These functions return information about the most dominant taxa in a
SummarizedExperiment
object.
getDominant(
x,
assay.type = assay_name,
assay_name = "counts",
group = rank,
rank = NULL,
other.name = "Other",
n = NULL,
complete = TRUE,
...
)
# S4 method for class 'SummarizedExperiment'
getDominant(
x,
assay.type = assay_name,
assay_name = "counts",
group = rank,
rank = NULL,
other.name = "Other",
n = NULL,
complete = TRUE,
...
)
addDominant(x, name = "dominant_taxa", other.name = "Other", n = NULL, ...)
# S4 method for class 'SummarizedExperiment'
addDominant(
x,
name = "dominant_taxa",
other.name = "Other",
n = NULL,
complete = FALSE,
...
)Character scalar. Specifies which assay to use for
calculation. (Default: "counts")
Deprecated. Use assay.type instead.
Character scalar. Defines a group. Must be one of the
columns from rowData(x). (Default: NULL)
Deprecated. Use group instead.
Character scalar. A name for features that are not
included in n the most frequent dominant features in the data.
(Default: "Other")
Numeric scalar. The number of features that are the most
frequent
dominant features. Default is NULL, which defaults that each sample is
assigned
a dominant taxon. (Default: NULL)
Logical scalar. A value to manage multiple dominant
taxa for a sample.
Default for getDominant is TRUE to include all equally dominant taxa
for each sample. complete = FALSE samples one taxa for the samples that have
multiple.
Default for addDominant is FALSE to add a column with only one
dominant taxon assigned for each sample into colData. complete = TRUE adds a
list that includes all dominant taxa for each sample into colData.
Additional arguments passed on to agglomerateByRank() when
rank is specified.
Character scalar. A name for the column of the
colData where results will be stored.
(Default: "dominant_taxa")
getDominant returns a named character vector x
while addDominant returns
SummarizedExperiment
with additional column in colData named *name*.
addDominant extracts the most abundant taxa in a
SummarizedExperiment
object, and stores the information in the colData. It is a wrapper for
getDominant.
With group parameter, it is possible to agglomerate rows based on
groups. If the value is one of the columns in taxonomyRanks(),
agglomerateByRank() is applied. Otherwise,
agglomerateByVariable() is utilized.
E.g. if 'Genus' rank is used, all abundances of same Genus
are added together, and agglomerated features are returned.
See corresponding functions for additional arguments to deal with
missing values or special characters.
data(GlobalPatterns)
x <- GlobalPatterns
# Finds the dominant taxa.
sim.dom <- getDominant(x, group = "Genus")
# Add information to colData
x <- addDominant(x, group = "Genus", name ="dominant_genera")
colData(x)
#> DataFrame with 26 rows and 8 columns
#> X.SampleID Primer Final_Barcode Barcode_truncated_plus_T
#> <factor> <factor> <factor> <factor>
#> CL3 CL3 ILBC_01 AACGCA TGCGTT
#> CC1 CC1 ILBC_02 AACTCG CGAGTT
#> SV1 SV1 ILBC_03 AACTGT ACAGTT
#> M31Fcsw M31Fcsw ILBC_04 AAGAGA TCTCTT
#> M11Fcsw M11Fcsw ILBC_05 AAGCTG CAGCTT
#> ... ... ... ... ...
#> TS28 TS28 ILBC_25 ACCAGA TCTGGT
#> TS29 TS29 ILBC_26 ACCAGC GCTGGT
#> Even1 Even1 ILBC_27 ACCGCA TGCGGT
#> Even2 Even2 ILBC_28 ACCTCG CGAGGT
#> Even3 Even3 ILBC_29 ACCTGT ACAGGT
#> Barcode_full_length SampleType
#> <factor> <factor>
#> CL3 CTAGCGTGCGT Soil
#> CC1 CATCGACGAGT Soil
#> SV1 GTACGCACAGT Soil
#> M31Fcsw TCGACATCTCT Feces
#> M11Fcsw CGACTGCAGCT Feces
#> ... ... ...
#> TS28 GCATCGTCTGG Feces
#> TS29 CTAGTCGCTGG Feces
#> Even1 TGACTCTGCGG Mock
#> Even2 TCTGATCGAGG Mock
#> Even3 AGAGAGACAGG Mock
#> Description dominant_genera
#> <factor> <character>
#> CL3 Calhoun South Carolina Pine soil, pH 4.9 CandidatusSolibacter
#> CC1 Cedar Creek Minnesota, grassland, pH 6.1 MC18
#> SV1 Sevilleta new Mexico, desert scrub, pH 8.3 CandidatusNitrososph..
#> M31Fcsw M3, Day 1, fecal swab, whole body study Bacteroides
#> M11Fcsw M1, Day 1, fecal swab, whole body study Bacteroides
#> ... ... ...
#> TS28 Twin #1 Faecalibacterium
#> TS29 Twin #2 Faecalibacterium
#> Even1 Even1 Bacteroides
#> Even2 Even2 Bacteroides
#> Even3 Even3 Bacteroides