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 2.850369 NaN 1 2.68786 NaN #> 2 Splat Variance 10.925370 NaN 1 10.16677 NaN #> 3 Splat ZerosGene 35.000000 NaN 1 40.95000 NaN #> 4 Splat MeanVar 10.337395 NaN 1 11.82182 NaN #> 5 Splat MeanZeros 45.000000 NaN 1 43.48500 NaN #> 6 Splat LibSize 63360.000000 NaN 1 63145.30000 NaN #> MAERank RMSE RMSEScaled RMSERank #> 1 1 3.24744 NaN 1 #> 2 1 13.01313 NaN 1 #> 3 1 45.30232 NaN 1 #> 4 1 15.00130 NaN 1 #> 5 1 53.57635 NaN 1 #> 6 1 64157.28039 NaN 1