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 2.617539 NaN 1 2.620761 NaN #> 2 Splat Variance 11.320287 NaN 1 10.223665 NaN #> 3 Splat ZerosGene 35.000000 NaN 1 41.665000 NaN #> 4 Splat MeanVar 10.533179 NaN 1 12.061862 NaN #> 5 Splat MeanZeros 45.000000 NaN 1 43.805000 NaN #> 6 Splat LibSize 59620.000000 NaN 1 62014.200000 NaN #> MAERank RMSE RMSEScaled RMSERank #> 1 1 3.164726 NaN 1 #> 2 1 12.986116 NaN 1 #> 3 1 45.973090 NaN 1 #> 4 1 15.097781 NaN 1 #> 5 1 53.529198 NaN 1 #> 6 1 63276.140327 NaN 1