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 2.543821 NaN 1 2.490035 NaN #> 2 Splat Variance 10.495774 NaN 1 9.766863 NaN #> 3 Splat ZerosGene 35.000000 NaN 1 41.330000 NaN #> 4 Splat MeanVar 8.419913 NaN 1 11.469025 NaN #> 5 Splat MeanZeros 40.000000 NaN 1 41.830000 NaN #> 6 Splat LibSize 57521.500000 NaN 1 59002.500000 NaN #> MAERank RMSE RMSEScaled RMSERank #> 1 1 3.004655 NaN 1 #> 2 1 12.483217 NaN 1 #> 3 1 45.764615 NaN 1 #> 4 1 14.691833 NaN 1 #> 5 1 51.732968 NaN 1 #> 6 1 60005.941034 NaN 1