findNhoodMarkers.RdThis function will perform differential gene expression analysis on
differentially abundant neighbourhoods, by first aggregating adjacent and
concordantly DA neighbourhoods, then comparing cells between these
aggregated groups. For differential gene experession based on an input design
within DA neighbourhoods see testDiffExp.
| x | A |
|---|---|
| da.res | A |
| da.fdr | A numeric scalar that determines at what FDR neighbourhoods are declared DA for the purposes of aggregating across concorantly DA neighbourhoods. |
| assay | A character scalar determining which |
| aggregate.samples | logical indicating wheather the expression values for cells in the same sample
and neighbourhood group should be merged for DGE testing. This allows to perform testing exploiting the replication structure
in the experimental design, rather than treating single-cells as independent replicates. The function used for aggregation depends on the
selected gene expression assay: if |
| sample_col | a character scalar indicating the column in the colData storing sample information
(only relevant if |
| overlap | A scalar integer that determines the number of cells that must overlap between adjacent neighbourhoods for merging. |
| lfc.threshold | A scalar that determines the absolute log fold change above which neighbourhoods should be considerd 'DA' for merging. Default=NULL |
| merge.discord | A logical scalar that overrides the default behaviour and allows adjacent neighbourhoods to be merged if they have discordant log fold change signs. Using this argument is generally discouraged, but may be useful for constructing an empirical null group of cells, regardless of DA sign. |
| subset.row | A logical, integer or character vector indicating the rows
of |
| gene.offset | A logical scalar the determines whether a per-cell offset is provided in the DGE GLM to adjust for the number of detected genes with expression > 0. |
| return.groups | A logical scalar that returns a |
| subset.nhoods | A logical, integer or character vector indicating which neighbourhoods to subset before aggregation and DGE testing. |
| na.function | A valid NA action function to apply, should be one of
|
| compute.new | A logical scalar indicating whether to force computing a new neighbourhood adjacency matrix if already present. |
A data.frame of DGE results containing a log fold change and adjusted
p-value for each aggregated group of neighbourhoods. If return.groups then
the return value is a list with the slots groups and dge containing the
aggregated neighbourhood groups per single-cell and marker gene results, respectively.
Warning: If all neighbourhoods are grouped together, then it is impossible to
run findNhoodMarkers. In this (hopefully rare) instance, this function will spit
out a warning and return NULL.
Adjacent neighbourhoods are first merged based on two criteria: 1) they share at
least overlap number of cells, and 2) the DA log fold change sign is concordant.
This behaviour can be modulated by setting overlap to be more or less stringent.
Additionally, a threshold on the log fold-changes can be set, such that lfc.threshold
is required to merge adjacent neighbourhoods. Note: adjacent neighbourhoods will never be
merged with opposite signs.
Using a one vs. all approach, each aggregated group of cells is compared to all others
using the single-cell log normalized gene expression with a GLM
(for details see limma-package), or the single-cell counts using a
negative binomial GLM (for details see edgeR-package). When using
the latter it is recommended to set gene.offset=TRUE as this behaviour adjusts
the model offsets by the number of detected genes in each cell.
library(SingleCellExperiment) ux.1 <- matrix(rpois(12000, 5), ncol=400) ux.2 <- matrix(rpois(12000, 4), ncol=400) ux <- rbind(ux.1, ux.2) vx <- log2(ux + 1) pca <- prcomp(t(vx)) sce <- SingleCellExperiment(assays=list(counts=ux, logcounts=vx), reducedDims=SimpleList(PCA=pca$x)) colnames(sce) <- paste0("Cell", 1:ncol(sce)) milo <- Milo(sce) milo <- buildGraph(milo, k=20, d=10, transposed=TRUE)#>#>milo <- calcNhoodDistance(milo, d=10) cond <- rep("A", ncol(milo)) cond.a <- sample(1:ncol(milo), size=floor(ncol(milo)*0.25)) cond.b <- setdiff(1:ncol(milo), cond.a) cond[cond.b] <- "B" meta.df <- data.frame(Condition=cond, Replicate=c(rep("R1", 132), rep("R2", 132), rep("R3", 136))) meta.df$SampID <- paste(meta.df$Condition, meta.df$Replicate, sep="_") milo <- countCells(milo, meta.data=meta.df, samples="SampID")#>#>#>test.meta <- data.frame("Condition"=c(rep("A", 3), rep("B", 3)), "Replicate"=rep(c("R1", "R2", "R3"), 2)) test.meta$Sample <- paste(test.meta$Condition, test.meta$Replicate, sep="_") rownames(test.meta) <- test.meta$Sample da.res <- testNhoods(milo, design=~0 + Condition, design.df=test.meta[colnames(nhoodCounts(milo)), ])#>nhood.dge <- findNhoodMarkers(milo, da.res, overlap=1, compute.new=TRUE)#>#>#>#> Warning: All graph neighbourhoods are in the same group - cannot perform DGE testing. Returning NULLnhood.dge#> NULL