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 2.578921 NaN 1 2.526951 NaN #> 2 Splat Variance 11.923672 NaN 1 10.771444 NaN #> 3 Splat ZerosGene 35.000000 NaN 1 41.210000 NaN #> 4 Splat MeanVar 11.687919 NaN 1 12.429719 NaN #> 5 Splat MeanZeros 45.000000 NaN 1 42.440000 NaN #> 6 Splat LibSize 53750.500000 NaN 1 57352.550000 NaN #> MAERank RMSE RMSEScaled RMSERank #> 1 1 3.056807 NaN 1 #> 2 1 13.346501 NaN 1 #> 3 1 45.518677 NaN 1 #> 4 1 15.389141 NaN 1 #> 5 1 51.924946 NaN 1 #> 6 1 58750.990661 NaN 1