preprocess {scDD} | R Documentation

## preprocess

### Description

Function to preprocess SingleCellExperiment object (1) to only keep genes with a certain number of nonzero entries, and (2) optionally apply a normalization procedure.

### Usage

    preprocess(SCdat, condition = "condition", zero.thresh = 0.9,
      scran_norm = FALSE, median_norm = FALSE)

### Arguments

`SCdat` | An object of class `SingleCellExperiment` that contains single-cell expression and metadata. The `assays` slot contains a named list of matrices, where the normalized counts are housed in the one named `normcounts`, and unnormalized counts are stored in the one names `counts`. If either `scran_norm` or `median_norm` is set to TRUE, the `normcounts` slot will be created from the `counts` slot. The counts and normalized counts matrices should have one row for each gene and one sample for each column. The `colData` slot should contain a data.frame with one row per sample and columns that contain metadata for each sample. This data.frame should contain a variable that represents biological condition, which is in the form of numeric values (either 1 or 2) that indicates which condition each sample belongs to (in the same order as the columns of `normcounts`). Optional additional metadata about each cell can also be contained in this data.frame, and additional information about the experiment can be contained in the `metadata` slot as a list.
`condition` | A character object that contains the name of the column in `colData` that represents the biological group or condition of interest (e.g. treatment versus control). Note that this variable should only contain two possible values since `scDD` can currently only handle two-group comparisons. The default option assumes that there is a column named "condition" that contains this variable.
`zero.thresh` | A numeric value between 0 and 1 that represents the maximum proportion of zeroes per gene allowable in the processed dataset
`scran_norm` | Logical indicating whether or not to normalize the data using scran Normalization from `scran`
`median_norm` | Logical indicating whether or not to normalize the data using Median Normalization from `EBSeq`

### Value

An object of class `SingleCellExperiment` with genes removed if they have more than `zero.thresh` zeroes, and the `normcounts` assay added if either `scran_norm` or `median_norm` is set to TRUE and only `counts` is provided. If `normcounts` already exists and either `scran_norm` or `median_norm` is set to TRUE, then the new normalized counts are placed in the `normcounts` assay slot, and the original values are moved to a new slot called `normcounts-orig`.

### References

Korthauer KD, Chu LF, Newton MA, Li Y, Thomson J, Stewart R, Kendziorski C. A statistical approach for identifying differential distributions in single-cell RNA-seq experiments. Genome Biology. 2016 Oct 25;17(1):222. https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-1077-y

### Examples

     # load toy example SingleCellExperiment object

     data(scDatEx)

     # apply the preprocess function to filter out genes if they have more than
     # 75% zero

     scDatEx <- preprocess(scDatEx, zero.thresh=0.75)

     # apply the preprocess function again, but this time threshold on the
     # proportion of zeroes and apply scran normalization
     # set the zero.thresh argument to 0.75 so that genes with more than 75%
     # zeroes are filtered out
     # set the scran_norm argument to TRUE to return scran normalized counts

     scDatEx.scran <- preprocess(scDatEx, zero.thresh=0.75, scran_norm=TRUE)

     # set the median_norm argument to TRUE to return Median normalized counts

     scDatEx.median <- preprocess(scDatEx, zero.thresh=0.75, median_norm=TRUE)

[Package scDD version 1.18.0 Index]