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 2.402442 NaN 1 2.481297 NaN #> 2 Splat Variance 11.598939 NaN 1 10.337802 NaN #> 3 Splat ZerosGene 35.000000 NaN 1 41.345000 NaN #> 4 Splat MeanVar 11.259905 NaN 1 12.078602 NaN #> 5 Splat MeanZeros 40.000000 NaN 1 41.945000 NaN #> 6 Splat LibSize 59020.500000 NaN 1 59926.850000 NaN #> MAERank RMSE RMSEScaled RMSERank #> 1 1 3.03372 NaN 1 #> 2 1 13.03590 NaN 1 #> 3 1 45.66043 NaN 1 #> 4 1 15.08035 NaN 1 #> 5 1 51.61323 NaN 1 #> 6 1 61634.63305 NaN 1