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 2.757589 NaN 1 2.651957 NaN #> 2 Splat Variance 10.467288 NaN 1 10.272985 NaN #> 3 Splat ZerosGene 40.000000 NaN 1 44.935000 NaN #> 4 Splat MeanVar 9.682925 NaN 1 12.193247 NaN #> 5 Splat MeanZeros 45.000000 NaN 1 44.460000 NaN #> 6 Splat LibSize 56511.000000 NaN 1 59458.300000 NaN #> MAERank RMSE RMSEScaled RMSERank #> 1 1 3.182101 NaN 1 #> 2 1 13.133714 NaN 1 #> 3 1 48.428039 NaN 1 #> 4 1 15.352203 NaN 1 #> 5 1 53.898052 NaN 1 #> 6 1 61094.139540 NaN 1