450k-demo.rmd

```{r 450k-demo-init, echo=FALSE, message=F}
library(knitr)
library(Cairo)
opts_chunk$set(warning=FALSE, fig.width=6, fig.height=6, dev="CairoPNG", stop=TRUE)

library(GEOquery)

```

# Normalize a 450k dataset

## Download example data set 

```{r child = 'dataset-450k-demo.rmd'}
```

```{r}
path <- download.450k.demo.dataset()
```

## Normalize dataset 

Create samplesheet
```{r}
library(meffil)

samplesheet <- meffil.create.samplesheet(path)

options(mc.cores=3)
```

Parameters.
```{r}
qc.file <- "450k-demo/qc-report.html"
author <- "Prickett, et al."
study <- "Silver-Russell syndrome patients (GEO:GSE55491)"
norm.file <- "450k-demo/normalization-report.html"
cell.type.reference <- "blood gse35069"
```

Generate quality control objects.
```{r 450k-demo-qc, cache=T}
qc.objects <- meffil.qc(samplesheet, cell.type.reference=cell.type.reference, verbose=T)
```

QC report.
```{r}
qc.summary <- meffil.qc.summary(qc.objects, verbose=T)
meffil.qc.report(qc.summary,
                 output.file=qc.file,
                 author=author,
                 study=study)
```

Remove any low quality samples.
```{r}
if (nrow(qc.summary$bad.samples) > 0)
    qc.objects <- meffil.remove.samples(qc.objects, qc.summary$bad.samples$sample.name)
```

Check how many principal components to include.
```{r, dev="CairoPNG"}
print(meffil.plot.pc.fit(qc.objects, n.cross=3)$plot)
```

Normalize dataset.
```{r, 450k-demo-norm, cache=T}
number.pcs <- 2
norm.objects <- meffil.normalize.quantiles(qc.objects, number.pcs=number.pcs, verbose=T)
norm.dataset <- meffil.normalize.samples(norm.objects,
                                        just.beta=F, 
                                        cpglist.remove=qc.summary$bad.cpgs$name,
                                        verbose=T)
```

Generate normalization report.
```{r}
beta <- meffil.get.beta(norm.dataset$M, norm.dataset$U)
pcs <- meffil.methylation.pcs(beta, sites=meffil.get.autosomal.sites("450k"), verbose=T)

parameters <- meffil.normalization.parameters(norm.objects)
parameters$batch.threshold <- 0.01
norm.summary <- meffil.normalization.summary(norm.objects=norm.objects,
                                             pcs=pcs,
                                             parameters=parameters, verbose=T)
meffil.normalization.report(norm.summary,
                            output.file=norm.file,
                            author=author,
                            study=study)
```

Just for fun, compare cell count estimates
obtained from raw data to estimates obtained from normalized beta matrix
(i.e. normalized methylation levels).
```{r}
counts.meffil <- t(meffil.cell.count.estimates(norm.objects))

counts.beta <- meffil.estimate.cell.counts.from.betas(beta, cell.type.reference)

for (cell.type in colnames(counts.meffil)) {
    cat(cell.type, cor(counts.meffil[,cell.type], counts.beta[,cell.type]), "\n")
}
```


Generate counts for saliva and see how they compare.
```{r}
counts.saliva <- mclapply(qc.objects, meffil.estimate.cell.counts, cell.type.reference="saliva gse48472", verbose=T)

counts.saliva <- t(sapply(counts.saliva, function(x) x$counts))

diag(cor(counts.saliva, counts.meffil)[colnames(counts.meffil), colnames(counts.meffil)])
quantile(counts.saliva[,"Buccal"])
cor(counts.saliva, counts.meffil)["Buccal",]
cor(counts.saliva)["Buccal",]
```