findFC: findFC

View source: R/Find_FC.R

findFCR Documentation



Find the appropriate Fold Change vectors for simulation that will be use in classic differential expression case.


findFC(SCdat, index, sd.range = c(1, 3), N = 4, overExpressionProb = 0.5,
  plot.FC = FALSE, condition = "condition")



An object of class SingleCellExperiment that contains normalized 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. 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, 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.


Reasonable set of genes for simulation


Numeric vector of length two which describes the interval (lower, upper) of standard deviations of fold changes to randomly select.


Integer value for the number of bins to divide range of fold changes for calculating standard deviations


Numeric value between 0 and 1 which describes the ratio of over to under expression values to sample.


Logical indicating whether or not to plot the observed and simulated log2 fold changes.


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.


This code is a modified version of Sam Younkin's simulate FC function. Major things that were changed are (1) standard deviations are calculated only on the nonzeroes, (2) the sampling of FCs is uniform on the log scale instead of the raw scale, and (3) the binning is done by quantiles instead of evenly spaced along the average expression values.


FC.vec Return Fold Change Vectors


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.

kdkorthauer/scDD documentation built on March 27, 2022, 5:11 a.m.