importDittoBulk {dittoSeq}R Documentation

import bulk sequencing data into a format that dittoSeq functions expect.

Description

import bulk sequencing data into a format that dittoSeq functions expect.

Usage

importDittoBulk(x, ...)

## S4 method for signature 'SummarizedExperiment'
importDittoBulk(x, reductions = NULL, metadata = NULL, combine_metadata = TRUE)

## S4 method for signature 'DGEList'
importDittoBulk(x, reductions = NULL, metadata = NULL, combine_metadata = TRUE)

## S4 method for signature 'list'
importDittoBulk(x, reductions = NULL, metadata = NULL)

Arguments

x

a DGEList, or SummarizedExperiment (includes DESeqDataSet) class object containing the sequencing data to be imported

...

For the generic, additional arguments passed to specific methods.

reductions

a named list of dimensionality reduction embeddings matrices. names will become the names of the dimensionality reductions and how each will be used with the reduction.use input of dittoDimPlot rows of the matrices should represent the different cells/samples of the dataset, and columns the different dimensions

metadata

a data.frame like object containing columns of extra information about the cells/samples (rows). The names of these columns can then be used to tretrieve and plot such data in any dittoSeq visualizations.

combine_metadata

Logical which sets whether original colData (DESeqDataSet/SummarizedExperiment) or $samples (DGEList) from x should be retained.

Value

A SingleCellExperiment object containing all assays (DESeqDataSet or SummarizeedExperiment) or all common slots (DGEList) of the input x, as well as any dimensionality reductions provided to reductions, and any provided metadata stored in colData.

When combine_metadata is set to FALSE, metadata inside x (colData or $samples) is ignored entirely. When combine_metadata is TRUE (the default), metadata inside x is combined with what is provided to the metadata input; but names must be unique, so when there are similarly named slots, the values provided to the metadata input are used.

Note

One recommended assay to create if it is not already present in your dataset, is a log-normalized version of the counts data. The logNormCounts function of the scater package is an easy way to make such a slot. dittoSeq defaults to grabbing expression data from an assay named logcounts > normcounts > counts

See Also

SingleCellExperiment for more information about this storage system.

Examples

## Bulk data is stored as a SingleCellExperiment
library(SingleCellExperiment)

# Generate some random data
nsamples <- 60
exp <- matrix(rpois(1000*nsamples, 20), ncol=nsamples)
colnames(exp) <- paste0("sample", seq_len(ncol(exp)))
rownames(exp) <- paste0("gene", seq_len(nrow(exp)))
logexp <- log2(exp + 1)

# Dimensionality Reductions
pca <- matrix(runif(nsamples*5,-2,2), nsamples)
tsne <- matrix(rnorm(nsamples*2), nsamples)

# Some Metadata
conds <- factor(rep(c("condition1", "condition2"), each=nsamples/2))
timept <- rep(c("d0", "d3", "d6", "d9"), each = 15)
genome <- rep(c(rep(TRUE,7),rep(FALSE,8)), 4)
grps <- sample(c("A","B","C","D"), nsamples, TRUE)
clusts <- as.character(1*(tsne[,1]>0&tsne[,2]>0) +
                       2*(tsne[,1]<0&tsne[,2]>0) +
                       3*(tsne[,1]>0&tsne[,2]<0) +
                       4*(tsne[,1]<0&tsne[,2]<0))

### We can import the counts directly, or as a SummarizedExperiment
myRNA <- importDittoBulk(
    x = list(counts = exp,
         logcounts = logexp))

### Adding metadata & PCA or other dimensionality reductions
# We can add these directly during import, or after.
myRNA <- importDittoBulk(
    x = list(counts = exp,
        logcounts = logexp),
    metadata = data.frame(
        conditions = conds,
        timepoint = timept,
        SNP = genome,
        groups = grps),
    reductions = list(
        pca = pca))

myRNA$clustering <- clusts

myRNA <- addDimReduction(
    myRNA,
    embeddings = tsne,
    name = "tsne")

# (other packages SCE manipulations can also be used)

### When we import from SummarizedExperiment, all metadata is retained.
# The object is just 'upgraded' to hold extra slots.
# The input is the same, aside from a message when metadata are replaced.
se <- SummarizedExperiment(
    list(counts = exp, logcounts = logexp))
myRNA <- importDittoBulk(
    x = se,
    metadata = data.frame(
        conditions = conds,
        timepoint = timept,
        SNP = genome,
        groups = grps,
        clustering = clusts),
    reductions = list(
        pca = pca,
        tsne = tsne))
myRNA

### For DESeq2, how we might have made this:
# DESeqDataSets are SummarizedExperiments, and behave similarly
# library(DESeq2)
# dds <- DESeqDataSetFromMatrix(
#     exp, data.frame(conditions), ~ conditions)
# dds <- DESeq(dds)
# dds_ditto <- importDittoBulk(dds)

### For edgeR, DGELists are a separate beast.
# dittoSeq imports what I know to commonly be inside them, but please submit
# an issue on the github (dtm2451/dittoSeq) if more should be retained.
# library(edgeR)
# dgelist <- DGEList(counts=exp, group=conditions)
# dge_ditto <- importDittoBulk(dgelist)
[Package dittoSeq version 1.0.2 Index]