Skip to contents

Print on screen a simple description of the content of phylter objects

Usage

# S3 method for class 'phylter'
print(x, ...)

Arguments

x

Object returned by function 'phylter()'.

...

Additional arguments.

Value

Print formatting

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