Summarise diffSCEs

Summarise the results of diffSCEs. Calculates the Median Absolute Deviation (MAD), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and Kolmogorov-Smirnov (KS) statistics 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")
summary <- summariseDiff(difference)
head(summary)
#>   Dataset Statistic          MAD MADScaled MADRank          MAE MAEScaled
#> 1   Splat      Mean     2.684928       NaN       1     2.662249       NaN
#> 2   Splat  Variance    11.775483       NaN       1    10.501502       NaN
#> 3   Splat ZerosGene    35.000000       NaN       1    42.470000       NaN
#> 4   Splat   MeanVar    11.163636       NaN       1    12.427581       NaN
#> 5   Splat MeanZeros    45.000000       NaN       1    44.355000       NaN
#> 6   Splat   LibSize 59206.000000       NaN       1 57475.650000       NaN
#>   MAERank         RMSE RMSEScaled RMSERank    KS       KSPVal KSRank
#> 1       1     3.217561        NaN        1 0.365 0.000000e+00      1
#> 2       1    13.183124        NaN        1 0.580 0.000000e+00      1
#> 3       1    46.870566        NaN        1 0.580 0.000000e+00      1
#> 4       1    15.548901        NaN        1    NA           NA     NA
#> 5       1    53.599207        NaN        1    NA           NA     NA
#> 6       1 58418.529116        NaN        1 1.000 1.450928e-11      1