Summarise the results of diffSCESets. 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 diffSCESets

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)...
#> Creating final SCESet...
#> Done!
sim2 <- simpleSimulate(nGenes = 1000, nCells = 20)
#> Simulating means...
#> Simulating counts...
#> Creating final SCESet...
difference <- diffSCESets(list(Splat = sim1, Simple = sim2), ref = "Simple") summary <- summariseDiff(difference) head(summary)
#> Dataset Statistic MAD MADScaled MADRank MAE MAEScaled #> 1 Splat Mean 2.565952 NaN 1 2.537638 NaN #> 2 Splat Variance 11.684223 NaN 1 10.622008 NaN #> 3 Splat ZerosGene 35.000000 NaN 1 41.320000 NaN #> 4 Splat MeanVar 11.216247 NaN 1 12.248453 NaN #> 5 Splat MeanZeros 45.000000 NaN 1 43.040000 NaN #> 6 Splat LibSize 60156.000000 NaN 1 62639.300000 NaN #> MAERank RMSE RMSEScaled RMSERank #> 1 1 3.081167 NaN 1 #> 2 1 13.288101 NaN 1 #> 3 1 45.494505 NaN 1 #> 4 1 15.266613 NaN 1 #> 5 1 52.700095 NaN 1 #> 6 1 64195.529851 NaN 1