Summarise the results of diffSCEs. Calculates the Median Absolute Deviation (MAD), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and Kolmogorov-Smirnov (KS) statistics for the various properties and ranks them.

summariseDiff(diff)

Arguments

diff

Output from diffSCEs

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)...
#> Done!
sim2 <- simpleSimulate(nGenes = 1000, nCells = 20)
#> Simulating means...
#> Simulating counts...
#> Creating final dataset...
difference <- diffSCEs(list(Splat = sim1, Simple = sim2), ref = "Simple") summary <- summariseDiff(difference) head(summary)
#> Dataset Statistic MAD MADScaled MADRank MAE MAEScaled #> 1 Splat Mean 2.457239 NaN 1 2.449271 NaN #> 2 Splat Variance 11.476979 NaN 1 10.248671 NaN #> 3 Splat ZerosGene 30.000000 NaN 1 38.875000 NaN #> 4 Splat MeanVar 10.941816 NaN 1 12.225792 NaN #> 5 Splat MeanZeros 45.000000 NaN 1 42.325000 NaN #> 6 Splat LibSize 54503.500000 NaN 1 55634.900000 NaN #> MAERank RMSE RMSEScaled RMSERank KS KSPVal KSRank #> 1 1 2.978541 NaN 1 0.327 0.000000e+00 1 #> 2 1 12.923752 NaN 1 0.571 0.000000e+00 1 #> 3 1 44.022437 NaN 1 0.558 0.000000e+00 1 #> 4 1 15.333907 NaN 1 NA NA NA #> 5 1 51.639375 NaN 1 NA NA NA #> 6 1 56196.302323 NaN 1 1.000 4.122307e-09 1