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 2.840543 NaN 1 2.850934 NaN #> 2 Splat Variance 11.680343 NaN 1 10.451830 NaN #> 3 Splat ZerosGene 40.000000 NaN 1 44.485000 NaN #> 4 Splat MeanVar 11.047790 NaN 1 12.143856 NaN #> 5 Splat MeanZeros 45.000000 NaN 1 44.780000 NaN #> 6 Splat LibSize 62717.000000 NaN 1 62238.650000 NaN #> MAERank RMSE RMSEScaled RMSERank #> 1 1 3.426694 NaN 1 #> 2 1 13.221106 NaN 1 #> 3 1 47.795136 NaN 1 #> 4 1 15.125288 NaN 1 #> 5 1 54.805109 NaN 1 #> 6 1 64042.506466 NaN 1