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.
Arguments
- diff
Output from
diffSCEs
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)...
#> Sparsifying assays...
#> Automatically converting to sparse matrices, threshold = 0.95
#> Skipping 'BatchCellMeans': estimated sparse size 1.5 * dense matrix
#> Skipping 'BaseCellMeans': estimated sparse size 1.5 * dense matrix
#> Skipping 'BCV': estimated sparse size 1.5 * dense matrix
#> Skipping 'CellMeans': estimated sparse size 1.5 * dense matrix
#> Skipping 'TrueCounts': estimated sparse size 2.6 * dense matrix
#> Skipping 'counts': estimated sparse size 2.6 * dense matrix
#> Done!
sim2 <- simpleSimulate(nGenes = 1000, nCells = 20)
#> Simulating means...
#> Simulating counts...
#> Creating final dataset...
#> Sparsifying assays...
#> Automatically converting to sparse matrices, threshold = 0.95
#> Converting 'counts' to sparse matrix: estimated sparse size 0.66 * dense matrix
difference <- diffSCEs(list(Splat = sim1, Simple = sim2), ref = "Simple")
summary <- summariseDiff(difference)
head(summary)
#> Dataset Statistic MAD MADScaled MADRank MAE MAEScaled
#> 1 Splat Mean 2.448493 NaN 1 2.579284 NaN
#> 2 Splat Variance 11.236758 NaN 1 10.195970 NaN
#> 3 Splat ZerosGene 30.000000 NaN 1 38.215000 NaN
#> 4 Splat MeanVar 10.731328 NaN 1 11.902256 NaN
#> 5 Splat MeanZeros 40.000000 NaN 1 42.945000 NaN
#> 6 Splat LibSize 53743.500000 NaN 1 59605.700000 NaN
#> MAERank RMSE RMSEScaled RMSERank KS KSPVal KSRank
#> 1 1 3.171115 NaN 1 0.362 2.451003e-57 1
#> 2 1 12.821435 NaN 1 0.578 1.622528e-145 1
#> 3 1 43.656328 NaN 1 0.585 4.727167e-149 1
#> 4 1 14.930377 NaN 1 NA NA NA
#> 5 1 53.004009 NaN 1 NA NA NA
#> 6 1 60958.486080 NaN 1 1.000 1.450889e-11 1