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 2.189448 NaN 1 2.413328 NaN #> 2 Splat Variance 11.093339 NaN 1 9.715559 NaN #> 3 Splat ZerosGene 30.000000 NaN 1 36.935000 NaN #> 4 Splat MeanVar 10.378331 NaN 1 11.562988 NaN #> 5 Splat MeanZeros 40.000000 NaN 1 41.485000 NaN #> 6 Splat LibSize 63648.000000 NaN 1 64685.100000 NaN #> MAERank RMSE RMSEScaled RMSERank #> 1 1 2.985198 NaN 1 #> 2 1 12.342547 NaN 1 #> 3 1 41.914496 NaN 1 #> 4 1 14.488452 NaN 1 #> 5 1 51.422028 NaN 1 #> 6 1 65468.891494 NaN 1