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 2.530516 NaN 1 2.50054 NaN #> 2 Splat Variance 11.620776 NaN 1 10.56835 NaN #> 3 Splat ZerosGene 35.000000 NaN 1 42.13500 NaN #> 4 Splat MeanVar 11.192251 NaN 1 12.16095 NaN #> 5 Splat MeanZeros 45.000000 NaN 1 42.99500 NaN #> 6 Splat LibSize 59471.500000 NaN 1 58817.15000 NaN #> MAERank RMSE RMSEScaled RMSERank KS KSPVal KSRank #> 1 1 3.059393 NaN 1 0.347 0.000000e+00 1 #> 2 1 13.148823 NaN 1 0.607 0.000000e+00 1 #> 3 1 46.456162 NaN 1 0.589 0.000000e+00 1 #> 4 1 15.175921 NaN 1 NA NA NA #> 5 1 52.428284 NaN 1 NA NA NA #> 6 1 60152.394407 NaN 1 1.000 4.122307e-09 1