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 2.434959 NaN 1 2.372109 NaN #> 2 Splat Variance 11.529400 NaN 1 10.235263 NaN #> 3 Splat ZerosGene 30.000000 NaN 1 37.590000 NaN #> 4 Splat MeanVar 10.907122 NaN 1 12.107674 NaN #> 5 Splat MeanZeros 45.000000 NaN 1 41.375000 NaN #> 6 Splat LibSize 62644.500000 NaN 1 61051.350000 NaN #> MAERank RMSE RMSEScaled RMSERank #> 1 1 2.909392 NaN 1 #> 2 1 12.895348 NaN 1 #> 3 1 42.376880 NaN 1 #> 4 1 15.083543 NaN 1 #> 5 1 50.717600 NaN 1 #> 6 1 61731.822271 NaN 1