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 2.615861 NaN 1 2.574927 NaN #> 2 Splat Variance 10.798169 NaN 1 10.057574 NaN #> 3 Splat ZerosGene 35.000000 NaN 1 41.755000 NaN #> 4 Splat MeanVar 9.744237 NaN 1 12.092071 NaN #> 5 Splat MeanZeros 45.000000 NaN 1 43.265000 NaN #> 6 Splat LibSize 61314.500000 NaN 1 63396.500000 NaN #> MAERank RMSE RMSEScaled RMSERank #> 1 1 3.137784 NaN 1 #> 2 1 12.857848 NaN 1 #> 3 1 46.153819 NaN 1 #> 4 1 15.172185 NaN 1 #> 5 1 52.764808 NaN 1 #> 6 1 65195.861842 NaN 1