Summarise the results of diffSCESets. Calculates the Median Absolute Deviation (MAD), Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) for the various properties and ranks them.

summariseDiff(diff)

Arguments

diff

Output from diffSCESets

Value

data.frame with MADs, MAEs, RMSEs, scaled statistics and ranks

Examples

sim1 <- splatSimulate(nGenes = 1000, batchCells = 20)
#> Getting parameters...
#> Creating simulation object...
#> Simulating library sizes...
#> Simulating gene means...
#> Simulating BCV...
#> Simulating counts..
#> Simulating dropout (if needed)...
#> Creating final SCESet...
#> Done!
sim2 <- simpleSimulate(nGenes = 1000, nCells = 20)
#> Simulating means...
#> Simulating counts...
#> Creating final SCESet...
difference <- diffSCESets(list(Splat = sim1, Simple = sim2), ref = "Simple") summary <- summariseDiff(difference) head(summary)
#> Dataset Statistic MAD MADScaled MADRank MAE MAEScaled #> 1 Splat Mean 2.680705 NaN 1 2.613714 NaN #> 2 Splat Variance 11.803596 NaN 1 10.839966 NaN #> 3 Splat ZerosGene 35.000000 NaN 1 41.705000 NaN #> 4 Splat MeanVar 11.293733 NaN 1 12.494680 NaN #> 5 Splat MeanZeros 45.000000 NaN 1 43.895000 NaN #> 6 Splat LibSize 62080.000000 NaN 1 63692.450000 NaN #> MAERank RMSE RMSEScaled RMSERank #> 1 1 3.125188 NaN 1 #> 2 1 13.638865 NaN 1 #> 3 1 45.945892 NaN 1 #> 4 1 15.583460 NaN 1 #> 5 1 53.301735 NaN 1 #> 6 1 64680.944707 NaN 1