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 2.288457 NaN 1 2.361992 NaN #> 2 Splat Variance 9.864920 NaN 1 9.885907 NaN #> 3 Splat ZerosGene 30.000000 NaN 1 38.245000 NaN #> 4 Splat MeanVar 9.311683 NaN 1 11.773079 NaN #> 5 Splat MeanZeros 40.000000 NaN 1 41.455000 NaN #> 6 Splat LibSize 59924.000000 NaN 1 60889.500000 NaN #> MAERank RMSE RMSEScaled RMSERank #> 1 1 2.911241 NaN 1 #> 2 1 12.576504 NaN 1 #> 3 1 43.466366 NaN 1 #> 4 1 14.872900 NaN 1 #> 5 1 51.016909 NaN 1 #> 6 1 62360.840953 NaN 1