Print on screen a simple description of the content of phylter objects
Usage
# S3 method for class 'phylter'
print(x, ...)Examples
data(carnivora)
res <- phylter(carnivora, parallel = FALSE)
#>
#> Number of Genes: 125
#> Number of Species: 53
#> --------
#> Initial score: 0.86235
#> 28 new cells to remove -> New score: 0.90272 -> OK
#> 18 new cells to remove -> New score: 0.90833 -> OK
#> 16 new cells to remove -> New score: 0.91501 -> OK
#> 18 new cells to remove -> New score: 0.92561 -> OK
#> 5 new cells to remove -> New score: 0.93404 -> OK
#> 4 new cells to remove -> New score: 0.93692 -> OK
#> 2 new cells to remove -> New score: 0.93712 -> OK
#> 1 new cells to remove -> New score: 0.94392 -> OK
#> 1 new cells to remove -> New score: 0.94417 -> OK
#> 1 new cells to remove -> New score: 0.94426 -> OK
#> => No more outliers detected -> Checking for complete gene outliers
#> => No more outliers detected -> STOPPING OPTIMIZATION
#> --------
#>
#> Total number of outliers detected: 94
#> Number of complete gene outliers : 0
#> Number of complete species outliers : 0
#>
#> Gain (concordance between matrices): 8.19%
#> Loss (data filtering): 1.42%
print(res)
#> Phylter Analysis
#> List of class phylter
#>
#> Call: phylter(X = carnivora, parallel = FALSE)
#>
#> $Initial Initial matrices and values, before optimization
#> $Final Final matrices, scores, outliers, after optimization
#> $DiscardedGenes List of discarded genes (not analyzed by phylter)
#>
#>
#>
#> Tips:
#> Use summary(x) to get an overview of the results.
#> Use plot(x) to see the distribution of outliers
#> Use plot2WR(x) to compare WR matrices before and after
#> Use write.phylter(x) to write the results to an easily parsable file
#> Use plotDispersion(x) to compare Distatis projections before and after