plotCountDepth: Evaluate the count-depth relationship before (or after)...
In SCnorm: Normalization of single cell RNA-seq data

Description Usage Arguments Value Author(s) Examples

Quantile regression is used to estimate the dependence of read counts on sequencing depth for every gene. If multiple conditions are provided, a separate plot is provided for each and the filters are applied within each condition separately. The plot can be used to evaluate the extent of the count-depth relationship in the dataset or can be be used to evaluate data normalized by alternative methods.

plotCountDepth(
  Data,
  NormalizedData = NULL,
  Conditions = NULL,
  Tau = 0.5,
  FilterCellProportion = 0.1,
  FilterExpression = 0,
  NumExpressionGroups = 10,
  NCores = NULL,
  ditherCounts = FALSE
)

`Data`	can be a matrix of single-cell expression with cells where rows are genes and columns are samples. Gene names should not be a column in this matrix, but should be assigned to rownames(Data). Data can also be an object of class `SummarizedExperiment` that contains the single-cell expression matrix and other metadata. The `assays` slot contains the expression matrix and is named `"Counts"`. This matrix 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 in the same order as the columns of `NormCounts`). Additional information about the experiment can be contained in the `metadata` slot as a list.
`NormalizedData`	matrix of normalized expression counts. Rows are genesand columns are samples. Only input this if evaluating already normalized data.
`Conditions`	vector of condition labels, this should correspond to the columns of the un-normalized expression matrix. If not provided data is assumed to come from same condition/batch.
`Tau`	value of quantile for the quantile regression used to estimate gene-specific slopes (default is Tau = .5 (median)).
`FilterCellProportion`	the proportion of non-zero expression estimates required to include the genes into the evaluation. Default is .10, and will not go below a proportion which uses less than 10 total cells/samples.
`FilterExpression`	exclude genes having median of non-zero expression below this threshold from count-depth plots (default = 0).
`NumExpressionGroups`	the number of groups to split the data into, genes are split into equally sized groups based on their non-zero median expression.
`NCores`	number of cores to use, default is detectCores() - 1. This will be used to set up a parallel environment using either MulticoreParam (Linux, Mac) or SnowParam (Windows) with NCores using the package BiocParallel.
`ditherCounts`	whether to dither/jitter the counts, may be used for data with many ties, default is FALSE.

returns a data.frame containing each gene's slope (count-depth relationship) and its associated expression group. A plot will be output.

Rhonda Bacher

 
data(ExampleSimSCData)
Conditions = rep(c(1,2), each= 90) 
#plotCountDepth(Data = ExampleSimSCData, Conditions = Conditions, 
  #FilterCellProportion = .1)