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 2.747262 NaN 1 2.687708 NaN #> 2 Splat Variance 12.048614 NaN 1 10.964929 NaN #> 3 Splat ZerosGene 40.000000 NaN 1 44.560000 NaN #> 4 Splat MeanVar 11.536097 NaN 1 12.817781 NaN #> 5 Splat MeanZeros 45.000000 NaN 1 44.735000 NaN #> 6 Splat LibSize 60309.000000 NaN 1 62775.150000 NaN #> MAERank RMSE RMSEScaled RMSERank #> 1 1 3.224377 NaN 1 #> 2 1 13.890753 NaN 1 #> 3 1 48.064020 NaN 1 #> 4 1 15.970356 NaN 1 #> 5 1 54.010879 NaN 1 #> 6 1 63881.558662 NaN 1