Description Usage Arguments Value References Examples
Function to preprocess SingleCellExperiment object (1) to only keep genes with a certain number of nonzero entries, and (2) optionally apply a normalization procedure.
1 2  preprocess(SCdat, condition = "condition", zero.thresh = 0.9,
scran_norm = FALSE, median_norm = FALSE)

SCdat 
An object of class 
condition 
A character object that contains the name of the column in

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 
median_norm 
Logical indicating whether or not to normalize the data
using Median Normalization from 
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 normcountsorig
.
Korthauer KD, Chu LF, Newton MA, Li Y, Thomson J, Stewart R, Kendziorski C. A statistical approach for identifying differential distributions in singlecell RNAseq experiments. Genome Biology. 2016 Oct 25;17(1):222. https://genomebiology.biomedcentral.com/articles/10.1186/s130590161077y
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# 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)

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