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 2.08877 NaN 1 2.280154 NaN #> 2 Splat Variance 11.60810 NaN 1 10.409312 NaN #> 3 Splat ZerosGene 30.00000 NaN 1 37.835000 NaN #> 4 Splat MeanVar 11.27607 NaN 1 12.092148 NaN #> 5 Splat MeanZeros 40.00000 NaN 1 40.810000 NaN #> 6 Splat LibSize 58036.50000 NaN 1 57569.750000 NaN #> MAERank RMSE RMSEScaled RMSERank #> 1 1 2.779126 NaN 1 #> 2 1 12.936623 NaN 1 #> 3 1 42.628336 NaN 1 #> 4 1 15.034437 NaN 1 #> 5 1 50.112374 NaN 1 #> 6 1 58351.247110 NaN 1