---
title: "Data Analyses"
author:
- Sean K. Maden
- Reid F. Thompson
- Kasper D. Hansen
- Abhinav Nellore
date: "`r format(Sys.time(), '%d %B, %Y')`"
bibliography: bibliography.bib
package: recountmethylation
vignette: >
%\VignetteIndexEntry{Data Analyses}
%\VignetteDepends{RCurl}
%\usepackage[UTF-8]{inputenc}
%\VignetteEncoding{UTF-8}
%\VignetteEngine{knitr::rmarkdown}
output:
BiocStyle::pdf_document:
toc: yes
toc_depth: 3
BiocStyle::html_document:
code_folding: show
toc: yes
tocfloat: yes
---
```{r setup, echo = FALSE}
suppressMessages(library(rhdf5))
suppressMessages(library(minfi))
suppressMessages(library(recountmethylation))
suppressMessages(library(knitr))
suppressMessages(library(ggplot2))
suppressMessages(library(gridExtra))
suppressMessages(library(GenomicRanges))
suppressMessages(library(limma))
suppressMessages(library(HDF5Array))
opts_chunk$set(eval = FALSE, echo = TRUE,
warning = FALSE, message = FALSE)
```
# Overview
This vignette walks through 3 analysis examples using data accessed with the
`recountmethylation` package. First, predicted and chronological ages are
compared from the sample metadata. Then quality signals (methylated and
unmethylated, log2 median scale) are compared between samples stored using
either formalin fixed paraffin-embedding (FFPE) or freezing. Finally,
tissue-specific probe sets with high DNA methylation (DNAm) fraction variances
are identifed and analyzed using liver and adipose samples. Note that versions
of these analyses also appear in the manuscript @maden_human_2020.
## Analysis script and limited chunk evaluation
This vignette accompanies the "data_analyses.R" script. Note the script was written with extensibility to new and larger comparator groups in mind. While the script should run
to completion without errors, it takes several hours in total to complete (excluding
the time to download large database files). Due to this lengthy script run time,
this vignette only evaluates code chunks utilizing final/resultant data objects
produced by the script (e.g. for tables, tests, and figures). For completeness,
remaining script steps and code are included but not evaluated.
Load the file "data_analyses.RData" from the `recountmethylation` package files.
This contains the resultant/final data objects produced by the script, which will
be used in evaluated code chunks below.
```{r, eval = TRUE}
sf <- system.file(file.path("extdata", "data_analyses"),
package = "recountmethylation")
load(file.path(sf, "data_analyses.RData"))
```
## Datasets and data objects
The analysis script uses sample metadata and 2 database files. Retrieve the provided sample metadata from the `recountmethylation` package files.
```{r, eval = TRUE}
# get local metadata
path <- system.file("extdata", "metadata", package = "recountmethylation")
mdpath <- paste(path, list.files(path)[1], sep = "/")
md <- get(load(mdpath))
```
Also obtain 2 `HDF5-SummarizedExperiment` database files, the
GenomicRanges and MethylSet files. Consult the `users_guide` vignette for
details about the database file formats and download instructions. Once the
datasets downloaded, they can be loaded into an R session as follows.
```{r}
# load methylset
gmdn <- "remethdb-h5se_gm_0-0-1_1590090412"
gm <- loadHDF5SummarizedExperiment(gmdn)
# load grset
grdn <- "remethdb-h5se_gr_0-0-1_1590090412"
gr <- loadHDF5SummarizedExperiment(grdn)
```
# Example 1: Comparing mined and predicted age
This example uses sample metadata to compare mined and predicted ages from
the `age` and `predage` variables, respectively. Values in `age` were mined
from GEO record metadata and are included with available age units. Values in
`predage` were calculated from noob-normalized (@triche_low-level_2013) DNAm
Beta-values with `agep`, a function from the `wateRmelon` package that
implements the Horvath biological age clock (@horvath_dna_2013).
## Make new variables and filter samples
Get samples for which both `age` and `predage` age are available. From `age`,
make a new numeric variable `chron.age`.
```{r, eval = TRUE}
mdf <- md[!md$age == "valm:NA",]
mdf$chron.age <- as.numeric(gsub(";.*", "", gsub("^valm:", "", mdf$age)))
mdf$predage <- as.numeric(mdf$predage)
mdf <- mdf[!is.na(mdf$chron.age),]
mdf <- mdf[!is.na(mdf$predage),]
```
Next, make a new variable `stype` from `sampletype` and remove samples with
missing values.
```{r, eval = TRUE}
mdf$stype <- as.character(gsub(";.*", "",
gsub("^msraptype:", "", mdf$sampletype)))
mdf <- mdf[!is.na(mdf$stype),]
```
Now make a new variable `is.cx` from querying `cancer` in the `disease` term.
This reflects whether a sample was likely from a cancer or a cancer patient.
```{r, eval = TRUE}
mdf$is.cx <- ifelse(grepl(".*cancer.*", mdf$disease), TRUE, FALSE)
```
Next, store the study-wise age differences in the `xdif` variable using the
mean absolute difference (a.k.a. "MAD") between `chron.age` and `predage`
across samples from the same study. Also store study sizes in the `ngsm` term
for plotting.
```{r, eval = TRUE}
xdif <- ngsm <- c()
for(g in unique(mdf$gseid)){
mdff <- mdf[mdf$gseid==g, ]
xdif <- c(xdif, mean(abs(mdff$chron.age - as.numeric(mdff$predage))))
ngsm <- c(ngsm, nrow(mdff))
}
names(xdif) <- names(ngsm) <- unique(mdf$gseid)
```
Make a new filtered `mdff` data frame using the new variables. Retain likely
non-cancer samples from studies with MAD <= 10 years. Pre- and post-filter
datasets (groups 1 and 2, respectively) are summarized below.
```{r, eval = TRUE}
filt <- mdf$stype == "tissue" & !mdf$is.cx
filt <- filt & !mdf$gseid %in% names(xdif[xdif > 10])
mdff <- mdf[filt, ]
```
## Analyses and summary statistics
Perform statistical analyses of `mdf` (group 1) and `mdff` (group 2). First,
generate multiple regressions for each.
```{r, eval = TRUE}
lm1 <- lm(mdf$predage ~ mdf$chron.age + mdf$gseid + mdf$stype + mdf$is.cx)
lm2 <- lm(mdff$predage ~ mdff$chron.age + mdff$gseid)
```
Now perform analyses of variances (ANOVAs) on multiple regressions. Summarize
variance percentages and p-values for covariates in each model. Columns
"Vperc" and "Pval" are the percent variance and unadjusted p-value for
covariates in each model.
```{r, eval = TRUE}
# anovas
av1 <- anova(lm1)
av2 <- anova(lm2)
# results summaries
sperc1 <- round(100*av1$`Sum Sq`[1:4]/sum(av1$`Sum Sq`), 2)
pval1 <- format(av1$`Pr(>F)`[1:4], scientific = TRUE, digits = 3)
sperc2 <- round(100*av2$`Sum Sq`[1:2]/sum(av2$`Sum Sq`), 2)
pval2 <- format(av2$`Pr(>F)`[1:2], scientific = TRUE, digits = 3)
# summary table
dan <- data.frame(Vperc1 = c(sperc1),
Pval1 = c(pval1),
Vperc2 = c(sperc2, "-", "-"),
Pval2 = c(pval2, "-", "-"),
stringsAsFactors = FALSE)
rownames(dan) <- c("Chron.Age", "GSEID", "SampleType", "Cancer")
knitr::kable(dan, align = "c")
```
Now calcualte the R-squared, Spearman correlation coefficient (Rho), and MAD
for each model.
```{r, eval = TRUE}
# rsquared
rsq1 <- round(summary(lm1)$r.squared, 2)
rsq2 <- round(summary(lm2)$r.squared, 2)
# correlation coefficient
rho1 <- round(cor.test(mdf$predage, mdf$chron.age,
method = "spearman")$estimate, 2)
rho2 <- round(cor.test(mdff$predage, mdff$chron.age,
test = "spearman")$estimate, 2)
# mean absolute difference
mad1 <- round(mean(abs(mdf$chron.age - mdf$predage)), 2)
mad2 <- round(mean(abs(mdff$chron.age - mdff$predage)), 2)
```
Finally, organize and display the results
```{r, eval = TRUE}
dss <- data.frame(group = c("1", "2"),
ngsm = c(nrow(mdf), nrow(mdff)),
ngse = c(length(unique(mdf$gseid)),
length(unique(mdff$gseid))),
r.squared = c(rsq1, rsq2), rho = as.character(c(rho1, rho2)),
mad = c(mad1, mad2), stringsAsFactors = FALSE)
knitr::kable(dss, align = "c")
```
## Scatter plots of study errors and sample ages
Plot sample counts and MAD for each GSE record, with a vertical line at the
10-years MAD cutoff used for the group 2 filter.
```{r, eval = TRUE, fig.width = 3.8, fig.height = 3.4}
plot(xdif, ngsm, ylab = "Study Size (Num. GSM)",
xlab = "Age Difference, MAD[Chron, Pred]")
abline(v = 10, col = "red")
```
Finally, plot the chronological and predicted ages for group 2 samples.
```{r, eval = TRUE, fig.width = 3.4, fig.height = 3.1}
ggplot(mdff, aes(x = chron.age, y = predage)) +
geom_point(size = 1.2, alpha = 0.2) + geom_smooth(method = "lm", size = 1.2) +
theme_bw() + xlab("Chronological Age") + ylab("Epigenetic (DNAm) Age")
```
# Example 2: Signal comparison of FFPE and frozen samples
This section compares methylated and unmethylated signal (log2 sample median
scale) between samples stored with either FFPE or fresh freezing (FF).
## Get samples with storage type information
Identify and summarize samples with the `storage` variable available. Use
values in `storage` to inform a new `sgroup` variable.
```{r, eval = TRUE}
mdf <- md[!md$storage == "NA",]
mdf$sgroup <- ifelse(grepl("FFPE", mdf$storage), "ffpe", "frozen")
```
```{r}
# get summary table
sst <- get_sst(sgroup.labs = c("ffpe", "frozen"), mdf)
knitr::kable(sst, align = "c") # table display
```
## Use blocking to calculate signal log2 medians
Subset the `MethylSet` object and extract the full signal matrices with the
`getMeth` and `getUnmeth` functions from the minfi package.
```{r}
gmf <- gm[, gm$gsm %in% mdf$gsm] # filt h5se object
mdf <- mdf[order(match(mdf$gsm, gmf$gsm)),]
identical(gmf$gsm, mdf$gsm)
gmf$storage <- mdf$storage # append storage info
```
```{r}
meth.all <- getMeth(gmf)
unmeth.all <- getUnmeth(gmf)
```
Next, prepare to calculate log2 median signals. To manage data in active
memory, process it in smaller units or blocks. Using the `get_blocks` helper
function, assign sample indices to blocks of size 1,000 using the `bsize`
argument.
```{r}
blocks <- getblocks(slength = ncol(gmf), bsize = 1000)
```
Now calculate log2 of sample median signals for each block. Vectorize
calculations within blocks with `apply`. Store results in the data.frame `ds`.
```{r}
ms <- matrix(nrow = 0, ncol = 2)
l2meth <- l2unmeth <- c()
for(i in 1:length(blocks)){
b <- blocks[[i]]
gmff <- gmf[, b]
methb <- as.matrix(meth.all[, b])
unmethb <- as.matrix(unmeth.all[, b])
l2meth <- c(l2meth, apply(methb, 2, function(x){
log2(median(as.numeric(x)))
}))
l2unmeth <- c(l2unmeth, apply(unmethb, 2, function(x){
log2(median(as.numeric(x)))
}))
ms <- rbind(ms, matrix(c(l2meth, l2unmeth), ncol = 2))
message(i)
}
rownames(ms) <- colnames(meth.all)
colnames(ms) <- c("meth.l2med", "unmeth.l2med")
ds <- as.data.frame(ms)
ds$storage <- ifelse(grepl("FFPE", gmf$storage), "ffpe", "frozen")
```
## Signals plotted by storage type
Evaluate signal patterns across storage type using plots using the ggplot2
package. First, make a 2d scatter plot of methylated and unmethylated signals
using the `geom_point` function. Color by storage type with the
`scale_color_manual` function (FFPE samples are orange, frozen samples are
purple).
```{r, eval = TRUE, fig.width = 4.3, fig.height = 3.1}
ggplot(ds, aes(x = meth.l2med, y = unmeth.l2med, color = storage)) +
geom_point(alpha = 0.35, cex = 3) + theme_bw() +
scale_color_manual(values = c("ffpe" = "orange", "frozen" = "purple"))
```
Next, make separate violin plots for signals and groups using the `geom_violin`
function with the same colors for each storage type. Draw horizontal median
lines by setting the `draw_quantiles` argument to 0.5.
```{r, eval = TRUE, fig.width = 4.5, fig.height = 2.5}
vp <- matrix(nrow = 0, ncol = 2)
vp <- rbind(vp, matrix(c(ds$meth.l2med, paste0("meth.", ds$storage)),
ncol = 2))
vp <- rbind(vp, matrix(c(ds$unmeth.l2med, paste0("unmeth.", ds$storage)),
ncol = 2))
vp <- as.data.frame(vp, stringsAsFactors = FALSE)
vp[,1] <- as.numeric(vp[,1])
colnames(vp) <- c("signal", "group")
vp$col <- ifelse(grepl("ffpe", vp$group), "orange", "purple")
# make plot
ggplot(vp, aes(x = group, y = signal, color = group)) +
scale_color_manual(values = c("meth.ffpe" = "orange",
"unmeth.ffpe" = "orange", "meth.frozen" = "purple",
"unmeth.frozen" = "purple")) +
geom_violin(draw_quantiles = c(0.5)) + theme_bw() +
theme(legend.position = "none")
```
# Example 3: Identify and analyze tissue-specific probes with the highest
variances
This example describes variance analyses in liver and adipose, 2 of the 7
tissues analyzed in the manuscript @maden_human_2020. This includes a quality
assessment, study ID linear adjustment of DNAm fractions, ANOVA-based and
probe filtering, 2-step variance analyses, and results plots.
## Sample identification and summary
Summarize the samples of interest. Use two vectors of GSM IDs, `adipose.gsmv`
and `liver.gsmv` to filter the metadata (see vectors in the data_analyses.R
script). Also define tissues in the new group variable `sgroup`. Summarize the
sample groups in a table
```{r, eval = TRUE}
gsmv <- c(adipose.gsmv, liver.gsmv)
mdf <- md[md$gsm %in% gsmv,]
mdf$sgroup <- ifelse(mdf$gsm %in% adipose.gsmv, "adipose", "liver")
sst.tvar <- get_sst(sgroup.labs = c("liver", "adipose"), mdf)
knitr::kable(sst.tvar, align = "c")
```
## Calculate log2 methylated and unmethylated signal medians
Subset the `MethylSet` dataset, then append the `sgroup` variable from `mdf`
and map the object to the genome using the `mapToGenome` function from the
`minfi` package.
```{r}
ms <- gm[,colnames(gm) %in% rownames(mdf)]
ms <- ms[,order(match(colnames(ms), rownames(mdf)))]
identical(colnames(ms), rownames(mdf))
# [1] TRUE
ms$sgroup <- mdf$sgroup
ms <- mapToGenome(ms)
dim(ms)
# [1] 485512 252
```
As in example 2 above, calculate the sample log2 median signals from signal
matrices. Process the data in blocks using within-block vectorization with
`apply`.
```{r}
# get log2 medians
meth.tx <- getMeth(ms)
unmeth.tx <- getUnmeth(ms)
blocks <- getblocks(slength = ncol(ms), bsize = 50)
# process data in blocks
l2m <- matrix(nrow = 0, ncol = 2)
for(i in 1:length(blocks)){
b <- blocks[[i]]
gmff <- ms[, b]
methb <- as.matrix(meth.tx[, b])
unmethb <- as.matrix(unmeth.tx[, b])
l2meth <- l2unmeth <- c()
l2meth <- c(l2meth, apply(methb, 2, function(x){
log2(median(as.numeric(x)))
}))
l2unmeth <- c(l2unmeth, apply(unmethb, 2, function(x){
log2(median(as.numeric(x)))
}))
l2m <- rbind(l2m, matrix(c(l2meth, l2unmeth), ncol = 2))
message(i)
}
ds2 <- as.data.frame(l2m)
colnames(ds2) <- c("l2med.meth", "l2med.unmeth")
ds2$tissue <- as.factor(ms$sgroup)
```
Make a scatter plot of log2 median signals by tissue type with the `geom_point`
function.
```{r, eval = TRUE, fig.width = 4.5, fig.height = 3.1}
ggplot(ds2, aes(x = l2med.meth, y = l2med.unmeth, color = tissue)) +
geom_point(alpha = 0.3, cex = 3) + theme_bw()
```
## Perform linear correction on DNAm for study IDs
Access the noob-normalized DNAm Beta-values from the `GenomicRatio` object `gr`
loaded above. Extract the DNAm fractions as M-values (logit2 transformed
Beta-values) with the `getM` minfi function. Perform linear correction on
study ID with the `removeBatchEffect` function from the limma package by
setting the `batch` argument to the "gseid" variable.
```{r}
lmv <- lgr <- lmd <- lb <- lan <- list()
tv <- c("adipose", "liver")
# get noob norm data
gr <- gr[,colnames(gr) %in% colnames(ms)]
gr <- gr[,order(match(colnames(gr), colnames(ms)))]
identical(colnames(gr), colnames(ms))
gr$sgroup <- ms$sgroup
# do study ID adj
for(t in tv){
lmv[[t]] <- gr[, gr$sgroup == t]
msi <- lmv[[t]]
madj <- limma::removeBatchEffect(getM(msi), batch = msi$gseid)
# store adjusted data in a new se object
lgr[[t]] <- GenomicRatioSet(GenomicRanges::granges(msi), M = madj,
annotation = annotation(msi))
# append samples metadata
lmd[[t]] <- pData(lgr[[t]]) <- pData(lmv[[t]])
# append preprocessing metadata
metadata(lgr[[t]]) <- list("preprocess" = "noobbeta;removeBatchEffect_gseid")
# make betavals list
lb[[t]] <- getBeta(lgr[[t]]) # beta values list
}
```
## Perform array-wide ANOVAs and filter probes
Prepare and run ANOVAs on autosomal probes. First, identify and remove sex
chromosome probes by accessing annotation with the `getAnnotation` minfi
function. List the filtered data in the `lbf` object.
```{r}
anno <- getAnnotation(gr)
chr.xy <-c("chrY", "chrX")
cg.xy <- rownames(anno[anno$chr %in% chr.xy,])
lbf <- list()
for(t in tv){
bval <- lb[[t]]
lbf[[t]] <- bval[!rownames(bval) %in% cg.xy,]
}
bv <- lbf[[1]]
```
Next, select and format the 9 model covariates for the ANOVA tests. From sample
metadata, select the variables for study ID ("gseid"), predicted sex
("predsex"), predicted age ("predage"), and predicted fractions of 6 cell types
("predcell..*"). Convert these to either factor or numeric type with the
functions `as.factor` and `as.numeric`, respectively.
```{r}
lvar <- list()
cnf <- c("gseid", "predsex", "predage", "predcell.CD8T",
"predcell.CD4T", "predcell.NK", "predcell.Bcell",
"predcell.Mono", "predcell.Gran")
for(t in tv){
for(c in cnf){
if(c %in% c("gseid", "predsex")){
lvar[[t]][[c]] <- as.factor(pData(lgr[[t]])[,c])
} else{
lvar[[t]][[c]] <- as.numeric(pData(lgr[[t]])[,c])
}
}
}
```
Run ANOVAs on probe Beta-values. Use the blocking-with-vectorization strategy
here as above, with large blocks of 100,000 sample indices each. Calculations
should complete in about 1 hour. For each test, retain unadjusted p-values and
variance percentages of the 9 covariates. Store the 18-column results matrices
in the `lan` list object.
```{r}
bv <- lbf[[1]]
blocks <- getblocks(slength = nrow(bv), bsize = 100000)
mr <- matrix(nrow = 0, ncol = 18)
lan <- list("adipose" = mr, "liver" = mr)
t1 <- Sys.time()
for(bi in 1:length(blocks)){
for(t in tv){
datr <- lbf[[t]][blocks[[bi]],]
tvar <- lvar[[t]]
newchunk <- t(apply(datr, 1, function(x){
# do multiple regression and anova
x <- as.numeric(x)
ld <- lm(x ~ tvar[[1]] + tvar[[2]] + tvar[[3]] + tvar[[4]] +
tvar[[5]] + tvar[[6]] + tvar[[7]] + tvar[[8]] + tvar[[9]])
an <- anova(ld)
# get results
ap <- an[c(1:9),5] # pval
av <- round(100*an[c(1:9),2]/sum(an[,2]), 3) # percent var
return(as.numeric(c(ap, av)))
}))
# append new results
lan[[t]] <- rbind(lan[[t]], newchunk)
}
message(bi, "tdif: ", Sys.time() - t1)
}
# append colnames
for(t in tv){colnames(lan[[t]]) <- rep(cnf, 2)}
```
Next, remove probes showing evidence of residual confounding from the
covariates. Adjust covariate p-values with the `p.adjust` function, and retain
probes with adjusted p-values >= 0.001 and variance < 10% variance for all 9
covariates. Retain the filtered probe DNAm data as `GenomicRatioSet`s for each
tissue in the list `lgr.filt`.
```{r}
pfilt <- 1e-3
varfilt <- 10
lcgkeep <- list() # list of filtered probe sets
for(t in tv){
pm <- lan[[t]][,c(1:9)]
vm <- lan[[t]][,c(10:18)]
# parse variable thresholds
cm <- as.data.frame(matrix(nrow = nrow(pm), ncol = ncol(pm)))
for(c in 1:ncol(pm)){
pc <- pm[,c];
pc.adj <- as.numeric(p.adjust(pc))
pc.filt <- pc.adj < pfilt
vc.filt <- vm[,c] >= varfilt
cm[,c] <- (pc.filt & vc.filt)
}
cgkeep <- apply(cm, 1, function(x){return((length(x[x == TRUE]) == 0))})
lcgkeep[[t]] <- rownames(pm)[cgkeep]
}
lgr.filt <- list("adipose" = lgr[[1]][lcgkeep[[1]],],
"liver" = lgr[[2]][lcgkeep[[2]],])
```
## Get probe DNAm summary statistics and analyze variances
Calculate probe DNAm summary statistics. For each tissue, calculate the minima,
maxima, means, medians, standard deviations, and variances of Beta-values
across samples. Store results in the `lcg.ss` list.
```{r}
cnv <- c("min", "max", "mean", "median", "sd", "var")
bv <- getBeta(lgr.filt[[t]])
lbt <- lcg.ss <- list()
bsize = 100000
for(t in tv){
lcg.ss[[t]] <- matrix(nrow = 0, ncol = 6)
lbt[[t]] <- bt <- as.matrix(getBeta(lgr.filt[[t]]))
blockst <- getblocks(slength = nrow(bt), bsize = bsize)
for(bi in 1:length(blockst)){
bc <- bt[blockst[[bi]],]
newchunk <- t(apply(bc, 1, function(x){
newrow <- c(min(x), max(x), mean(x), median(x), sd(x), var(x))
return(as.numeric(newrow))
}))
lcg.ss[[t]] <- rbind(lcg.ss[[t]], newchunk)
message(t, ";", bi)
}
colnames(lcg.ss[[t]]) <- cnv
}
```
Perform the main variance analyses with 2 strategies. This selects the 2,000
probes with the highest group-specific variances.
First, use a single variance cutoff, or "absolute" quantile cutoff, for each
group. List probes in the top 99th quantile variances for each tissue in the
`lmvp.abs` object.
```{r}
qiv = seq(0, 1, 0.01)
qwhich = c(100)
lmvp.abs <- list()
lci <- list()
for(t in tv){
cgv <- c()
sa <- lcg.ss[[t]]
sa <- as.data.frame(sa, stringsAsFactors = FALSE)
q <- quantile(sa$var, qiv)[qwhich]
lmvp.abs[[t]] <- rownames(sa[sa$var > q,])
}
```
Now select high-variance probes with binning for each tissue. Assign probes to
1 of 10 bins using 0.1 mean Beta-value intervals. Select probes in the top
99th variance quantiles for each bin, and store in `lmvp.bin`.
```{r}
# binned quantiles method
qiv = seq(0, 1, 0.01) # quantile filter
qwhich = c(100)
bin.xint <- 0.1
binv = seq(0, 1, bin.xint)[1:10] # binned bval mean
# iter on ncts
lmvp.bin = list()
for(t in tv){
sa <- as.data.frame(lcg.ss[[t]])
cgv <- c()
# iterate on betaval bins
for(b in binv){
bf <- sa[sa$mean >= b & sa$mean < b + bin.xint, ] # get probes in bin
q <- qf <- quantile(bf$var, qiv)[qwhich] # do bin filter
cgv <- c(cgv, rownames(bf)[bf$var > q]) # append probes list
}
lmvp.bin[[t]] <- cgv
}
```
With the variance analyses complete, filter the `lmvp.abs` and `lmvp.bin`
probes by tissue specificity. Tissue-specific probes should only occur among
high variance probes for a single tissue. Categorize probes as
"tissue-specific" or "non-specific" using the `table` function to determine
their frequency of occurrence across tissues.
```{r, eval = TRUE}
cgav <- c()
for(t in tv){
txcg <- unique(c(lmvp.abs[[t]], lmvp.bin[[t]]))
cgav <- c(cgav, txcg)
}
cgdf <- as.data.frame(table(cgav))
cgdf$type <- ifelse(cgdf[,2] > 1, "non-specific", "tissue-specific")
table(cgdf$type)
```
After filtering probes by tissue specificity, rank them by descending DNAm
variance. Select 1,000 probes from `lmvp.abs`, then 1,000 non-overlapping
probes from `lmvp.bin`, retain the 2,000 highest-variance probes by tissue
in the `ltxcg` list.
```{r}
cgfilt <- cgdf$type == "non-specific"
cgdff <- cgdf[!cgfilt,]
ltxcg <- list()
for(t in tv){
cgtx <- c()
cgabs <- lmvp.abs[[t]]
cgbin <- lmvp.bin[[t]]
st <- as.data.frame(lcg.ss[[t]])
# get t tissue specific probes
filtbt <- rownames(st) %in% cgdff[,1]
st <- st[filtbt,]
# get top 1k t tissue specific abs probes
filt.bf1 <- rownames(st) %in% cgabs
sf1 <- st[filt.bf1,]
sf1 <- sf1[rev(order(sf1$var)),]
cgtx <- rownames(sf1)[1:1000]
# get top 1k t tissue specific bin probes, after filt
filt.bf2 <- rownames(st) %in% cgbin &
!rownames(st) %in% rownames(sf1)
sf2 <- st[filt.bf2,]
sf2 <- sf2[rev(order(sf2$var)),]
cgtx <- c(cgtx, rownames(sf2)[1:1000])
ltxcg[[t]] <- cgtx
}
```
## Violin plots and heatmaps of probe set DNAm means and variances
First, get probe set DNAm summaries and annotation data.
```{r}
# filtered cg summaries
lfcg <- lapply(lcg.ss,
function(x){x <- x[rownames(x) %in% unique(unlist(ltxcg)),]})
# annotation subset
anno <- getAnnotation(gr) # save anno for cga
anno <- anno[,c("Name", "UCSC_RefGene_Name", "UCSC_RefGene_Group",
"Relation_to_Island")]
anno <- anno[rownames(anno) %in% unique(unlist(ltxcg)),]
# filtered beta values
lcgssf <- list()
for(t in tv){
bv <- lcg.ss[[t]]
bvf <- bv[rownames(bv) %in% ltxcg[[t]],]
lcgssf[[t]] <- bvf
}
```
Use the `makevp()` helper function to make violin plots with horizontal bars at
distribution medians. This function formats the data and calls `geom_violin`
to make violin plots for DNAm fraction means and variances. Store plots in
the `lvp` list, then display them vertically using the `grid.arrange` function
from the gridExtra package.
```{r, eval = TRUE, fig.width = 3.8, fig.height = 4.5}
lvp <- makevp(lfcg, ltxcg)
grid.arrange(lvp[[1]], lvp[[2]], ncol = 1, bottom = "Tissue")
```
Tabulate the means of probe set statistics by tissue.
```{r, eval = TRUE}
tcgss <- matrix(nrow = 0, ncol = 6)
for(t in tv){
datt <- apply(lcgssf[[t]], 2, function(x){
round(mean(x), digits = 2)
})
mt <- matrix(datt, nrow = 1)
tcgss <- rbind(tcgss, mt)
}
colnames(tcgss) <- colnames(lcgssf$adipose)
rownames(tcgss) <- tv
knitr::kable(t(tcgss), align = "c")
```
Next, prepare genome region heatmaps with 3 helper functions. These will
tabulate probe abundances by region and use the probe Beta-value means to
calculate the mean of means (left heatmap) and variance of means (right
heatmap) by genome region type.
First, define island and gene annotation groups from the manifest using
`get_cga`. Next, get region-specific DNAm summaries with `hmsets` using a
minimum region coverage of 2. This means values are calculated for regions
with least 2 probes, and regions with less are assigned "NA" and greyed out.
Make the 2 plots objects for means and variances with `hmplots()`, which wraps
the `geom_tile` ggplot2 function. Finally, display plots horizontally with
`grid.arrange`.
```{r, eval = TRUE, fig.width = 8.2, fig.height = 4.7}
cga <- get_cga(anno)
lhmset <- hmsets(ltxcg, lfcg, cga)
lhmplots <- hmplots(lhmset$hm.mean, lhmset$hm.var, lhmset$hm.size)
grid.arrange(lhmplots$hm.mean.plot, lhmplots$hm.var.plot,
layout_matrix = matrix(c(1, 1, 1, 1, 1, 2, 2), nrow = 1),
bottom = "Tissue", left = "Annotation/Region Type")
```
Colors blue, white, and red represent low, intermediate, and high means and
variances of region-specific mean Beta-values. Cell numbers show probe region
quantities and are identical for both plots.
# Conclusions
This vignette described cross-study analyses using data objects accessible
with `recountmethylation` and appearing in the manuscript @maden_human_2020.
See the manuscript for more information about samples, quality metric signal
patterns, and extended variability analyses. For details about data objects,
consult the package `users_guide` vignette. Full code and helper function
definitions are contained in the `data_analyses.R` companion script. For
additional utilities to analyze DNAm data, consult the `minfi` and
`wateRmelon` packages.
# Session info
```{r get_sessioninfo, eval = TRUE}
sessionInfo()
```
# Works Cited