Find Highly Variable Genes

Compute the sum of squares of the residuals and compare to a chisq null distribution. This gives very liberal p-values sicen the null distribution is often not satisifed with real data

Usage

findHVGs(x, fit)

Arguments

x

ResidualMatrixGLM matrix

fit

model fit from lucida()

Value

tibble storing

ID

gene identifier

sumSq

p-value that sumSq is larger than expected under the null

P.Value

p-value that sumSq is larger than expected under the null

FDR

false discovery rate

isHVG

is FDR < 0.05

Examples

library(SingleCellExperiment)
#> Loading required package: SummarizedExperiment
#> Loading required package: MatrixGenerics
#> Loading required package: matrixStats
#> 
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#>     colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse,
#>     colCounts, colCummaxs, colCummins, colCumprods, colCumsums,
#>     colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,
#>     colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
#>     colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
#>     colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
#>     colWeightedMeans, colWeightedMedians, colWeightedSds,
#>     colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
#>     rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,
#>     rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,
#>     rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,
#>     rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
#>     rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,
#>     rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
#>     rowWeightedSds, rowWeightedVars
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# Load example data
data(example_sce, package="muscat")
sce <- example_sce

# Compute library size for each cell
sce$libSize <- colSums(counts(sce))

# fit joint model on all cells
fit <- lucida(sce, ~ 1)
#> Analyze all cells jointly...
#> all 
#> 

# extract residuals as a ResidualMatrixGLM
res <- residuals(fit, sce)

# Find highly variable genes
findHVGs( res, fit)
#> # A tibble: 1,205 × 5
#>    ID         sumSq   P.Value       FDR isHVG
#>    <chr>      <dbl>     <dbl>     <dbl> <lgl>
#>  1 HES4       5033. 0         0         TRUE 
#>  2 ISG15    226595. 0         0         TRUE 
#>  3 AURKAIP1   7030. 0         0         TRUE 
#>  4 MRPL20     4081. 1.88e-225 2.05e-225 TRUE 
#>  5 SSU72      5723. 0         0         TRUE 
#>  6 RER1       7515. 0         0         TRUE 
#>  7 RPL22      8038. 0         0         TRUE 
#>  8 PARK7      9331. 0         0         TRUE 
#>  9 ENO1      18893. 0         0         TRUE 
#> 10 FBXO6      4181. 4.45e-239 4.96e-239 TRUE 
#> # ℹ 1,195 more rows