Estimate divergence against a given reference sample.
addDivergence(
x,
assay.type = assay_name,
assay_name = "counts",
name = "divergence",
reference = "median",
FUN = vegan::vegdist,
method = "bray",
...
)
# S4 method for SummarizedExperiment
addDivergence(
x,
assay.type = assay_name,
assay_name = "counts",
name = "divergence",
reference = "median",
FUN = vegan::vegdist,
method = "bray",
...
)a SummarizedExperiment object.
the name of the assay used for calculation of the sample-wise estimates.
a single character value for specifying which
assay to use for calculation.
(Please use assay.type instead. At some point assay_name
will be disabled.)
a name for the column of the colData the results should be
stored in. By default, name is "divergence".
a numeric vector that has length equal to number of
features, or a non-empty character value; either 'median' or 'mean'.
reference specifies the reference that is used to calculate
divergence. by default, reference is "median".
a function for distance calculation. The function must
expect the input matrix as its first argument. With rows as samples
and columns as features. By default, FUN is
vegan::vegdist.
a method that is used to calculate the distance. Method is
passed to the function that is specified by FUN. By default,
method is "bray".
optional arguments
x with additional colData named *name*
Microbiota divergence (heterogeneity / spread) within a given sample set can be quantified by the average sample dissimilarity or beta diversity with respect to a given reference sample.
This measure is sensitive to sample size. Subsampling or bootstrapping can be applied to equalize sample sizes between comparisons.
data(GlobalPatterns)
tse <- GlobalPatterns
# By default, reference is median of all samples. The name of column where results
# is "divergence" by default, but it can be specified.
tse <- addDivergence(tse)
# The method that are used to calculate distance in divergence and
# reference can be specified. Here, euclidean distance and dist function from
# stats package are used. Reference is the first sample.
tse <- addDivergence(tse, name = "divergence_first_sample",
reference = assays(tse)$counts[,1],
FUN = stats::dist, method = "euclidean")
# Reference can also be median or mean of all samples.
# By default, divergence is calculated by using median. Here, mean is used.
tse <- addDivergence(tse, name = "divergence_average", reference = "mean")
# All three divergence results are stored in colData.
colData(tse)
#> DataFrame with 26 rows and 10 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 divergence
#> <factor> <numeric>
#> CL3 Calhoun South Carolina Pine soil, pH 4.9 0.989114
#> CC1 Cedar Creek Minnesota, grassland, pH 6.1 0.991217
#> SV1 Sevilleta new Mexico, desert scrub, pH 8.3 0.986994
#> M31Fcsw M3, Day 1, fecal swab, whole body study 0.995435
#> M11Fcsw M1, Day 1, fecal swab, whole body study 0.996395
#> ... ... ...
#> TS28 Twin #1 0.991388
#> TS29 Twin #2 0.992698
#> Even1 Even1 0.990063
#> Even2 Even2 0.989827
#> Even3 Even3 0.991461
#> divergence_first_sample divergence_average
#> <numeric> <numeric>
#> CL3 0.0 0.879196
#> CC1 83210.0 0.875744
#> SV1 73809.5 0.915286
#> M31Fcsw 419594.0 0.842727
#> M11Fcsw 626574.7 0.870541
#> ... ... ...
#> TS28 185596 0.813599
#> TS29 352153 0.863493
#> Even1 225268 0.809229
#> Even2 194434 0.808371
#> Even3 204304 0.814546