Summarise the results of diffSCEs. 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 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 2.526815 NaN 1 2.445861 NaN #> 2 Splat Variance 11.390815 NaN 1 10.045729 NaN #> 3 Splat ZerosGene 35.000000 NaN 1 41.305000 NaN #> 4 Splat MeanVar 10.687854 NaN 1 11.962774 NaN #> 5 Splat MeanZeros 40.000000 NaN 1 41.495000 NaN #> 6 Splat LibSize 55882.500000 NaN 1 58030.650000 NaN #> MAERank RMSE RMSEScaled RMSERank #> 1 1 2.986489 NaN 1 #> 2 1 12.686655 NaN 1 #> 3 1 45.644003 NaN 1 #> 4 1 14.905983 NaN 1 #> 5 1 51.031118 NaN 1 #> 6 1 59276.438841 NaN 1