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")
#> Note that the names of some metrics have changed, see 'Renamed metrics' in ?calculateQCMetrics. #> Old names are currently maintained for back-compatibility, but may be removed in future releases.
#> Note that the names of some metrics have changed, see 'Renamed metrics' in ?calculateQCMetrics. #> Old names are currently maintained for back-compatibility, but may be removed in future releases.
summary <- summariseDiff(difference) head(summary)
#> Dataset Statistic MAD MADScaled MADRank MAE MAEScaled #> 1 Splat Mean 2.623408 NaN 1 2.659195 NaN #> 2 Splat Variance 11.848783 NaN 1 10.505147 NaN #> 3 Splat ZerosGene 40.000000 NaN 1 44.110000 NaN #> 4 Splat MeanVar 11.400920 NaN 1 12.415584 NaN #> 5 Splat MeanZeros 45.000000 NaN 1 43.945000 NaN #> 6 Splat LibSize 55997.500000 NaN 1 56484.550000 NaN #> MAERank RMSE RMSEScaled RMSERank #> 1 1 3.21666 NaN 1 #> 2 1 13.21816 NaN 1 #> 3 1 47.71111 NaN 1 #> 4 1 15.39405 NaN 1 #> 5 1 53.33878 NaN 1 #> 6 1 57530.14610 NaN 1