <- x[x$hdiv > 0] hd.95 = quantile(x$hdiv, 0.95)
tv.95 = quantile(abs(x$TV), 0.95) return(c(tv = tv.95, hd = hd.95)) })) critical.val  ## tv.95% hd.95% ## C1 0.6842105 66.76081 ## C2 0.6800000 66.71995 ## C3 0.6777456 65.98495 ## T1 0.9397681 138.68237 ## T2 0.9478351 141.72637 ## T3 0.9466565 141.77241  ## Estimation of potential DMPs with Methyl-IT In Methyl-IT, DMP estimation requires for the knowledge of the probability distribution of the noise (plus signal), which is used as null hypothesis. The best fitted distribution model can be estimated with function gofReport. Here, for illustration purposes, we will use specific estimations based on 2- and 3-parameter gamma distribution models directly using function nonlinearFitDist. ### Potential DMPs estimated with 2-parameter gamma distribution model nlms.g2p <- nonlinearFitDist(divs, column = 9L, verbose = FALSE, num.cores = 6L, dist.name = "Gamma2P") # Potential DMPs from 'Gamma2P' model pDMPs.g2p <- getPotentialDIMP(LR = divs, nlms = nlms.g2p, div.col = 9L, tv.cut = 0.68, tv.col = 7, alpha = 0.05, dist.name = "Gamma2P")  ### Potential DMPs estimated with 3-parameter gamma distribution model nlms.g3p <- nonlinearFitDist(divs, column = 9L, verbose = FALSE, num.cores = 6L, dist.name = "Gamma3P") # Potential DMPs from 'Gamma2P' model pDMPs.g3p <- getPotentialDIMP(LR = divs, nlms = nlms.g3p, div.col = 9L, tv.cut = 0.68, tv.col = 7, alpha = 0.05, dist.name = "Gamma3P")  ## Cutpoint estimation As a result of the natural spontaneous variation, naturally occurring DMPs can be identified in samples from the control and treatment populations. Machine-learning algorithms implemented in Methyl-IT are applied to discriminate treatment-induced DMPs from those naturally generated. The simple cutpoint estimation available in Methyl-IT is based on the application of Youden index (8). Although cutpoints are estimated for a single variable, the classification performance can be evaluated for several variables and applying different model classifiers. In the current example, the column carrying TV (div.col = 7L) will be used to estimate the cutpoint. The column values will be expressed in terms of$TV_d=|p_{tt}-p_{ct}|$. A minimum cutpoint value for TV derived from the minimum 95% quantile (tv.cut = 0.92) found in the treatment group will be applied (see Methylation analysis with Methyl-IT). Next, a logistic model classifier will be fitted with the 60% (prop = 0.6) of the raw data (training set) and then the resting 40% of individual samples will be used to evaluate the model performance. The predictor variable included in the model are specified with function parameter column (for more detail see estimateCutPoint or type ?estimateCutPoint in R console). ### Simple cutpoint estimation for Gamma2P model Here, we use the results of modeling the distribution of the Hellinger divergence (HD) of methylation levels through a 2-parameter gamma probability distribution model. The critical values for$HD_{\alpha = 0.05}^{CT_{G2P}}$used to get potential DMPs were: nams <- names(nlms.g2p) crit <- unlist(lapply(nlms.g2p, function(x) qgamma(0.95, shape = x$Estimate[1],
scale = x$Estimate[2]))) names(crit) <- nams crit  ## C1 C2 C3 T1 T2 T3 ## 58.59180 57.99972 57.81016 112.40001 113.92362 114.48802  As before the cutpoint is estimated based on ‘Youden Index’ (8). A PCA+LDA model classifier (classifier = "pca.lda") is applied. That is, a principal component analysis (PCA) is applied on the original raw matrix of data and the four possible component (n.pc = 4) derived from the analysis are used in a further linear discriminant analysis (LDA). A scaling step is applied to the raw matrix of data before the application of the mentioned procedure (center = TRUE, scale = TRUE). Here, PCA will yield new orthogonal (non-correlated) variables, the principal components, which prevent any potential bias effect originated by any correlation or association of the original variables. By using function estimateCutPoint, we can estimate the cutpoint, based on HD (div.col = 9L) or on$TV_d$(div.col = 7L): # Cutpoint estimation for the FT approach using the ECDF critical value cut.g2p = estimateCutPoint(LR = pDMPs.g2p, simple = TRUE, column = c(hdiv = TRUE, bay.TV = TRUE, wprob = TRUE, pos = TRUE), classifier1 = "pca.lda", n.pc = 4, control.names = control.nam, treatment.names = treatment.nam, center = TRUE, scale = TRUE, clas.perf = TRUE, prop = 0.6, div.col = 9L) cut.g2p  ## Cutpoint estimation with 'Youden Index' ## Simple cutpoint estimation ## Cutpoint = 114.22 ## ## Cytosine sites from treatment have divergence values >= 114.22 ## ## The accessible objects in the output list are: ## Length Class Mode ## cutpoint 1 -none- numeric ## testSetPerformance 6 confusionMatrix list ## testSetModel.FDR 1 -none- numeric ## model 2 pcaLDA list ## modelConfMatrix 6 confusionMatrix list ## initModel 1 -none- character ## postProbCut 1 -none- logical ## postCut 1 -none- logical ## classifier 1 -none- character ## statistic 1 -none- logical ## optStatVal 1 -none- logical ## cutpData 1 -none- logical ## initModelConfMatrix 6 confusionMatrix list  As indicated above, the model classifier performance and its corresponding false discovery rate can be retrieved as: cut.g2p$testSetPerformance

## Confusion Matrix and Statistics
##
##           Reference
## Prediction   CT   TT
##         CT  319    0
##         TT    0 3544
##
##                Accuracy : 1
##                  95% CI : (0.999, 1)
##     No Information Rate : 0.9174
##     P-Value [Acc > NIR] : < 2.2e-16
##
##                   Kappa : 1
##
##  Mcnemar's Test P-Value : NA
##
##             Sensitivity : 1.0000
##             Specificity : 1.0000
##          Pos Pred Value : 1.0000
##          Neg Pred Value : 1.0000
##              Prevalence : 0.9174
##          Detection Rate : 0.9174
##    Detection Prevalence : 0.9174
##       Balanced Accuracy : 1.0000
##
##        'Positive' Class : TT
##

cut.g2p$testSetModel.FDR  ## [1] 0  Here, DMP classification is modeled with PCA+QDA classifier (classifier = "pca.lda"). That is, principal component analysis (PCA) is applied on the original raw matrix of data and the four possible component (n.pc = 4) are used in a further linear discriminant analysis (LDA). A scaling step is applied to the raw matrix of data before the application of the mentioned procedure (center = TRUE, scale = TRUE). Next, a different model classifier can be applied to model the classification derived from the previous cutpoint estimation. ### Simple cutpoint estimation for Gamma3P model The same analyses for the cutpoint estimation can be performed for 3P gamma model # Cutpoint estimation for the FT approach using the ECDF critical value cut.g3p = estimateCutPoint(LR = pDMPs.g3p, simple = TRUE, column = c(hdiv = TRUE, bay.TV = TRUE, wprob = TRUE, pos = TRUE), classifier1 = "pca.lda", n.pc = 4, control.names = control.nam, treatment.names = treatment.nam, center = TRUE, scale = TRUE, clas.perf = TRUE, prop = 0.6, div.col = 9L) cut.g3p  ## Cutpoint estimation with 'Youden Index' ## Simple cutpoint estimation ## Cutpoint = 115.24 ## ## Cytosine sites from treatment have divergence values >= 115.24 ## ## The accessible objects in the output list are: ## Length Class Mode ## cutpoint 1 -none- numeric ## testSetPerformance 6 confusionMatrix list ## testSetModel.FDR 1 -none- numeric ## model 2 pcaLDA list ## modelConfMatrix 6 confusionMatrix list ## initModel 1 -none- character ## postProbCut 1 -none- logical ## postCut 1 -none- logical ## classifier 1 -none- character ## statistic 1 -none- logical ## optStatVal 1 -none- logical ## cutpData 1 -none- logical ## initModelConfMatrix 6 confusionMatrix list  As indicated above, the model classifier performance and its corresponding false discovery rate can be retrieved as: cut.g3p$testSetPerformance

## Confusion Matrix and Statistics
##
##           Reference
## Prediction   CT   TT
##         CT  309    0
##         TT    0 3483
##
##                Accuracy : 1
##                  95% CI : (0.999, 1)
##     No Information Rate : 0.9185
##     P-Value [Acc > NIR] : < 2.2e-16
##
##                   Kappa : 1
##
##  Mcnemar's Test P-Value : NA
##
##             Sensitivity : 1.0000
##             Specificity : 1.0000
##          Pos Pred Value : 1.0000
##          Neg Pred Value : 1.0000
##              Prevalence : 0.9185
##          Detection Rate : 0.9185
##    Detection Prevalence : 0.9185
##       Balanced Accuracy : 1.0000
##
##        'Positive' Class : TT
##

cut.g3p$testSetModel.FDR  ## [1] 0  ### DMP prediction based on classification models The model obtained in the previous step can be used for further prediction with function predict from MethylIT.utils package. For example, we would take a random sample and run: set.seed(1) randsampl <- unlist(pDMPs.g3p) randsampl <- randsampl[sample.int(length(randsampl), 10)] pred <- predict(cut.g3p$model, newdata = randsampl)
pred

## $class ## [1] CT TT TT TT TT TT TT TT TT TT ## Levels: CT TT ## ##$posterior
##                 CT          TT
##  [1,] 1.000000e+00 1.19242e-08
##  [2,] 5.231187e-46 1.00000e+00
##  [3,] 2.136182e-45 1.00000e+00
##  [4,] 6.739051e-47 1.00000e+00
##  [5,] 2.015394e-46 1.00000e+00
##  [6,] 2.379968e-46 1.00000e+00
##  [7,] 3.473689e-46 1.00000e+00
##  [8,] 1.760048e-46 1.00000e+00
##  [9,] 7.640639e-47 1.00000e+00
## [10,] 3.254017e-47 1.00000e+00
##
## $x ## LD1 ## [1,] -7.465499 ## [2,] 1.041471 ## [3,] 0.943772 ## [4,] 1.183774 ## [5,] 1.107704 ## [6,] 1.096158 ## [7,] 1.069901 ## [8,] 1.117111 ## [9,] 1.175055 ## [10,] 1.234328  The variable pred$posterior provides the posterior classification probabilities that a DMP could belong to control (CT) or to treatment (TT) group. The variable ‘x‘ provides the cytosine methylation changes in terms of its values in the linear discriminant function LD1. Notice that, for each row, the sum of posterior probabilities is equal 1. By default, individuals with TT posterior probabilities greater than 0.5 are predicted to belong to the treatment class. For example:

classfiction = rep("CT", 10)
classfiction[pred$posterior[, 2] > 0.5] <- "TT" classfiction  ## [1] "CT" "TT" "TT" "TT" "TT" "TT" "TT" "TT" "TT" "TT"  We can be more strict increasing the posterior classification probability cutoff classfiction = rep("CT", 10) classfiction[pred$posterior[, 2] > 0.7] <- "TT"
classfiction

##  [1] "CT" "TT" "TT" "TT" "TT" "TT" "TT" "TT" "TT" "TT"


The posterior classification probability cutoff can be controlled with parameter post.cut from estimateCutPoint function (default: $post.cut=0.5$).

## Machine-learning (ML) based approach to search for an optimal cutpoint

In the next example the cutpoint estimation for the Hellinger divergence of methylation levels (div.col = 9L) is accomplished. Function estimateCutPoint can be used to search for a cutpoint as well. Two model classifiers can be used. classifiers1 will be used to estimate the posterior classification probabilities of DMP into those from control and those from treatment. These probabilities are then used to estimate the cutpoint in the range of values from, say, 0.5 to 0.8. Next, a classifier2 will be used to evaluate the classification performance. In this case, the search for an optimal cutpoint is accomplished maximizing the accuracy (stat = 0) of classifier2.

### ML cutpoint estimation for potential DMPs based on Gamma2P model

The ML search for an optimal cutpoint is accomplished in the set of potential DMPs, which were identified using a Gamma2P probability distribution model as null hypothesis.

cut.g2p = estimateCutPoint(LR = pDMPs.g2p, simple = FALSE,
column = c(hdiv = TRUE, bay.TV = TRUE,
wprob = TRUE, pos = TRUE),
classifier1 = "pca.lda",
classifier2 = "pca.qda", stat = 0,
control.names = control.nam,
treatment.names = treatment.nam,
cut.values = seq(45, 114, 1), post.cut = 0.5,
clas.perf = TRUE, prop = 0.6,
center = TRUE, scale = TRUE,
n.pc = 4, div.col = 9L)
cut.g2p

## Cutpoint estimation with 'pca.lda' classifier
## Cutpoint search performed using model posterior probabilities
##
## Posterior probability used to get the cutpoint = 0.5
## Cytosine sites with treatment PostProbCut >= 0.5 have a
## divergence value >= 112.4247
##
## Optimized statistic: Accuracy = 1
## Cutpoint = 112.42
##
## Model classifier 'pca.qda'
##
## The accessible objects in the output list are:
##                    Length Class           Mode
## cutpoint           1      -none-          numeric
## testSetPerformance 6      confusionMatrix list
## testSetModel.FDR   1      -none-          numeric
## model              2      pcaQDA          list
## modelConfMatrix    6      confusionMatrix list
## initModel          1      -none-          character
## postProbCut        1      -none-          numeric
## postCut            1      -none-          numeric
## classifier         1      -none-          character
## statistic          1      -none-          character
## optStatVal         1      -none-          numeric
## cutpData           1      -none-          logical


Model performance in the test dataset is:

cut.g2p$testSetPerformance  ## Confusion Matrix and Statistics ## ## Reference ## Prediction CT TT ## CT 1274 0 ## TT 0 3580 ## ## Accuracy : 1 ## 95% CI : (0.9992, 1) ## No Information Rate : 0.7375 ## P-Value [Acc > NIR] : < 2.2e-16 ## ## Kappa : 1 ## ## Mcnemar's Test P-Value : NA ## ## Sensitivity : 1.0000 ## Specificity : 1.0000 ## Pos Pred Value : 1.0000 ## Neg Pred Value : 1.0000 ## Prevalence : 0.7375 ## Detection Rate : 0.7375 ## Detection Prevalence : 0.7375 ## Balanced Accuracy : 1.0000 ## ## 'Positive' Class : TT ##  Model performance in in the whole dataset is: cut.g2p$modelConfMatrix

## Confusion Matrix and Statistics
##
##           Reference
## Prediction   CT   TT
##         CT 3184    0
##         TT    0 8948
##
##                Accuracy : 1
##                  95% CI : (0.9997, 1)
##     No Information Rate : 0.7376
##     P-Value [Acc > NIR] : < 2.2e-16
##
##                   Kappa : 1
##
##  Mcnemar's Test P-Value : NA
##
##             Sensitivity : 1.0000
##             Specificity : 1.0000
##          Pos Pred Value : 1.0000
##          Neg Pred Value : 1.0000
##              Prevalence : 0.7376
##          Detection Rate : 0.7376
##    Detection Prevalence : 0.7376
##       Balanced Accuracy : 1.0000
##
##        'Positive' Class : TT
##


The False discovery rate is:

cut.g2p$testSetModel.FDR  ## [1] 0  The model classifier PCA+LDA has enough discriminatory power to discriminate control DMP from those induced by the treatment for HD values$112.4247 \le HD$. The probabilities$P(HD \le 122.43)$to observe a cytosine site with$HD \le 112.4247$on each individual is: nams <- names(nlms.g2p) crit <- unlist(lapply(nlms.g2p, function(x) pgamma(cut.g2p$cutpoint, shape = x$Estimate[1], scale = x$Estimate[2])))
names(crit) <- nams
crit

##        C1        C2        C3        T1        T2        T3
## 0.9964704 0.9966560 0.9967314 0.9500279 0.9483024 0.9476610


In other words, most of the methylation changes are not (and should not be) considered DMPs. Notice that although the same HD value could be found in the same differentially methylated cytosine site in control and treatment, if the probabilities distributions of the information divergences (null hypotheses) from control and treatment are different, then these DMPs can be distinguished.

### ML cutpoint estimation for potential DMPs based on Gamma3P model

Likewise, for the 3-parameter gamma model we can search for an optimal cutpoint:

cut.g3p = estimateCutPoint(LR = pDMPs.g3p, simple = FALSE,
column = c(hdiv = TRUE, TV = TRUE,
wprob = TRUE, pos = TRUE),
classifier1 = "pca.lda",
classifier2 = "pca.qda", stat = 0,
control.names = control.nam,
treatment.names = treatment.nam,
cut.values = seq(45, 114, 1), post.cut = 0.5,
clas.perf = TRUE, prop = 0.6,
center = TRUE, scale = TRUE,
n.pc = 4, div.col = 9L)
cut.g3p

## Cutpoint estimation with 'pca.lda' classifier
## Cutpoint search performed using model posterior probabilities
##
## Posterior probability used to get the cutpoint = 0.5
## Cytosine sites with treatment PostProbCut >= 0.5 have a
## divergence value >= 113.497
##
## Optimized statistic: Accuracy = 1
## Cutpoint = 113.5
##
## Model classifier 'pca.qda'
##
## The accessible objects in the output list are:
##                    Length Class           Mode
## cutpoint           1      -none-          numeric
## testSetPerformance 6      confusionMatrix list
## testSetModel.FDR   1      -none-          numeric
## model              2      pcaQDA          list
## modelConfMatrix    6      confusionMatrix list
## initModel          1      -none-          character
## postProbCut        1      -none-          numeric
## postCut            1      -none-          numeric
## classifier         1      -none-          character
## statistic          1      -none-          character
## optStatVal         1      -none-          numeric
## cutpData           1      -none-          logical


DMPs are identified with function selectDIMP

g2p.dmps <- selectDIMP(pDMPs.g2p, div.col = 9L, cutpoint = cut.g2p$cutpoint) g3p.dmps <- selectDIMP(pDMPs.g3p, div.col = 9L, cutpoint = cut.g3p$cutpoint)


## DMPs estimated with Fisher’s exact Test (FT)

For comparison purposes DMPs are estimated with Fisher’s exact test as well.

ft. = FisherTest(LR = divs, tv.cut = 0.68, pAdjustMethod = "BH",
pvalCutOff = 0.05, num.cores = 4L,
verbose = FALSE, saveAll = FALSE)

ft.dmps <- getPotentialDIMP(LR = ft., div.col = 9L, dist.name = "None",
tv.cut = 0.68, tv.col = 7, alpha = 0.05)


## Classification performance of DMP classification

After the cutpoint application to select DMPs, a Monte Carlo (bootstrap) analysis reporting several classifier performance indicators can be accomplished by using function evaluateDIMPclass and its settings output = "mc.val" and num.boot.

### Classification performance for DMPs based on Gamma2P model

g2p.class = evaluateDIMPclass(LR = g2p.dmps, control.names = control.nam,
treatment.names = treatment.nam,
column = c(hdiv = TRUE, TV = TRUE,
wprob = TRUE, pos = TRUE),
classifier = "pca.qda", n.pc = 4,
center = TRUE, scale = TRUE, num.boot = 300,
output = "all", prop = 0.6
)
g2p.class$mc.val  ## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull ## Min. :1 Min. :1 Min. :0.9991 Min. :1 Min. :0.9139 ## 1st Qu.:1 1st Qu.:1 1st Qu.:0.9991 1st Qu.:1 1st Qu.:0.9139 ## Median :1 Median :1 Median :0.9991 Median :1 Median :0.9139 ## Mean :1 Mean :1 Mean :0.9991 Mean :1 Mean :0.9139 ## 3rd Qu.:1 3rd Qu.:1 3rd Qu.:0.9991 3rd Qu.:1 3rd Qu.:0.9139 ## Max. :1 Max. :1 Max. :0.9991 Max. :1 Max. :0.9139 ## ## AccuracyPValue McnemarPValue Sensitivity Specificity Pos Pred Value ## Min. :9.096e-154 Min. : NA Min. :1 Min. :1 Min. :1 ## 1st Qu.:9.096e-154 1st Qu.: NA 1st Qu.:1 1st Qu.:1 1st Qu.:1 ## Median :9.096e-154 Median : NA Median :1 Median :1 Median :1 ## Mean :9.096e-154 Mean :NaN Mean :1 Mean :1 Mean :1 ## 3rd Qu.:9.096e-154 3rd Qu.: NA 3rd Qu.:1 3rd Qu.:1 3rd Qu.:1 ## Max. :9.096e-154 Max. : NA Max. :1 Max. :1 Max. :1 ## NA's :300 ## Neg Pred Value Precision Recall F1 Prevalence ## Min. :1 Min. :1 Min. :1 Min. :1 Min. :0.9139 ## 1st Qu.:1 1st Qu.:1 1st Qu.:1 1st Qu.:1 1st Qu.:0.9139 ## Median :1 Median :1 Median :1 Median :1 Median :0.9139 ## Mean :1 Mean :1 Mean :1 Mean :1 Mean :0.9139 ## 3rd Qu.:1 3rd Qu.:1 3rd Qu.:1 3rd Qu.:1 3rd Qu.:0.9139 ## Max. :1 Max. :1 Max. :1 Max. :1 Max. :0.9139 ## ## Detection Rate Detection Prevalence Balanced Accuracy ## Min. :0.9139 Min. :0.9139 Min. :1 ## 1st Qu.:0.9139 1st Qu.:0.9139 1st Qu.:1 ## Median :0.9139 Median :0.9139 Median :1 ## Mean :0.9139 Mean :0.9139 Mean :1 ## 3rd Qu.:0.9139 3rd Qu.:0.9139 3rd Qu.:1 ## Max. :0.9139 Max. :0.9139 Max. :1 ##  ### Classification performance for DMPs based on Gamma3P model Likewise the evaluation of the DMP classification performance can be accomplished for DMPs estimated based on the$’Gamma3P’$model: g3p.class = evaluateDIMPclass(LR = g3p.dmps, control.names = control.nam, treatment.names = treatment.nam, column = c(hdiv = TRUE, TV = TRUE, wprob = TRUE, pos = TRUE), classifier = "pca.qda", n.pc = 4, center = TRUE, scale = TRUE, num.boot = 300, output = "all", prop = 0.6 ) g3p.class$mc.val

##     Accuracy     Kappa   AccuracyLower   AccuracyUpper  AccuracyNull
##  Min.   :1   Min.   :1   Min.   :0.999   Min.   :1     Min.   :0.914
##  1st Qu.:1   1st Qu.:1   1st Qu.:0.999   1st Qu.:1     1st Qu.:0.914
##  Median :1   Median :1   Median :0.999   Median :1     Median :0.914
##  Mean   :1   Mean   :1   Mean   :0.999   Mean   :1     Mean   :0.914
##  3rd Qu.:1   3rd Qu.:1   3rd Qu.:0.999   3rd Qu.:1     3rd Qu.:0.914
##  Max.   :1   Max.   :1   Max.   :0.999   Max.   :1     Max.   :0.914
##
##  AccuracyPValue       McnemarPValue  Sensitivity  Specificity Pos Pred Value
##  Min.   :1.393e-150   Min.   : NA   Min.   :1    Min.   :1    Min.   :1
##  1st Qu.:1.393e-150   1st Qu.: NA   1st Qu.:1    1st Qu.:1    1st Qu.:1
##  Median :1.393e-150   Median : NA   Median :1    Median :1    Median :1
##  Mean   :1.393e-150   Mean   :NaN   Mean   :1    Mean   :1    Mean   :1
##  3rd Qu.:1.393e-150   3rd Qu.: NA   3rd Qu.:1    3rd Qu.:1    3rd Qu.:1
##  Max.   :1.393e-150   Max.   : NA   Max.   :1    Max.   :1    Max.   :1
##                       NA's   :300
##  Neg Pred Value   Precision     Recall        F1      Prevalence
##  Min.   :1      Min.   :1   Min.   :1   Min.   :1   Min.   :0.914
##  1st Qu.:1      1st Qu.:1   1st Qu.:1   1st Qu.:1   1st Qu.:0.914
##  Median :1      Median :1   Median :1   Median :1   Median :0.914
##  Mean   :1      Mean   :1   Mean   :1   Mean   :1   Mean   :0.914
##  3rd Qu.:1      3rd Qu.:1   3rd Qu.:1   3rd Qu.:1   3rd Qu.:0.914
##  Max.   :1      Max.   :1   Max.   :1   Max.   :1   Max.   :0.914
##
##  Detection Rate  Detection Prevalence Balanced Accuracy
##  Min.   :0.914   Min.   :0.914        Min.   :1
##  1st Qu.:0.914   1st Qu.:0.914        1st Qu.:1
##  Median :0.914   Median :0.914        Median :1
##  Mean   :0.914   Mean   :0.914        Mean   :1
##  3rd Qu.:0.914   3rd Qu.:0.914        3rd Qu.:1
##  Max.   :0.914   Max.   :0.914        Max.   :1
##


We do not have evidence to support statistical differences between the classification performances estimated for ‘Gamma2P’ and ‘Gamma3P’ probability distribution models. Hence, in this case we select the model that yield the lowest cutpoint

### Classification performance for DMPs based on Fisher’s exact test

Classification performance results obtained with Monte Carlos sampling for the $Gamma2P$ and $Gamma3P$ models are quite different from those obtained with FT:

ft.class = evaluateDIMPclass(LR = ft.dmps, control.names = control.nam,
treatment.names = treatment.nam,
column = c(hdiv = TRUE, TV = TRUE,
wprob = TRUE, pos = TRUE),
classifier = "pca.lda", n.pc = 4,
center = TRUE, scale = TRUE, num.boot = 300,
output = "all", prop = 0.6
)
ft.class$mc.val  ## Accuracy Kappa AccuracyLower AccuracyUpper ## Min. :0.8076 Min. :-0.007635 Min. :0.8001 Min. :0.8148 ## 1st Qu.:0.8105 1st Qu.: 0.005355 1st Qu.:0.8031 1st Qu.:0.8177 ## Median :0.8110 Median : 0.007678 Median :0.8036 Median :0.8182 ## Mean :0.8110 Mean : 0.007553 Mean :0.8036 Mean :0.8182 ## 3rd Qu.:0.8115 3rd Qu.: 0.009748 3rd Qu.:0.8041 3rd Qu.:0.8187 ## Max. :0.8137 Max. : 0.020669 Max. :0.8064 Max. :0.8209 ## AccuracyNull AccuracyPValue McnemarPValue Sensitivity ## Min. :0.8188 Min. :0.9205 Min. :0 Min. :0.9835 ## 1st Qu.:0.8188 1st Qu.:0.9781 1st Qu.:0 1st Qu.:0.9857 ## Median :0.8188 Median :0.9847 Median :0 Median :0.9863 ## Mean :0.8188 Mean :0.9827 Mean :0 Mean :0.9863 ## 3rd Qu.:0.8188 3rd Qu.:0.9888 3rd Qu.:0 3rd Qu.:0.9868 ## Max. :0.8188 Max. :0.9990 Max. :0 Max. :0.9893 ## Specificity Pos Pred Value Neg Pred Value Precision ## Min. :0.01134 Min. :0.8181 Min. :0.1337 Min. :0.8181 ## 1st Qu.:0.01726 1st Qu.:0.8193 1st Qu.:0.2150 1st Qu.:0.8193 ## Median :0.01874 Median :0.8196 Median :0.2318 Median :0.8196 ## Mean :0.01858 Mean :0.8196 Mean :0.2304 Mean :0.8196 ## 3rd Qu.:0.02022 3rd Qu.:0.8198 3rd Qu.:0.2455 3rd Qu.:0.8198 ## Max. :0.02663 Max. :0.8208 Max. :0.3187 Max. :0.8208 ## Recall F1 Prevalence Detection Rate ## Min. :0.9835 Min. :0.8933 Min. :0.8188 Min. :0.8053 ## 1st Qu.:0.9857 1st Qu.:0.8950 1st Qu.:0.8188 1st Qu.:0.8071 ## Median :0.9863 Median :0.8952 Median :0.8188 Median :0.8076 ## Mean :0.9863 Mean :0.8952 Mean :0.8188 Mean :0.8076 ## 3rd Qu.:0.9868 3rd Qu.:0.8955 3rd Qu.:0.8188 3rd Qu.:0.8080 ## Max. :0.9893 Max. :0.8969 Max. :0.8188 Max. :0.8101 ## Detection Prevalence Balanced Accuracy ## Min. :0.9825 Min. :0.4975 ## 1st Qu.:0.9848 1st Qu.:0.5017 ## Median :0.9853 Median :0.5025 ## Mean :0.9854 Mean :0.5024 ## 3rd Qu.:0.9860 3rd Qu.:0.5031 ## Max. :0.9879 Max. :0.5066  A quite different story is found when information on the probability distribution of noise (null hypothesis) is added to the classifier: ft.g2p.dmps <- getPotentialDIMP(LR = ft., nlms = nlms.g2p, div.col = 9L, tv.cut = 0.68, tv.col = 7, alpha = 0.05, dist.name = "Gamma2P") ft.g2p.class = evaluateDIMPclass(LR = ft.g2p.dmps, control.names = control.nam, treatment.names = treatment.nam, column = c(hdiv = TRUE, TV = TRUE, wprob = TRUE, pos = TRUE), classifier = "pca.lda", n.pc = 4, center = TRUE, scale = TRUE, num.boot = 300, output = "all", prop = 0.6 ) ft.g2p.class$mc.val

##     Accuracy     Kappa   AccuracyLower    AccuracyUpper  AccuracyNull
##  Min.   :1   Min.   :1   Min.   :0.9992   Min.   :1     Min.   :0.7375
##  1st Qu.:1   1st Qu.:1   1st Qu.:0.9992   1st Qu.:1     1st Qu.:0.7375
##  Median :1   Median :1   Median :0.9992   Median :1     Median :0.7375
##  Mean   :1   Mean   :1   Mean   :0.9992   Mean   :1     Mean   :0.7375
##  3rd Qu.:1   3rd Qu.:1   3rd Qu.:0.9992   3rd Qu.:1     3rd Qu.:0.7375
##  Max.   :1   Max.   :1   Max.   :0.9992   Max.   :1     Max.   :0.7375
##
##  AccuracyPValue McnemarPValue  Sensitivity  Specificity Pos Pred Value
##  Min.   :0      Min.   : NA   Min.   :1    Min.   :1    Min.   :1
##  1st Qu.:0      1st Qu.: NA   1st Qu.:1    1st Qu.:1    1st Qu.:1
##  Median :0      Median : NA   Median :1    Median :1    Median :1
##  Mean   :0      Mean   :NaN   Mean   :1    Mean   :1    Mean   :1
##  3rd Qu.:0      3rd Qu.: NA   3rd Qu.:1    3rd Qu.:1    3rd Qu.:1
##  Max.   :0      Max.   : NA   Max.   :1    Max.   :1    Max.   :1
##                 NA's   :300
##  Neg Pred Value   Precision     Recall        F1      Prevalence
##  Min.   :1      Min.   :1   Min.   :1   Min.   :1   Min.   :0.7375
##  1st Qu.:1      1st Qu.:1   1st Qu.:1   1st Qu.:1   1st Qu.:0.7375
##  Median :1      Median :1   Median :1   Median :1   Median :0.7375
##  Mean   :1      Mean   :1   Mean   :1   Mean   :1   Mean   :0.7375
##  3rd Qu.:1      3rd Qu.:1   3rd Qu.:1   3rd Qu.:1   3rd Qu.:0.7375
##  Max.   :1      Max.   :1   Max.   :1   Max.   :1   Max.   :0.7375
##
##  Detection Rate   Detection Prevalence Balanced Accuracy
##  Min.   :0.7375   Min.   :0.7375       Min.   :1
##  1st Qu.:0.7375   1st Qu.:0.7375       1st Qu.:1
##  Median :0.7375   Median :0.7375       Median :1
##  Mean   :0.7375   Mean   :0.7375       Mean   :1
##  3rd Qu.:0.7375   3rd Qu.:0.7375       3rd Qu.:1
##  Max.   :0.7375   Max.   :0.7375       Max.   :1
##


Now, we add additional information about the optimal cutpoint

ft.g2p_cutp.dmps <- selectDIMP(ft.g2p.dmps, div.col = 9L, cutpoint = cut.g2p$cutpoint) ft.g2p_cut.class = evaluateDIMPclass(LR = ft.g2p_cutp.dmps, control.names = control.nam, treatment.names = treatment.nam, column = c(hdiv = TRUE, TV = TRUE, wprob = TRUE, pos = TRUE), classifier = "pca.lda", n.pc = 4, center = TRUE, scale = TRUE, num.boot = 300, output = "all", prop = 0.6 ) ft.g2p_cut.class$mc.val

##     Accuracy     Kappa   AccuracyLower    AccuracyUpper  AccuracyNull
##  Min.   :1   Min.   :1   Min.   :0.9991   Min.   :1     Min.   :0.9139
##  1st Qu.:1   1st Qu.:1   1st Qu.:0.9991   1st Qu.:1     1st Qu.:0.9139
##  Median :1   Median :1   Median :0.9991   Median :1     Median :0.9139
##  Mean   :1   Mean   :1   Mean   :0.9991   Mean   :1     Mean   :0.9139
##  3rd Qu.:1   3rd Qu.:1   3rd Qu.:0.9991   3rd Qu.:1     3rd Qu.:0.9139
##  Max.   :1   Max.   :1   Max.   :0.9991   Max.   :1     Max.   :0.9139
##
##  AccuracyPValue       McnemarPValue  Sensitivity  Specificity Pos Pred Value
##  Min.   :9.096e-154   Min.   : NA   Min.   :1    Min.   :1    Min.   :1
##  1st Qu.:9.096e-154   1st Qu.: NA   1st Qu.:1    1st Qu.:1    1st Qu.:1
##  Median :9.096e-154   Median : NA   Median :1    Median :1    Median :1
##  Mean   :9.096e-154   Mean   :NaN   Mean   :1    Mean   :1    Mean   :1
##  3rd Qu.:9.096e-154   3rd Qu.: NA   3rd Qu.:1    3rd Qu.:1    3rd Qu.:1
##  Max.   :9.096e-154   Max.   : NA   Max.   :1    Max.   :1    Max.   :1
##                       NA's   :300
##  Neg Pred Value   Precision     Recall        F1      Prevalence
##  Min.   :1      Min.   :1   Min.   :1   Min.   :1   Min.   :0.9139
##  1st Qu.:1      1st Qu.:1   1st Qu.:1   1st Qu.:1   1st Qu.:0.9139
##  Median :1      Median :1   Median :1   Median :1   Median :0.9139
##  Mean   :1      Mean   :1   Mean   :1   Mean   :1   Mean   :0.9139
##  3rd Qu.:1      3rd Qu.:1   3rd Qu.:1   3rd Qu.:1   3rd Qu.:0.9139
##  Max.   :1      Max.   :1   Max.   :1   Max.   :1   Max.   :0.9139
##
##  Detection Rate   Detection Prevalence Balanced Accuracy
##  Min.   :0.9139   Min.   :0.9139       Min.   :1
##  1st Qu.:0.9139   1st Qu.:0.9139       1st Qu.:1
##  Median :0.9139   Median :0.9139       Median :1
##  Mean   :0.9139   Mean   :0.9139       Mean   :1
##  3rd Qu.:0.9139   3rd Qu.:0.9139       3rd Qu.:1
##  Max.   :0.9139   Max.   :0.9139       Max.   :1
##


In other words, information on the probability distributions of the natural spontaneous methylation variation in the control and treatment population are essential to discriminate the background noise from the treatment induced signal.

## Graphics of DMP classification performance

DMP count data:

dt <- t(rbind(G2P = sapply(g2p.dmps, length),
G3P = sapply(g3p.dmps, length),
FT = sapply(ft.dmps, length),
FT.G2P = sapply(ft.g2p.dmps, length),
FT.SD = sapply(ft.g2p_cutp.dmps, length)
))
dt

##     G2P  G3P   FT FT.G2P FT.SD
## C1  255  251 1730   1055   255
## C2  306  298 1682   1070   306
## C3  281  276 1657   1059   281
## T1 2925 2871 7589   2926  2925
## T2 3032 2965 7646   3032  3032
## T3 2990 2934 7679   2990  2990


The comparison between the approaches FT.G2P and FT.SD (full signal detection on FT output) tells us that only 255, 306, and 281 cytosine sites detected with FT in the control samples C1, C2, and C3, respectively, carry methylation signals comparable (in magnitude) to those signals induced by the treatment.

Classification performance data:

df <- data.frame(method = c("FT", "FT.G2P", "FT.SD", "G2P", "G3P"),
rbind(
c(colMeans(ft.class$boots)[c(1, 8:11, 18)], FDR = ft.class$con.mat$FDR), c(colMeans(ft.g2p.class$boots)[c(1, 8:11, 18)], FDR = ft.g2p.class$con.mat$FDR),
c(colMeans(ft.g2p_cut.class$boots)[c(1, 8:11, 18)], FDR = ft.g2p_cut.class$con.mat$FDR), c(colMeans(g2p.class$boots)[c(1, 8:11, 18)], FDR = g2p.class$con.mat$FDR),
c(colMeans(g3p.class$boots)[c(1, 8:11, 18)], FDR = g3p.class$con.mat$FDR) ))  Graphics: color <- c("darkgreen", "#147714FF", "#3D9F3DFF", "#66C666FF", "#90EE90FF") dt <- data.frame(dt, sample = names(g2p.dmps)) ## ------------------------- DMP count graphic --------------------------------- par(family = "serif", lwd = 0.1, cex = 1, mar = c(2,5,2,2), mfcol = c(1, 2)) barplot(cbind(FT, FT.G2P, FT.SD, G2P, G3P) ~ sample, panel.first={points(0, 0, pch=16, cex=1e6, col="grey95") grid(col="white", lty = 1, lwd = 1)}, data = dt, beside = TRUE, legend.text = TRUE, las = 1, lwd = 0.05, yaxt = "n", cex.names = 1.4, font = 3, xlab = "", col = color, args.legend = list(x=10, y=6000, text.font = 2, box.lwd = 0, horiz = FALSE, adj = 0, xjust = 0.65, yjust = 0.8, bty = "n", cex = 1.2, x.intersp = 0.2, inset = -1, ncol = 1, fill = color)) axis(2, hadj = 0.9, las = 2, lwd = 0.4, tck = -0.02, cex.axis = 1.2, font = 2, line = -0.2) mtext(side = 2, text = "DMP count", line = 3, cex = 1.4, font = 3) ## ------------------ DMP classifiction performance graphic ------------------- color <- c("mediumblue", "#0000FFFF", "#3949F6FF", "#566CF2FF", "#7390EEFF", "#90B3EAFF", "#ADD8E6FF") labs <- df$method

par(family = "serif", lwd = 0.1, cex = 1, mar = c(4,2,2,10))
x <- barplot(cbind(Accuracy, Sensitivity, Specificity, Pos.Pred.Value, Neg.Pred.Value,
Balanced.Accuracy, FDR) ~ method,
panel.first={points(0, 0, pch=16, cex=1e6, col="grey95")
grid(col="white", lty = 1, lwd = 1)},
data = df, beside = TRUE,  legend.text = TRUE, las = 1, lwd = 0.1, yaxt = "n",
cex.names = 1.4, font = 3, xlab = "", col = color, ylim = c(0,1),
args.legend = list(x = 52, y = 1., text.font = 2, box.lwd = 0, horiz = FALSE,
adj = 0, xjust = 0.65, yjust = 0.8, bty = "n", cex = 1.2,
x.intersp = 0.2, inset = -1, ncol = 1, fill = color))
axis(2, hadj = 0.8, las = 2, lwd = 0.4, tck = -0.02, cex.axis = 1.2, font = 2,
line = -0.4)
mtext(side = 2, text = "Performance value", line = 2, cex = 1.4, font = 3)


FT.G2P and FT.SD approaches lead to excellent classification performances on this data set. At this point, we can appeal the parsimony principle, follows from Occam’s razor that states “among competing hypotheses, the hypothesis with the fewest assumptions should be selected. In other words, results indicates that the signal-detection and machine-learning approach is sufficient [@ElNaqa2018; @Sanchez2019].

# Conclusions

1. A proper discrimination of the methylation signal from the stochastic methylation background requires for the knowledge of probability distributions of the methylation signal from control and treatment population. Such a knowledge permits a suitable estimation of the cutoff value to discriminate the methylation signal induced by the treatment from the stochastic methylation background detected in the control group.

2. It does not matter how significant a differentially methylation event for a given cytosine position would be (after the application of some statistical test), but on how big the probability to be observed in the control group is. In simple words, if for a given DMP the probability of to be observed in the control is big enough, then such a DMP did not result from a treatment effect.

3. A suitable evaluation on how much the mentioned probability can be big enough derives by estimating an optimal cutpoint. But a classification into two groups results from the cutpoint estimation and the problem on the estimation of such a cutpoint is equivalent to find a discriminatory function (as set by Fisher, [@Fisher1938; @Green1979]). Cases with function values below some cutoff are classified in one group, while values above the cutoff are put in the other group.

4. MethylIT function estimateCutPoint permits the estimation and search for an optimal cutpoint by confronting the problem as in the spirit of the classical signal detection and as a classification problem. The best model classifier will depend on the dataset under study.So, uses must check for which is the model classifier with the best classification performance for his/her dataset.

# References

1. Ngo, Thuy T.M., Jejoong Yoo, Qing Dai, Qiucen Zhang, Chuan He, Aleksei Aksimentiev, and Taekjip Ha. 2016. “Effects of cytosine modifications on DNA flexibility and nucleosome mechanical stability.” Nature Communications 7 (February). Nature Publishing Group: 10813. https://doi.org/10.1038/ncomms10813.

2. Min, Wei, Liang Jiang, Ji Yu, S C Kou, Hong Qian, and X Sunney Xie. 2005. “Nonequilibrium steady state of a nanometric biochemical system: Determining the thermodynamic driving force from single enzyme turnover time traces.” Nano Letters 5 (12): 2373–8. https://doi.org/10.1021/nl0521773.

3. Koslover, Elena F, and Andrew J Spakowitz. 2012. “Force fluctuations impact kinetics of biomolecular systems.” Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics 86 (1 Pt 1): 011906. http://www.ncbi.nlm.nih.gov/pubmed/23005451.

4. Samoilov, Michael S., Gavin Price, and Adam P. Arkin. 2006. “From fluctuations to phenotypes: the physiology of noise.” Science’s STKE : Signal Transduction Knowledge Environment 2006 (366). https://doi.org/10.1126/stke.3662006re17.

5. Eldar, Avigdor, and Michael B. Elowitz. 2010. “Functional roles for noise in genetic circuits.” Nature Publishing Group. https://doi.org/10.1038/nature09326.

6. Sanchez, Robersy, and Sally A. Mackenzie. 2016. “Information Thermodynamics of Cytosine DNA Methylation.” Edited by Barbara Bardoni. PLOS ONE 11 (3). Public Library of Science: e0150427. https://doi.org/10.1371/journal.pone.0150427.

7. Sanchez, Robersy, Xiaodong Yang, Thomas Maher, and Sally Mackenzie. 2019. “Discrimination of DNA Methylation Signal from Background Variation for Clinical Diagnostics.” Int. J. Mol. Sci. 20 (21): 5343. https://doi.org/https://doi.org/10.3390/ijms20215343.

8. Youden, W. J. 1950. “Index for rating diagnostic tests.” Cancer 3 (1): 32–35. https://doi.org/10.1002/1097-0142(1950)3:1<32::AID-CNCR2820030106>3.0.CO;2-3.

9. El Naqa, Issam, Dan Ruan, Gilmer Valdes, Andre Dekker, Todd McNutt, Yaorong Ge, Q. Jackie Wu, et al. 2018. “Machine learning and modeling: Data, validation, communication challenges.” Medical Physics 45 (10): e834–e840. https://doi.org/10.1002/mp.12811.

10. Fisher, R.A. 1938. “The statistical utilization of multiple measurents.” Annals of Eugenics 8: 376–86.

11. Green, Bert F. 1979. “The Two Kinds of Linear Discriminant Functions and Their Relationship.” Journal of Educational Statistics 4 (3): 247–63.

# PCA and LDA with Methyl-IT

## Principal Components and Linear Discriminant. Downstream Methylation Analyses with Methyl-IT

When methylation analysis is intended for diagnostic/prognostic purposes, for example, in clinical applications for patient diagnostics, to know whether the patient would be in healthy or disease stage we would like to have a good predictor tool in our side. It turns out that classical machine learning (ML) tools like hierarchical clustering, principal components and linear discriminant analysis can help us to reach such a goal. The current Methyl-IT downstream analysis is equipped with the mentioned ML tools.

# 1. Dataset

For the current example on methylation analysis with Methyl-IT we will use simulated data. Read-count matrices of methylated and unmethylated cytosine are generated with MethylIT.utils function simulateCounts. A basic example generating datasets is given in:

library(MethylIT) library(MethylIT.utils)
library(ggplot2)
library(ape)

alpha.ct <- 0.01
alpha.g1 <- 0.021
alpha.g2 <- 0.025

# The number of cytosine sites to generate
sites = 50000
# Set a seed for pseudo-random number generation
set.seed(124)
control.nam <- c("C1", "C2", "C3", "C4", "C5")
treatment.nam1 <- c("T1", "T2", "T3", "T4", "T5")
treatment.nam2 <- c("T6", "T7", "T8", "T9", "T10")

# Reference group
ref0 = simulateCounts(num.samples = 3, sites = sites, alpha = alpha.ct, beta = 0.5,
size = 50, theta = 4.5, sample.ids = c("R1", "R2", "R3"))
# Control group
ctrl = simulateCounts(num.samples = 5, sites = sites, alpha = alpha.ct, beta = 0.5,
size = 50, theta = 4.5, sample.ids = control.nam)
# Treatment group II
treat = simulateCounts(num.samples = 5, sites = sites, alpha = alpha.g1, beta = 0.5,
size = 50, theta = 4.5, sample.ids = treatment.nam1)

# Treatment group II
treat2 = simulateCounts(num.samples = 5, sites = sites, alpha = alpha.g2, beta = 0.5,
size = 50, theta = 4.5, sample.ids = treatment.nam2)

A reference sample (virtual individual) is created using individual samples from the control population using function poolFromGRlist. The reference sample is further used to compute the information divergences of methylation levels, $TV_d$ and $H$, with function estimateDivergence [1]. This is a first fundamental step to remove the background noise (fluctuations) generated by the inherent stochasticity of the molecular processes in the cells.

# === Methylation level divergences ===
# Reference sample
ref = poolFromGRlist(ref0, stat = "mean", num.cores = 4L, verbose = FALSE)

divs <- estimateDivergence(ref = ref, indiv = c(ctrl, treat, treat2), Bayesian = TRUE,
num.cores = 6L, percentile = 1, verbose = FALSE)

# To remove hd == 0 to estimate. The methylation signal only is given for
divs = lapply(divs, function(div) div[ abs(div$hdiv) > 0 ], keep.attr = TRUE) names(divs) <- names(divs) To get some statistical description about the sample is useful. Here, empirical critical values for the probability distribution of$H$and$TV_d$is obtained using quantile function from the R package stats. critical.val <- do.call(rbind, lapply(divs, function(x) { x <- x[x$hdiv > 0]
hd.95 = quantile(x$hdiv, 0.95) tv.95 = quantile(abs(x$TV), 0.95)
return(c(tv = tv.95, hd = hd.95))
}))
critical.val
##        tv.95%   hd.95%
## C1  0.2987088 21.92020
## C2  0.2916667 21.49660
## C3  0.2950820 21.71066
## C4  0.2985075 21.98416
## C5  0.3000000 22.04791
## T1  0.3376711 33.51223
## T2  0.3380282 33.00639
## T3  0.3387097 33.40514
## T4  0.3354077 31.95119
## T5  0.3402172 33.97772
## T6  0.4090909 38.05364
## T7  0.4210526 38.21258
## T8  0.4265781 38.78041
## T9  0.4084507 37.86892
## T10 0.4259411 38.60706

# 2. Modeling the methylation signal

Here, the methylation signal is expressed in terms of Hellinger divergence of methylation levels. Here, the signal distribution is modelled by a Weibull probability distribution model. Basically, the model could be a member of the generalized gamma distribution family. For example, it could be gamma distribution, Weibull, or log-normal. To describe the signal, we may prefer a model with a cross-validations: R.Cross.val > 0.95. Cross-validations for the nonlinear regressions are performed in each methylome as described in (Stevens 2009). The non-linear fit is performed through the function nonlinearFitDist.

The above statistical description of the dataset (evidently) suggests that there two main groups: control and treatments, while treatment group would split into two subgroups of samples. In the current case, to search for a good cutpoint, we do not need to use all the samples. The critical value $H_{\alpha=0.05}=33.51223$ suggests that any optimal cutpoint for the subset of samples T1 to T5 will be optimal for the samples T6 to T10 as well.

Below, we are letting the PCA+LDA model classifier to take the decision on whether a differentially methylated cytosine position is a treatment DMP. To do it, Methyl-IT function getPotentialDIMP is used to get methylation signal probabilities of the oberved $H$ values for all cytosine site (alpha = 1), in accordance with the 2-parameter Weibull distribution model. Next, this information will be used to identify DMPs using Methyl-IT function estimateCutPoint. Cytosine positions with $H$ values above the cutpoint are considered DMPs. Finally, a PCA + QDA model classifier will be fitted to classify DMPs into two classes: DMPs from control and those from treatment. Here, we fundamentally rely on a relatively strong $tv.cut \ge 0.34$ and on the signal probability distribution (nlms.wb) model.

dmps.wb <- getPotentialDIMP(LR = divs[1:10],
nlms = nlms.wb[1:10],  div.col = 9L,
tv.cut = 0.34, tv.col = 7, alpha = 1,
dist.name = "Weibull2P")
cut.wb = estimateCutPoint(LR = dmps.wb, simple = FALSE,
column = c(hdiv = TRUE, TV = TRUE,
wprob = TRUE, pos = TRUE),
classifier1 = "pca.lda",
classifier2 = "pca.qda", tv.cut = 0.34,
control.names = control.nam,
treatment.names = treatment.nam1,
post.cut = 0.5, cut.values = seq(15, 38, 1),
clas.perf = TRUE, prop = 0.6,
center = FALSE, scale = FALSE,
n.pc = 4, div.col = 9L, stat = 0)
cut.wb
## Cutpoint estimation with 'pca.lda' classifier
## Cutpoint search performed using model posterior probabilities
##
## Posterior probability used to get the cutpoint = 0.5
## Cytosine sites with treatment PostProbCut >= 0.5 have a
## divergence value >= 3.121796
##
## Optimized statistic: Accuracy = 1
## Cutpoint = 37.003
##
## Model classifier 'pca.qda'
##
## The accessible objects in the output list are:
##                    Length Class           Mode
## cutpoint           1      -none-          numeric
## testSetPerformance 6      confusionMatrix list
## testSetModel.FDR   1      -none-          numeric
## model              2      pcaQDA          list
## modelConfMatrix    6      confusionMatrix list
## initModel          1      -none-          character
## postProbCut        1      -none-          numeric
## postCut            1      -none-          numeric
## classifier         1      -none-          character
## statistic          1      -none-          character
## optStatVal         1      -none-          numeric

The cutpoint is higher from what is expected from the higher treatment empirical critical value and DMPs are found for $H$ values: $H^{TT_{Emp}}_{\alpha=0.05}=33.98<37≤H$. The model performance in the whole dataset is:

# Model performance in in the whole dataset
cut.wb$modelConfMatrix ## Confusion Matrix and Statistics ## ## Reference ## Prediction CT TT ## CT 4897 0 ## TT 2 9685 ## ## Accuracy : 0.9999 ## 95% CI : (0.9995, 1) ## No Information Rate : 0.6641 ## P-Value [Acc > NIR] : <2e-16 ## ## Kappa : 0.9997 ## Mcnemar's Test P-Value : 0.4795 ## ## Sensitivity : 1.0000 ## Specificity : 0.9996 ## Pos Pred Value : 0.9998 ## Neg Pred Value : 1.0000 ## Prevalence : 0.6641 ## Detection Rate : 0.6641 ## Detection Prevalence : 0.6642 ## Balanced Accuracy : 0.9998 ## ## 'Positive' Class : TT ##  # The False discovery rate cut.wb$testSetModel.FDR
## [1] 0

# 3. Represeting individual samples as vectors from the N-dimensional space

The above cutpoint can be used to identify DMPs from control and treatment. The PCA+QDA model classifier can be used any time to discriminate control DMPs from those treatment. DMPs are retrieved using selectDIMP function:

dmps.wb <- selectDIMP(LR = divs, div.col = 9L, cutpoint = 37, tv.cut = 0.34, tv.col = 7)

Next, to represent individual samples as vectors from the N-dimensional space, we can use getGRegionsStat function from MethylIT.utils R package. Here, the simulated “chromosome” is split into regions of 450bp non-overlapping windows. and the density of Hellinger divergences values is taken for each windows.

ns <- names(dmps.wb)
DMRs <- getGRegionsStat(GR = dmps.wb, win.size = 450, step.size = 450, stat = "mean", column = 9L)
names(DMRs) <- ns

# 4. Hierarchical Clustering

Hierarchical clustering (HC) is an unsupervised machine learning approach. HC can provide an initial estimation of the number of possible groups and information on their members. However, the effectivity of HC will depend on the experimental dataset, the metric used, and the glomeration algorithm applied. For an unknown reason (and based on the personal experience of the author working in numerical taxonomy), Ward’s agglomeration algorithm performs much better on biological experimental datasets than the rest of the available algorithms like UPGMA, UPGMC, etc.
dmgm <- uniqueGRanges(DMRs, verbose = FALSE)
dmgm <- t(as.matrix(mcols(dmgm)))
rownames(dmgm) <- ns
sampleNames <- ns

hc = hclust(dist(data.frame( dmgm ))^2, method = 'ward.D')
hc.rsq = hc
hc.rsq$height <- sqrt( hc$height )4.

## 4.1. Dendrogram

colors = sampleNames
colors[grep("C", colors)] <- "green4"
colors[grep("T[6-9]{1}", colors)] <- "red"
colors[grep("T10", colors)] <- "red"
colors[grep("T[1-5]", colors)] <- "blue"

# rgb(red, green, blue, alpha, names = NULL, maxColorValue = 1)
clusters.color = c(rgb(0, 0.7, 0, 0.1), rgb(0, 0, 1, 0.1), rgb(1, 0.2, 0, 0.1))

par(font.lab=2,font=3,font.axis=2, mar=c(0,3,2,0), family="serif" , lwd = 0.4)
plot(as.phylo(hc.rsq), tip.color = colors, label.offset = 0.5, font = 2, cex = 0.9,
edge.width  = 0.4, direction = "downwards", no.margin = FALSE,
align.tip.label = TRUE, adj = 0)
axisPhylo( 2, las = 1, lwd = 0.4, cex.axis = 1.4, hadj = 0.8, tck = -0.01 )
hclust_rect(hc.rsq, k = 3L, border = c("green4", "blue", "red"),
color = clusters.color, cuts = c(0.56, 15, 0.41, 300))

Here, we have use function as.phylo from the R package ape for better dendrogram visualization and function hclust_rect from MethylIT.utils R package to draw rectangles with background colors around the branches of a dendrogram highlighting the corresponding clusters.

# 5. PCA + LDA

MethylIT function will be used to perform the PCA and PCA + LDA analyses. The function returns a list of two objects: 1) ‘lda’: an object of class ‘lda’ from package ‘MASS’. 2) ‘pca’: an object of class ‘prcomp’ from package ‘stats’. For information on how to use these objects see ?lda and ?prcomp.

Unlike hierarchical clustering (HC), LDA is a supervised machine learning approach. So, we must provide a prior classification of the individuals, which can be derived, for example, from the HC, or from a previous exploratory analysis with PCA.

# A prior classification derived from HC
grps <- cutree(hc, k = 3)
grps[grep(1, grps)] <- "CT"
grps[grep(2, grps)] <- "T1"
grps[grep(3, grps)] <- "T2"
grps <- factor(grps)

ld <- pcaLDA(data = data.frame(dmgm), grouping = grps, n.pc = 3, max.pc = 3,
scale = FALSE, center = FALSE, tol = 1e-6)
summary(ld$pca) ## Importance of first k=3 (out of 15) components:## PC1 PC2 PC3## Standard deviation 41.5183 4.02302 3.73302## Proportion of Variance 0.9367 0.00879 0.00757## Cumulative Proportion 0.9367 0.94546 0.95303 We may retain enough components so that the cumulative percent of variance accounted for at least 70 to 80% [2]. By setting$scale=TRUE$and$center=TRUE$, we could have different results and would improve or not our results. In particular, these settings are essentials if the N-dimensional space is integrated by variables from different measurement scales/units, for example, Kg and g, or Kg and Km. ## 5.1. PCA The individual coordinates in the principal components (PCs) are returned by function pcaLDA. In the current case, based on the cumulative proportion of variance, the two firsts PCs carried about the 94% of the total sample variance and could split the sample into meaningful groups. pca.coord <- ld$pca$x pca.coord ## PC1 PC2 PC3## C1 -21.74024 0.9897934 -1.1708548 ## C2 -20.39219 -0.1583025 0.3167283 ## C3 -21.19112 0.5833411 -1.1067609 ## C4 -21.45676 -1.4534412 0.3025241 ## C5 -21.28939 0.4152275 1.0021932 ## T1 -42.81810 1.1155640 8.9577860 ## T2 -43.57967 1.1712155 2.5135643 ## T3 -42.29490 2.5326690 -0.3136530 ## T4 -40.51779 0.2819725 -1.1850555 ## T5 -44.07040 -2.6172732 -4.2384395 ## T6 -50.03354 7.5276969 -3.7333568 ## T7 -50.08428 -10.1115700 3.4624095 ## T8 -51.07915 -5.4812595 -6.7778593 ## T9 -50.27508 2.3463125 3.5371351 ## T10 -51.26195 3.5405915 -0.9489265 ## 5.2. Graphic PC1 vs PC2 Next, the graphic for individual coordinates in the two firsts PCs can be easely visualized now: dt <- data.frame(pca.coord[, 1:2], subgrp = grps) p0 <- theme( axis.text.x = element_text( face = "bold", size = 18, color="black", # hjust = 0.5, vjust = 0.5, family = "serif", angle = 0, margin = margin(1,0,1,0, unit = "pt" )), axis.text.y = element_text( face = "bold", size = 18, color="black", family = "serif", margin = margin( 0,0.1,0,0, unit = "mm" )), axis.title.x = element_text(face = "bold", family = "serif", size = 18, color="black", vjust = 0 ), axis.title.y = element_text(face = "bold", family = "serif", size = 18, color="black", margin = margin( 0,2,0,0, unit = "mm" ) ), legend.title=element_blank(), legend.text = element_text(size = 20, face = "bold", family = "serif"), legend.position = c(0.899, 0.12), panel.border = element_rect(fill=NA, colour = "black",size=0.07), panel.grid.minor = element_line(color= "white",size = 0.2), axis.ticks = element_line(size = 0.1), axis.ticks.length = unit(0.5, "mm"), plot.margin = unit(c(1,1,0,0), "lines")) ggplot(dt, aes(x = PC1, y = PC2, colour = grps)) + geom_vline(xintercept = 0, color = "white", size = 1) + geom_hline(yintercept = 0, color = "white", size = 1) + geom_point(size = 6) + scale_color_manual(values = c("green4","blue","brown1")) + stat_ellipse(aes(x = PC1, y = PC2, fill = subgrp), data = dt, type = "norm", geom = "polygon", level = 0.5, alpha=0.2, show.legend = FALSE) + scale_fill_manual(values = c("green4","blue","brown1")) + p0 ## 5.3. Graphic LD1 vs LD2 In the current case, better resolution is obtained with the linear discriminant functions, which is based on the three firsts PCs. Notice that the number principal components used the LDA step must be lower than the number of individuals ($N$) divided by 3:$N/3$. ind.coord <- predict(ld, newdata = data.frame(dmgm), type = "scores") dt <- data.frame(ind.coord, subgrp = grps) p0 <- theme( axis.text.x = element_text( face = "bold", size = 18, color="black", # hjust = 0.5, vjust = 0.5, family = "serif", angle = 0, margin = margin(1,0,1,0, unit = "pt" )), axis.text.y = element_text( face = "bold", size = 18, color="black", family = "serif", margin = margin( 0,0.1,0,0, unit = "mm" )), axis.title.x = element_text(face = "bold", family = "serif", size = 18, color="black", vjust = 0 ), axis.title.y = element_text(face = "bold", family = "serif", size = 18, color="black", margin = margin( 0,2,0,0, unit = "mm" ) ), legend.title=element_blank(), legend.text = element_text(size = 20, face = "bold", family = "serif"), legend.position = c(0.08, 0.12), panel.border = element_rect(fill=NA, colour = "black",size=0.07), panel.grid.minor = element_line(color= "white",size = 0.2), axis.ticks = element_line(size = 0.1), axis.ticks.length = unit(0.5, "mm"), plot.margin = unit(c(1,1,0,0), "lines")) ggplot(dt, aes(x = LD1, y = LD2, colour = grps)) + geom_vline(xintercept = 0, color = "white", size = 1) + geom_hline(yintercept = 0, color = "white", size = 1) + geom_point(size = 6) + scale_color_manual(values = c("green4","blue","brown1")) + stat_ellipse(aes(x = LD1, y = LD2, fill = subgrp), data = dt, type = "norm", geom = "polygon", level = 0.5, alpha=0.2, show.legend = FALSE) + scale_fill_manual(values = c("green4","blue","brown1")) + p0 ## References 1. Liese, Friedrich, and Igor Vajda. 2006. “On divergences and informations in statistics and information theory.” IEEE Transactions on Information Theory 52 (10): 4394–4412. doi:10.1109/TIT.2006.881731. 2. Stevens, James P. 2009. Applied Multivariate Statistics for the Social Sciences. Fifth Edit. Routledge Academic. # Methylation analysis with Methyl-IT. Part 3 Methylation analysis with Methyl-IT is illustrated on simulated datasets of methylated and unmethylated read counts with relatively high average of methylation levels: 0.15 and 0.286 for control and treatment groups, respectively. Herein, the detection of the methylation signal is confronted as a signal detection problem. A first look on the estimation of an optimal cutoff point for the methylation signal is covered. ## 1. Background Normally, there is a spontaneous variability in the control group. This is a consequence of the random fluctuations, or noise, inherent to the methylation process. The stochasticity of the the methylation process is derives from the stochasticity inherent in biochemical processes. There are fundamental physical reasons to acknowledge that biochemical processes are subject to noise and fluctuations [1,2]. So, regardless constant environment, statistically significant methylation changes can be found in control population with probability greater than zero and proportional to a Boltzmann factor [3]. Natural signals and those generated by human technology are not free of noise and, as mentioned above, the methylation signal is no exception. Only signal detection based approaches are designed to filter out the signal from the noise, in natural and in human generated signals. The need for the application of (what is now known as) signal detection in cancer research was pointed out by Youden in the midst of the last century [4]. Here, the application of signal detection approach was performed according with the standard practice in current implementations of clinical diagnostic test [5-7]. That is, optimal cutoff values of the methylation signal were estimated on the receiver operating characteristic curves (ROCs) and applied to identify DMPs. The decision of whether a DMP detected by Fisher’s exact test (or any other statistical test implemented in Methyl-IT) is taken based on the optimal cutoff value. ## 2. Cutpoint for the spontaneous variability in the control group In Methyl-IT function estimateCutPoint is used in the estimation of the optimal cutoff (cutpoint) value to distinguish signal from noise. There are also another available approaches that will be covered in a further post. # Cutpoint estimation for FT approach cut.ft = estimateCutPoint(LR = ft.tv, simple = TRUE, control.names = control.nam, treatment.names = treatment.nam, div.col = 7L, verbose = FALSE) # Cutpoint estimation for the FT approach using the ECDF critical value cut.ft.hd = estimateCutPoint(LR = ft.hd, simple = TRUE, control.names = control.nam, treatment.names = treatment.nam, div.col = 7L, verbose = FALSE) cut.emd = estimateCutPoint(LR = DMP.ecdf, simple = TRUE, control.names = control.nam, treatment.names = treatment.nam, div.col = 7L, verbose = FALSE) # Cutpoint estimation for the Weibull 2-parameter distribution approach cut.wb = estimateCutPoint(LR = DMPs.wb, simple = TRUE, control.names = control.nam, treatment.names = treatment.nam, div.col = 7L, verbose = FALSE) # Cutpoint estimation for the Gamma 2-parameter distribution approach cut.g2p = estimateCutPoint(LR = DMPs.g2p, simple = TRUE, control.names = control.nam, treatment.names = treatment.nam, div.col = 7L, verbose = FALSE) # Control cutpoint to define TRUE negatives and TRUE positives cuts <- data.frame(cut.ft = cut.ft$cutpoint, cut.ft.hd = cut.ft.hd$cutpoint, cut.ecdf = cut.emd$cutpoint, cut.wb = cut.wb$cutpoint, cut.g2p = cut.g2p$cutpoint)
cuts

##      cut.ft cut.ft.hd cut.ecdf   cut.wb   cut.g2p
## 1 0.9847716 0.9847716 0.987013 0.988024 0.9847716

Now, with high probability true DMP can be selected with Methyl-IT function selectDIMP.

ft.DMPs <- selectDIMP(ft.tv, div.col = 7L, cutpoint = 0.9847716, absolute = TRUE)
ft.hd.DMPs <- selectDIMP(ft.hd, div.col = 7L, cutpoint = 0.9847716, absolute = TRUE)
emd.DMPs <- selectDIMP(DMP.ecdf, div.col = 7L, cutpoint = 0.9847716, absolute = TRUE)
wb.DMPs <- selectDIMP(DMPs.wb, div.col = 7L, cutpoint = 0.9847716, absolute = TRUE)
g2p.DMPs <- selectDIMP(DMPs.g2p, div.col = 7L, cutpoint = 0.9847716, absolute = TRUE)


A summary table with the number of detected DMPs by each approach:

data.frame(ft = unlist(lapply(ft.DMPs, length)), ft.hd = unlist(lapply(ft.hd.DMPs, length)),
ecdf = unlist(lapply(DMP.ecdf, length)), Weibull = unlist(lapply(wb.DMPs, length)),
Gamma = unlist(lapply(g2p.DMPs, length)))
##      ft ft.hd ecdf Weibull Gamma
## C1  889   589   56     578   688
## C2  893   608   58     593   708
## C3  933   623   57     609   723
## T1 1846  1231  107     773  1087
## T2 1791  1182  112     775  1073
## T3 1771  1178  116     816  1093

Nevertheless, we should evaluate the classification performance as given in the next section.

## 3. Evaluation of DMP classification

As shown above, DMPs are found in the control population as well. Hence, it is important to know whether a DMP is the resulting effect of the treatment or just spontaneously occurred in the control sample as well. In particular, the confrontation of this issue is extremely important when methylation analysis is intended to be used as part of a diagnostic clinical test and a decision making in biotechnology industry.

Methyl-IT function is used here, to evaluate the classification of DMPs into one of the two classes, control and treatment. Several classifiers are available to be used with this function (see the help/manual for this function or type ?evaluateDIMPclass in R console).

To evaluate the classification performances, for each methylation analysis approach, we show the results with the best available classifier. Here, the best results were found with a logistic model and a quadratic discriminant analysis (QDA) based on principal component (PC).

### 3.1. Evaluation of Fisher’s exact test DMP classification

ft.class = evaluateDIMPclass(LR = ft.DMPs, control.names = control.nam,
treatment.names = treatment.nam,
column = c(hdiv = TRUE, TV = TRUE,
wprob = TRUE, pos = TRUE),
classifier = "logistic", interaction = "wprob:TV",
output = "conf.mat", prop = 0.6, pval.col = 11L
)## Model: treat ~ hdiv + TV + logP + pos + TV:logP
## $Performance ## Confusion Matrix and Statistics ## ## Reference ## Prediction CT TT ## CT 135 29 ## TT 618 1511 ## ## Accuracy : 0.7178 ## 95% CI : (0.6989, 0.7362) ## No Information Rate : 0.6716 ## P-Value [Acc > NIR] : 1.015e-06 ## ## Kappa : 0.2005 ## Mcnemar's Test P-Value : < 2.2e-16 ## ## Sensitivity : 0.9812 ## Specificity : 0.1793 ## Pos Pred Value : 0.7097 ## Neg Pred Value : 0.8232 ## Prevalence : 0.6716 ## Detection Rate : 0.6590 ## Detection Prevalence : 0.9285 ## Balanced Accuracy : 0.5802 ## ## 'Positive' Class : TT ## ## ##$FDR
## [1] 0.2902771
##
## $model ## ## Call: glm(formula = formula, family = binomial(link = "logit"), data = dt) ## ## Coefficients: ## (Intercept) hdiv TV logP pos ## 4.199e+01 -1.451e-01 -4.367e+01 1.092e+00 -6.602e-04 ## TV:logP ## -1.546e+00 ## ## Degrees of Freedom: 3437 Total (i.e. Null); 3432 Residual ## Null Deviance: 4353 ## Residual Deviance: 4067 AIC: 4079 ### 3.2. Evaluation DMP classification derived from Fisher’s exact test and ECDF critical value ft.hd.class = evaluateDIMPclass(LR = ft.hd.DMPs, control.names = control.nam, treatment.names = treatment.nam, column = c(hdiv = TRUE, TV = TRUE, wprob = TRUE, pos = TRUE), classifier = "logistic", interaction = "wprob:TV", pval.col = 11L, output = "conf.mat", prop = 0.6 ) ## Model: treat ~ hdiv + TV + logP + pos + TV:logPft.hd.class ##$Performance
## Confusion Matrix and Statistics
##
##           Reference
## Prediction  CT  TT
##         CT 174  18
##         TT 325 980
##
##                Accuracy : 0.7709
##                  95% CI : (0.7487, 0.792)
##     No Information Rate : 0.6667
##     P-Value [Acc > NIR] : < 2.2e-16
##
##                   Kappa : 0.3908
##  Mcnemar's Test P-Value : < 2.2e-16
##
##             Sensitivity : 0.9820
##             Specificity : 0.3487
##          Pos Pred Value : 0.7510
##          Neg Pred Value : 0.9062
##              Prevalence : 0.6667
##          Detection Rate : 0.6546
##    Detection Prevalence : 0.8717
##       Balanced Accuracy : 0.6653
##
##        'Positive' Class : TT
##
##
## $FDR ## [1] 0.2490421 ## ##$model
##
## Call:  glm(formula = formula, family = binomial(link = "logit"), data = dt)
##
## Coefficients:
## (Intercept)         hdiv           TV         logP          pos
##    229.1917      -0.1727    -232.0026       4.4204       0.1808
##     TV:logP
##     -4.9700
##
## Degrees of Freedom: 2241 Total (i.e. Null);  2236 Residual
## Null Deviance:       2854
## Residual Deviance: 2610  AIC: 2622

### 3.3. Evaluation ECDF based DMP classification

ecdf.class = evaluateDIMPclass(LR = DMP.ecdf, control.names = control.nam,
treatment.names = treatment.nam,
column = c(hdiv = TRUE, TV = TRUE,
wprob = TRUE, pos = TRUE),
classifier = "pca.qda", n.pc = 4, pval.col = 10L,
center = TRUE, scale = TRUE,
output = "conf.mat", prop = 0.6
)
ecdf.class
## $Performance ## Confusion Matrix and Statistics ## ## Reference ## Prediction CT TT ## CT 72 145 ## TT 4 4 ## ## Accuracy : 0.3378 ## 95% CI : (0.2763, 0.4036) ## No Information Rate : 0.6622 ## P-Value [Acc > NIR] : 1 ## ## Kappa : -0.0177 ## Mcnemar's Test P-Value : <2e-16 ## ## Sensitivity : 0.02685 ## Specificity : 0.94737 ## Pos Pred Value : 0.50000 ## Neg Pred Value : 0.33180 ## Prevalence : 0.66222 ## Detection Rate : 0.01778 ## Detection Prevalence : 0.03556 ## Balanced Accuracy : 0.48711 ## ## 'Positive' Class : TT ## ## ##$FDR
## [1] 0.5
##
## $model ##$qda
## Call:
## qda(ind.coord, grouping = data[resp][, 1], tol = tol, method = method)
##
## Prior probabilities of groups:
##        CT        TT
## 0.3373134 0.6626866
##
## Group means:
##             PC1         PC2         PC3         PC4
## CT  0.013261015  0.06134001  0.04739951 -0.09566950
## TT -0.006749976 -0.03122262 -0.02412678  0.04869664
##
## $pca ## Standard deviations (1, .., p=4): ## [1] 1.1635497 1.0098710 0.9744562 0.8226468 ## ## Rotation (n x k) = (4 x 4): ## PC1 PC2 PC3 PC4 ## hdiv -0.6692087 0.2408009 0.0214317 -0.7026488 ## TV -0.3430242 -0.4422200 0.8043031 0.1996806 ## logP 0.6439784 -0.1703322 0.3451435 -0.6611768 ## pos -0.1406627 -0.8470202 -0.4832319 -0.1710485 ## ## attr(,"class") ## [1] "pcaQDA" ### 3.4. Evaluation of Weibull based DMP classification ws.class = evaluateDIMPclass(LR = wb.DMPs, control.names = control.nam, treatment.names = treatment.nam, column = c(hdiv = TRUE, TV = TRUE, wprob = TRUE, pos = TRUE), classifier = "pca.qda", n.pc = 4, pval.col = 10L, center = TRUE, scale = TRUE, output = "conf.mat", prop = 0.6 ) ws.class ##$Performance
## Confusion Matrix and Statistics
##
##           Reference
## Prediction  CT  TT
##         CT 512   0
##         TT   0 725
##
##                Accuracy : 1
##                  95% CI : (0.997, 1)
##     No Information Rate : 0.5861
##     P-Value [Acc > NIR] : < 2.2e-16
##
##                   Kappa : 1
##  Mcnemar's Test P-Value : NA
##
##             Sensitivity : 1.0000
##             Specificity : 1.0000
##          Pos Pred Value : 1.0000
##          Neg Pred Value : 1.0000
##              Prevalence : 0.5861
##          Detection Rate : 0.5861
##    Detection Prevalence : 0.5861
##       Balanced Accuracy : 1.0000
##
##        'Positive' Class : TT
##
##
## $FDR ## [1] 0 ## ##$model
## $qda ## Call: ## qda(ind.coord, grouping = data[resp][, 1], tol = tol, method = method) ## ## Prior probabilities of groups: ## CT TT ## 0.4133837 0.5866163 ## ## Group means: ## PC1 PC2 PC3 PC4 ## CT 0.0006129907 0.02627154 0.01436693 -0.3379167 ## TT -0.0004319696 -0.01851334 -0.01012426 0.2381272 ## ##$pca
## Standard deviations (1, .., p=4):
## [1] 1.3864692 1.0039443 0.9918014 0.2934775
##
## Rotation (n x k) = (4 x 4):
##              PC1         PC2         PC3           PC4
## hdiv -0.70390059 -0.01853114 -0.06487288  0.7070870321
## TV   -0.08754381  0.61403346  0.78440944  0.0009100769
## logP  0.70393865  0.01695660  0.06446889  0.7071255955
## pos  -0.03647491 -0.78888021  0.61346321 -0.0007020890
##
## attr(,"class")
## [1] "pcaQDA"

### 3.5. Evaluation of Gamma based DMP classification

g2p.class = evaluateDIMPclass(LR = g2p.DMPs, control.names = control.nam,
treatment.names = treatment.nam,
column = c(hdiv = TRUE, TV = TRUE,
wprob = TRUE, pos = TRUE),
classifier = "pca.qda", n.pc = 4, pval.col = 10L,
center = TRUE, scale = TRUE,
output = "conf.mat", prop = 0.6
)
g2p.class
## $Performance ## Confusion Matrix and Statistics ## ## Reference ## Prediction CT TT ## CT 597 0 ## TT 0 970 ## ## Accuracy : 1 ## 95% CI : (0.9976, 1) ## No Information Rate : 0.619 ## P-Value [Acc > NIR] : < 2.2e-16 ## ## Kappa : 1 ## Mcnemar's Test P-Value : NA ## ## Sensitivity : 1.000 ## Specificity : 1.000 ## Pos Pred Value : 1.000 ## Neg Pred Value : 1.000 ## Prevalence : 0.619 ## Detection Rate : 0.619 ## Detection Prevalence : 0.619 ## Balanced Accuracy : 1.000 ## ## 'Positive' Class : TT ## ## ##$FDR
## [1] 0
##
## $model ##$qda
## Call:
## qda(ind.coord, grouping = data[resp][, 1], tol = tol, method = method)
##
## Prior probabilities of groups:
##        CT        TT
## 0.3810132 0.6189868
##
## Group means:
##            PC1          PC2           PC3        PC4
## CT -0.09825234  0.009217742 -0.0015298812 -0.3376429
## TT  0.06047858 -0.005673919  0.0009417082  0.2078338
##
## \$pca
## Standard deviations (1, .., p=4):
## [1] 1.3934010 1.0005617 0.9910414 0.2741290
##
## Rotation (n x k) = (4 x 4):
##              PC1         PC2         PC3           PC4
## hdiv -0.70097482 -0.01600430 -0.09106340  0.7071673216
## TV   -0.13109213  0.26685824  0.95477779 -0.0009560507
## logP  0.70085340  0.01201179  0.09357872  0.7070383630
## pos  -0.01592678 -0.96352803  0.26711394 -0.0031966410
##
## attr(,"class")
## [1] "pcaQDA"

### 3.6. Summary of DMP classification performance

For the current simulated dataset, the best classification performance was obtained for the approach of DMP detection based on a 2-parameter gamma probability distribution model for the Hellinger divergence of methylation levels. DMPs from treatment can be distinguished from control DMPs with very high accuracy. The second best approach was obtained for the 2-parameter Weibull probability distribution model.

Obviously, for practical application, we do not need to go through all these steps. Herein, we just illustrate the need for a knowledge on the probability distributions of the signal plus noise in control and in treatment groups. The available approaches for methylation analysis are not designed to evaluate the natural variation in the control population. As a matter of fact, the phrase “natural variation” itself implies the concept of probability distribution of the variable under study. Although the probability distribution of the variable measured is objective and, as such, it does not depend on the model assumptions, the selection of the best fitted model notably improves the accuracy of our predictions.

## 4. Conclusions Summary

Herein, an illustrative example of methylation analysis with Methyl-IT have been presented. Whatever could be the statistical test/approach used to identify DMPs, the analysis with simulated datasets, where the average of methylation levels in the control samples is relatively high, indicates the need for the application of signal detection based approaches.

### 4.1. Concluding remarks

The simplest suggested steps to follow for a methylation analysis with Methyl-IT are:

1. To estimate a reference virtual sample from a reference group by using function poolFromGRlist. Notice that several statistics are available to estimate the virtual samples, i.e., mean, median, sum. For experiments limited by the number of sample, at least, try the estimation of the virtual sample from the control group. Alternatively, the whole reference group can be used in pairwise comparisons with control and treatment groups (computationally expensive).
2. To estimate information divergence using function estimateDivergence
1. To perform the estimation of the cumulative density function of the Hellinger divergence of methylation levels using function nonlinearFitDist.
1. To get the potential DMPs using function getPotentialDIMP.
1. To estimate the optimal cutpoint using function estimateCutPoint.
1. To retrieve DMPs with function selectDIMP.
1. To evaluate the classificatio performance using function evaluateDIMPclass.

As shown here, alternative analysis is possible by using Fisher’s exact test (FT). Whether FT would be better than the approach summarized above will depend on the dataset under study. The approaches with Root Mean Square Test (RMST) and Hellinger divergence test (HDT) are also possible with function rmstGR. In these cases, we can proceed as suggested for FT. In general, RMST and HDT yield better results (not discussed here) than FT.

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