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 2.288933 NaN 1 2.375445 NaN #> 2 Splat Variance 11.025042 NaN 1 9.969259 NaN #> 3 Splat ZerosGene 30.000000 NaN 1 40.500000 NaN #> 4 Splat MeanVar 9.509015 NaN 1 11.819818 NaN #> 5 Splat MeanZeros 45.000000 NaN 1 42.780000 NaN #> 6 Splat LibSize 59367.000000 NaN 1 59618.300000 NaN #> MAERank RMSE RMSEScaled RMSERank KS KSPVal KSRank #> 1 1 2.85907 NaN 1 0.334 0.000000e+00 1 #> 2 1 12.57383 NaN 1 0.573 0.000000e+00 1 #> 3 1 45.84158 NaN 1 0.542 0.000000e+00 1 #> 4 1 14.92356 NaN 1 NA NA NA #> 5 1 51.94324 NaN 1 NA NA NA #> 6 1 61010.54295 NaN 1 1.000 1.450928e-11 1