print() shows a header (genes, pixels, samples and the test
settings), the number of genes with pValuePermuteAdj < 0.05 by the
sign of correlationCoef, how many genes stopped early or were
skipped, the median percentSimilarity, and the first rows.
summary() returns these counts (and more: the distribution of
nPermutations, the settings, the genes with deltaGridEdge,
summary(percentSimilarity)) as an object with its own
print() method; alpha sets the threshold on
pValuePermuteAdj. as.data.frame() returns the table as a
plain data frame, without the class and the attributes. Selecting rows
with [ keeps the class and the attributes (details is
subset to the selected genes); selecting columns gives a plain data
frame. The columns are described in
compareSpatial().
Usage
# S3 method for class 'STcompareResult'
print(x, n = 6L, ...)
# S3 method for class 'STcompareResult'
summary(object, alpha = 0.05, ...)
# S3 method for class 'summary.STcompareResult'
print(x, ...)
# S3 method for class 'STcompareResult'
x[i, j, ..., drop]
# S3 method for class 'STcompareResult'
as.data.frame(x, row.names = NULL, optional = FALSE, ...)Arguments
- x, object
A result of
compareSpatial().- n
The number of rows to print.
- alpha
The threshold on
pValuePermuteAdjbelow whichsummary()counts a gene as significant.- i, j, drop
Rows and columns to select, as for data frames.
- row.names, optional, ...
Passed on or ignored, as for the generics.
Value
print() returns x invisibly; summary() an
object of class "summary.STcompareResult"; as.data.frame() a
data frame.
Examples
data(simRanPatternRasts)
# two independent simulated fields, rasterized onto one grid of pixels
res <- compareSpatial(simRanPatternRasts[[1]], simRanPatternRasts[[2]],
nPermutations = 199, nThreads = 2)
#> compareSpatial: 1 gene on 275 shared pixels in 0.1 s (11 permutations in total, 2 threads)
print(res)
#> STcompareResult: 1 gene on 275 shared pixels; x vs y (assay pixelval)
#> Correlation: adaptive permutation p-values (stop at 10 exceedances, at most 199 permutations); 11 deltas from 0.01 to 0.9; rank-remapped surrogates
#> pValuePermuteAdj < 0.05: 0 genes (0 with correlationCoef > 0, 0 with correlationCoef < 0)
#> stopped early: 1; reached nPermutations: 0; skipped: 0; failed: 0
#> Similarity (|log2(y / x)| <= 1): median percentSimilarity 1 (0 genes with NA)
#>
#> gene nPixels correlationCoef pValueNaive pValuePermuteX pValuePermuteY
#> 1 275 -0.0416 0.492 0.833 0.917
#> pValuePermute pValuePermuteAdj nPermutations stop deltaStarMedianX
#> 0.909 0.909 11 exceedances 0.2
#> deltaStarMedianY deltaGridEdge percentSimilarity percentDissimilarityX
#> 0.2 FALSE 1 0
#> percentDissimilarityY numPixelInThresh t1 t2 status message
#> 0 275 8.75 8.93 ok
summary(res)
#> compareSpatial: 1 gene on 275 shared pixels (x vs y); 1 ok, 0 skipped, 0 failed
#> Correlation, pValuePermuteAdj < 0.05: 0 genes (0 with correlationCoef > 0, 0 with correlationCoef < 0)
#> stopped early: 1; reached nPermutations: 0; skipped: 0; failed: 0
#> nPermutations per gene: median 11, max 11; 11 in total
#> settings: nPermutations = 199, exceedances = 10, 11 deltas (0.01 to 0.9), rank-remapped surrogates, BH adjustment, seed 0
#> Similarity, percentSimilarity:
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 1 1 1 1 1 1
#> Run time: 0.1 s
head(as.data.frame(res))
#> gene nPixels correlationCoef pValueNaive pValuePermuteX pValuePermuteY
#> 1 1 275 -0.04158115 0.4922708 0.8333333 0.9166667
#> pValuePermute pValuePermuteAdj nPermutations stop deltaStarMedianX
#> 1 0.9090909 0.9090909 11 exceedances 0.2
#> deltaStarMedianY deltaGridEdge percentSimilarity percentDissimilarityX
#> 1 0.2 FALSE 1 0
#> percentDissimilarityY numPixelInThresh t1 t2 status message
#> 1 0 275 8.748878 8.928013 ok