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 2.67384 NaN 1 2.710879 NaN #> 2 Splat Variance 11.83125 NaN 1 10.836162 NaN #> 3 Splat ZerosGene 35.00000 NaN 1 40.860000 NaN #> 4 Splat MeanVar 11.35193 NaN 1 12.464230 NaN #> 5 Splat MeanZeros 45.00000 NaN 1 43.990000 NaN #> 6 Splat LibSize 54007.50000 NaN 1 57638.350000 NaN #> MAERank RMSE RMSEScaled RMSERank #> 1 1 3.287816 NaN 1 #> 2 1 13.638593 NaN 1 #> 3 1 45.098780 NaN 1 #> 4 1 15.539611 NaN 1 #> 5 1 53.837719 NaN 1 #> 6 1 58755.898060 NaN 1