R/SparrowResult-methods.R
results.RdThe resultNames, result, and results functions enable
you to explore the results of the analysis run with seas().
The results that are stored within a SparrowResult object have a
more or less 1:1 mapping with the values passed as methods, parameter
of the seas() call.
Generates a table to indicate the number of genesets per collection that
pass a given FDR. The table provides separate groups of rows for each of
the methods run in the seas() call that generated that
generated x.
resultNames(x)
result(x, ...)
# S3 method for class 'SparrowResult'
result(
x,
name = NULL,
stats.only = FALSE,
rank.by = c("pval", "t", "logFC"),
add.suffix = FALSE,
as.dt = FALSE,
...
)
results(
x,
names = resultNames(x),
stats.only = TRUE,
rank.by = c("pval", "logFC", "t"),
add.suffix = length(names) > 1L,
as.dt = FALSE
)
tabulateResults(
x,
names = resultNames(x),
max.p = 0.2,
p.col = c("padj", "padj.by.collection", "pval"),
as.dt = FALSE
)A SparrowResult() object.
pass through arguments
the names of the results desired
logical, set to FALSE if you want to return all
(column-wise) data for each result. By default only the pvalues,
adjusted pvalues, and rank are returned.
the statistic to use to append a rank column for the
geneset result. By default we rank by pvalue calculated by the GSEA
method. You can rank the results based on the trimmed mean of the logFC's
calculated for all of the features in the geneset ("logFC"), or the
trimmed t-statistics of the these features ("t").
If TRUE, adds .name as a suffix to the
columns of the method-specific statistics returned, ie. the
pval column from the "camera" result will be turned to
pval.camera.
If FALSE (default), the data.frame like thing that
this funciton returns will be set to a data.frame. Set this to TRUE
to keep this object as a data.table
the names of the GSEA methods to be reported. By default, this function will display results for all methods.
The maximum padj value to consider a result significant
use padj or padj.by.collection?
a data.table with the results from the requested method.
a data.table that summarizes the significant results per method per collection for the GSEA that was run
The product of an indivdual GSEA is consumed by the corresponding
do.<METHOD> function and converted into a data.table of results that
is internally stored.
Use the resultNames() function to identify which results are available
for interrogation. The result() function returns the statistics of
one individual result, and the results() function combines the results
from the specified methods into an arbitrarily wide data.table with
method-suffixed column names.
Use the tabulateResults() function to create a summary table that
tallies the number of significant genesets per collection, per method at
the specified FDR thresholds.
res <- exampleSparrowResult()
resultNames(res)
#> [1] "camera" "fry"
head(result(res, "camera"))
#> collection name active N n
#> 1 c2 BIOCARTA_AGPCR_PATHWAY TRUE 13 11
#> 2 c2 BOYAULT_LIVER_CANCER_SUBCLASS_G123_DN TRUE 51 41
#> 3 c2 BURTON_ADIPOGENESIS_PEAK_AT_2HR TRUE 51 50
#> 4 c2 BYSTRYKH_HEMATOPOIESIS_STEM_CELL_IL3RA TRUE 9 6
#> 5 c2 CAIRO_PML_TARGETS_BOUND_BY_MYC_UP TRUE 23 23
#> 6 c2 CHARAFE_BREAST_CANCER_BASAL_VS_MESENCHYMAL_DN TRUE 50 45
#> pval padj padj.by.collection n.sig n.neutral n.up n.down
#> 1 0.0227176661 0.0822694116 0.048680713 1 10 3 8
#> 2 0.0000431032 0.0005459739 0.000215516 9 32 11 30
#> 3 0.0014736432 0.0106445384 0.005135523 9 41 14 36
#> 4 0.5128750806 0.6187064464 0.569861201 0 6 1 5
#> 5 0.0621180232 0.1331287916 0.116471293 2 21 14 9
#> 6 0.0015406569 0.0106445384 0.005135523 7 38 13 32
#> n.sig.up n.sig.down mean.logFC mean.logFC.trim mean.t mean.t.trim
#> 1 0 1 -0.4953724 -0.5044566 -1.3726441 -1.3765614
#> 2 0 9 -0.7685127 -0.6500474 -1.5021451 -1.5212565
#> 3 1 8 -0.7654912 -0.7382118 -1.0964370 -1.1564061
#> 4 0 0 -0.2319652 -0.2319652 -0.5767635 -0.5767635
#> 5 2 0 0.3419176 0.2139964 0.6136099 0.4334291
#> 6 0 7 -0.4568979 -0.4711493 -1.1350462 -1.0681036
#> Direction rank
#> 1 Down 20
#> 2 Down 6
#> 3 Down 10
#> 4 Down 63
#> 5 Up 35
#> 6 Down 11
head(results(res))
#> collection name active N n
#> 1 c2 BIOCARTA_AGPCR_PATHWAY TRUE 13 11
#> 2 c2 BOYAULT_LIVER_CANCER_SUBCLASS_G123_DN TRUE 51 41
#> 3 c2 BURTON_ADIPOGENESIS_PEAK_AT_2HR TRUE 51 50
#> 4 c2 BYSTRYKH_HEMATOPOIESIS_STEM_CELL_IL3RA TRUE 9 6
#> 5 c2 CAIRO_PML_TARGETS_BOUND_BY_MYC_UP TRUE 23 23
#> 6 c2 CHARAFE_BREAST_CANCER_BASAL_VS_MESENCHYMAL_DN TRUE 50 45
#> pval.camera padj.camera padj.by.collection.camera pval.fry padj.fry
#> 1 0.0227176661 0.0822694116 0.048680713 0.0013287077 0.02141405
#> 2 0.0000431032 0.0005459739 0.000215516 0.0009023934 0.02141405
#> 3 0.0014736432 0.0106445384 0.005135523 0.0510869578 0.11086895
#> 4 0.5128750806 0.6187064464 0.569861201 0.2692576321 0.35282035
#> 5 0.0621180232 0.1331287916 0.116471293 0.2265804573 0.33764931
#> 6 0.0015406569 0.0106445384 0.005135523 0.0290220163 0.07605770
#> pval.mixed.fry padj.mixed.fry padj.by.collection.fry n.sig n.neutral n.up
#> 1 1.059937e-02 1.258676e-02 0.01014350 1 10 3
#> 2 5.202474e-07 4.626506e-06 0.01014350 9 32 11
#> 3 5.030725e-04 7.646702e-04 0.10616306 9 41 14
#> 4 9.573635e-01 9.573635e-01 0.35120561 0 6 1
#> 5 2.279844e-02 2.548061e-02 0.32368637 2 21 14
#> 6 2.194651e-04 3.706522e-04 0.07255504 7 38 13
#> n.down n.sig.up n.sig.down mean.logFC mean.logFC.trim mean.t mean.t.trim
#> 1 8 0 1 -0.4953724 -0.5044566 -1.3726441 -1.3765614
#> 2 30 0 9 -0.7685127 -0.6500474 -1.5021451 -1.5212565
#> 3 36 1 8 -0.7654912 -0.7382118 -1.0964370 -1.1564061
#> 4 5 0 0 -0.2319652 -0.2319652 -0.5767635 -0.5767635
#> 5 9 2 0 0.3419176 0.2139964 0.6136099 0.4334291
#> 6 32 0 7 -0.4568979 -0.4711493 -1.1350462 -1.0681036
#> rank.camera rank.fry
#> 1 20 3
#> 2 6 2
#> 3 10 35
#> 4 63 58
#> 5 35 51
#> 6 11 29