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 2.338299 NaN 1 2.392073 NaN #> 2 Splat Variance 11.506849 NaN 1 10.222706 NaN #> 3 Splat ZerosGene 30.000000 NaN 1 38.780000 NaN #> 4 Splat MeanVar 9.588721 NaN 1 11.820910 NaN #> 5 Splat MeanZeros 45.000000 NaN 1 42.060000 NaN #> 6 Splat LibSize 55543.500000 NaN 1 58282.850000 NaN #> MAERank RMSE RMSEScaled RMSERank #> 1 1 2.907697 NaN 1 #> 2 1 12.865170 NaN 1 #> 3 1 44.006818 NaN 1 #> 4 1 14.953873 NaN 1 #> 5 1 51.575673 NaN 1 #> 6 1 59902.661935 NaN 1