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 2.808554 NaN 1 2.772518 NaN #> 2 Splat Variance 11.361681 NaN 1 10.428142 NaN #> 3 Splat ZerosGene 40.000000 NaN 1 45.485000 NaN #> 4 Splat MeanVar 10.717219 NaN 1 12.528423 NaN #> 5 Splat MeanZeros 47.500000 NaN 1 45.605000 NaN #> 6 Splat LibSize 50259.000000 NaN 1 55938.050000 NaN #> MAERank RMSE RMSEScaled RMSERank #> 1 1 3.327546 NaN 1 #> 2 1 13.298290 NaN 1 #> 3 1 49.252157 NaN 1 #> 4 1 15.695765 NaN 1 #> 5 1 54.819020 NaN 1 #> 6 1 56998.524432 NaN 1