ewce_expression_data {EWCE} | R Documentation |
ewce_expression_data
takes a differential expression table and
determines the probability of cell-type enrichment in the up & down
regulated genes
ewce_expression_data( sct_data, annotLevel = 1, tt, sortBy = "t", thresh = 250, reps = 100, ttSpecies = "mouse", sctSpecies = "mouse" )
sct_data |
List generated using |
annotLevel |
an integer indicating which level of the annotation to analyse. Default = 1. |
tt |
Differential expression table. Can be output of limma::topTable function. Minimum requirement is that one column stores a metric of increased/decreased expression (i.e. log fold change, t-statistic for differential expression etc) and another contains either HGNC or MGI symbols. |
sortBy |
Column name of metric in tt which should be used to sort up- from down- regulated genes. Default="t" |
thresh |
The number of up- and down- regulated genes to be included in each analysis. Default=250 |
reps |
Number of random gene lists to generate (default=100 but should be over 10000 for publication quality results) |
ttSpecies |
Either 'mouse' or 'human' depending on which species the differential expression table was generated from |
sctSpecies |
Either 'mouse' or 'human' depending on which species the single cell data was generated from |
A list containing five data frames:
results
: dataframe in which each row gives the statistics
(p-value, fold change and number of standard deviations from the mean)
associated with the enrichment of the stated cell type in the gene list.
An additional column *Direction* stores whether it the result is from the
up or downregulated set.
hit.cells.up
: vector containing the summed proportion of
expression in each cell type for the target list
hit.cells.down
: vector containing the summed proportion of
expression in each cell type for the target list#'
bootstrap_data.up
: matrix in which each row represents the
summed proportion of expression in each cell type for one of the random
lists
bootstrap_data.down
: matrix in which each row represents the
summed proportion of expression in each cell type for one of the random
lists
library(ewceData) # Load the single cell data ctd <- ctd() # Set the parameters for the analysis # Use 3 bootstrap lists for speed, for publishable analysis use >10000 reps <- 3 # Use 5 up/down regulated genes (thresh) for speed, default is 250 thresh = 5 annotLevel <- 1 # <- Use cell level annotations (i.e. Interneurons) # Load the top table tt_alzh <- tt_alzh() tt_results <- ewce_expression_data( sct_data = ctd, tt = tt_alzh, annotLevel = 1, thresh = thresh, reps = reps, ttSpecies = "human", sctSpecies = "mouse" )