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 2.431092 NaN 1 2.419012 NaN #> 2 Splat Variance 10.156587 NaN 1 9.597323 NaN #> 3 Splat ZerosGene 30.000000 NaN 1 38.525000 NaN #> 4 Splat MeanVar 8.766075 NaN 1 11.305514 NaN #> 5 Splat MeanZeros 40.000000 NaN 1 41.730000 NaN #> 6 Splat LibSize 62089.500000 NaN 1 59054.250000 NaN #> MAERank RMSE RMSEScaled RMSERank #> 1 1 2.947972 NaN 1 #> 2 1 12.058784 NaN 1 #> 3 1 43.461190 NaN 1 #> 4 1 14.258312 NaN 1 #> 5 1 51.490776 NaN 1 #> 6 1 60072.826128 NaN 1