# 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 ## An example of methylation analysis with simulated datasets Part 3: Estimation of an optimal cutpoint for the methylation signal 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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