Agglomerate data using taxonomic information or other grouping

Agglomeration functions can be used to sum-up data based on specific criteria such as taxonomic ranks, variables or prevalence.

agglomerateByRank can be used to sum up data based on associations with certain taxonomic ranks, as defined in rowData. Only available taxonomyRanks can be used.

agglomerateByVariable merges data on rows or columns of a SummarizedExperiment as defined by a factor alongside the chosen dimension. This function allows agglomeration of data based on other variables than taxonomy ranks. Metadata from the rowData or colData are retained as defined by archetype. assay are agglomerated, i.e. summed up. If the assay contains values other than counts or absolute values, this can lead to meaningless values being produced.

agglomerateByRanks takes a SummarizedExperiment, splits it along the taxonomic ranks, aggregates the data per rank, converts the input to a SingleCellExperiment objects and stores the aggregated data as alternative experiments. unsplitByRanks takes these alternative experiments and flattens them again into a single SummarizedExperiment.

agglomerateByRank(x, ...)

# S4 method for class 'TreeSummarizedExperiment'
agglomerateByRank(
  x,
  rank = taxonomyRanks(x)[1],
  update.tree = agglomerateTree,
  agglomerate.tree = agglomerateTree,
  agglomerateTree = FALSE,
  ...
)

# S4 method for class 'SingleCellExperiment'
agglomerateByRank(
  x,
  rank = taxonomyRanks(x)[1],
  altexp = NULL,
  altexp.rm = strip_altexp,
  strip_altexp = TRUE,
  ...
)

# S4 method for class 'SummarizedExperiment'
agglomerateByRank(
  x,
  rank = taxonomyRanks(x)[1],
  empty.rows.rm = TRUE,
  empty.fields = c(NA, "", " ", "\t", "-", "_"),
  ...
)

agglomerateByVariable(x, ...)

# S4 method for class 'TreeSummarizedExperiment'
agglomerateByVariable(
  x,
  by,
  group = f,
  f,
  update.tree = mergeTree,
  mergeTree = FALSE,
  ...
)

# S4 method for class 'SummarizedExperiment'
agglomerateByVariable(x, by, group = f, f, ...)

agglomerateByRanks(x, ...)

# S4 method for class 'SummarizedExperiment'
agglomerateByRanks(
  x,
  ranks = taxonomyRanks(x),
  na.rm = TRUE,
  as.list = FALSE,
  ...
)

# S4 method for class 'SingleCellExperiment'
agglomerateByRanks(
  x,
  ranks = taxonomyRanks(x),
  na.rm = TRUE,
  as.list = FALSE,
  ...
)

# S4 method for class 'TreeSummarizedExperiment'
agglomerateByRanks(
  x,
  ranks = taxonomyRanks(x),
  na.rm = TRUE,
  as.list = FALSE,
  ...
)

splitByRanks(x, ...)

unsplitByRanks(x, ...)

# S4 method for class 'SingleCellExperiment'
unsplitByRanks(
  x,
  ranks = taxonomyRanks(x),
  keep.dimred = keep_reducedDims,
  keep_reducedDims = FALSE,
  ...
)

# S4 method for class 'TreeSummarizedExperiment'
unsplitByRanks(
  x,
  ranks = taxonomyRanks(x),
  keep.dimred = keep_reducedDims,
  keep_reducedDims = FALSE,
  ...
)

Arguments

x: TreeSummarizedExperiment.
...: arguments passed to agglomerateByRank function for SummarizedExperiment objects and other functions. See agglomerateByRank for more details.
rank: Character scalar. Defines a taxonomic rank. Must be a value of taxonomyRanks() function.
update.tree: Logical scalar. Should rowTree() also be merged? (Default: FALSE)
agglomerate.tree: Deprecated. Use update.tree instead.
agglomerateTree: Deprecated. Use update.tree instead.
altexp: Character scalar or integer scalar. Specifies an alternative experiment containing the input data.
altexp.rm: Logical scalar. Should alternative experiments be removed prior to agglomeration? This prevents too many nested alternative experiments by default. (Default: TRUE)
strip_altexp: Deprecated. Use altexp.rm instead.
empty.rows.rm: Logical scalar. Defines whether rows including empty.fields in specified rank will be excluded. (Default: TRUE)
empty.fields: Character vector. Defines which values should be regarded as empty. (Default: c(NA, "", " ", "\t")). They will be removed if na.rm = TRUE before agglomeration.
by: Character scalar. Determines if data is merged row-wise / for features ('rows') or column-wise / for samples ('cols'). Must be 'rows' or 'cols'.
group: Character scalar, character vector or factor vector. A column name from rowData(x) or colData(x) or alternatively a vector specifying how the merging is performed. If vector, the value must be the same length as nrow(x)/ncol(x). Rows/Cols corresponding to the same level will be merged. If length(levels(group)) == nrow(x)/ncol(x), x will be returned unchanged.
f: Deprecated. Use group instead.
mergeTree: Deprecated. Use update.tree instead.
ranks: Character vector. Defines taxonomic ranks. Must all be values of taxonomyRanks() function.
na.rm: Logical scalar. Should NA values be omitted? (Default: TRUE)
as.list: Logical scalar. Should the list of SummarizedExperiment objects be returned by the function agglomerateByRanks as a SimpleList or stored in altExps? (Default: FALSE)
keep.dimred: Logical scalar. Should the reducedDims(x) be transferred to the result? Please note, that this breaks the link between the data used to calculate the reduced dims. (Default: FALSE)
keep_reducedDims: Deprecated. Use keep.dimred instead.

Value

agglomerateByRank returns a taxonomically-agglomerated, optionally-pruned object of the same class as x. agglomerateByVariable returns an object of the same class as x with the specified entries merged into one entry in all relevant components. agglomerateByRank returns a taxonomically-agglomerated, optionally-pruned object of the same class as x.

For agglomerateByRanks: If as.list = TRUE : SummarizedExperiment objects in a SimpleList If as.list = FALSE : The SummarizedExperiment passed as a parameter and now containing the SummarizedExperiment objects in its altExps

For unsplitByRanks: x, with rowData and assay data replaced by the unsplit data. colData of x is kept as well and any existing rowTree is dropped as well, since existing rowLinks are not valid anymore.

Details

Agglomeration sums up the values of assays at the specified taxonomic level. With certain assays, e.g. those that include binary or negative values, this summing can produce meaningless values. In those cases, consider performing agglomeration first, and then applying the transformation afterwards.

agglomerateByVariable works similarly to sumCountsAcrossFeatures. However, additional support for TreeSummarizedExperiment was added and science field agnostic names were used. In addition the archetype argument lets the user select how to preserve row or column data.

For merge data of assays the function from scuttle are used.

agglomerateByRanks will use by default all available taxonomic ranks, but this can be controlled by setting ranks manually. NA values are removed by default, since they would not make sense, if the result should be used for unsplitByRanks at some point. The input data remains unchanged in the returned SingleCellExperiment objects.

unsplitByRanks will remove any NA value on each taxonomic rank so that no ambiguous data is created. In additional, a column taxonomicLevel is created or overwritten in the rowData to specify from which alternative experiment this originates from. This can also be used for splitAltExps to split the result along the same factor again. The input data from the base objects is not returned, only the data from the altExp(). Be aware that changes to rowData of the base object are not returned, whereas only the colData of the base object is kept.

Examples


### Agglomerate data based on taxonomic information

data(GlobalPatterns)
# print the available taxonomic ranks
colnames(rowData(GlobalPatterns))
#> [1] "Kingdom" "Phylum"  "Class"   "Order"   "Family"  "Genus"   "Species"
taxonomyRanks(GlobalPatterns)
#> [1] "Kingdom" "Phylum"  "Class"   "Order"   "Family"  "Genus"   "Species"

# agglomerate at the Family taxonomic rank
x1 <- agglomerateByRank(GlobalPatterns, rank="Family")
## How many taxa before/after agglomeration?
nrow(GlobalPatterns)
#> [1] 19216
nrow(x1)
#> [1] 341

# agglomerate the tree as well
x2 <- agglomerateByRank(GlobalPatterns, rank="Family",
                       update.tree = TRUE)
nrow(x2) # same number of rows, but
#> [1] 341
rowTree(x1) # ... different
#> 
#> Phylogenetic tree with 19216 tips and 19215 internal nodes.
#> 
#> Tip labels:
#>   549322, 522457, 951, 244423, 586076, 246140, ...
#> Node labels:
#>   , 0.858.4, 1.000.154, 0.764.3, 0.995.2, 1.000.2, ...
#> 
#> Rooted; includes branch lengths.
rowTree(x2) # ... tree
#> 
#> Phylogenetic tree with 341 tips and 340 internal nodes.
#> 
#> Tip labels:
#>   951, 215972, 138353, 546313, 173903, 202347, ...
#> Node labels:
#>   , 0.858.4, 0.764.3, 0.985.6, 1.000.112, 0.978.18, ...
#> 
#> Rooted; includes branch lengths.

# If assay contains binary or negative values, summing might lead to
# meaningless values, and you will get a warning. In these cases, you might
# want to do agglomeration again at chosen taxonomic level.
tse <- transformAssay(GlobalPatterns, method = "pa")
tse <- agglomerateByRank(tse, rank = "Genus")
#> Warning: 'pa' includes binary values.
#> Agglomeration of it might lead to meaningless values.
#> Check the assay, and consider doing transformation againmanually with agglomerated data.
tse <- transformAssay(tse, method = "pa")

# removing empty labels by setting empty.rows.rm = TRUE
sum(is.na(rowData(GlobalPatterns)$Family))
#> [1] 5603
x3 <- agglomerateByRank(GlobalPatterns, rank="Family", empty.rows.rm = TRUE)
nrow(x3) # different from x2
#> [1] 341

# Because all the rownames are from the same rank, rownames do not include
# prefixes, in this case "Family:".
print(rownames(x3[1:3,]))
#> [1] "125ds10" "211ds20" "5B-12"  

# To add them, use getTaxonomyLabels function.
rownames(x3) <- getTaxonomyLabels(x3, with.rank = TRUE)
print(rownames(x3[1:3,]))
#> [1] "Family:125ds10" "Family:211ds20" "Family:5B-12"  

# use 'empty.ranks.rm' to remove columns that include only NAs
x4 <- agglomerateByRank(GlobalPatterns, rank="Phylum",
                        empty.ranks.rm = TRUE)
head(rowData(x4))
#> DataFrame with 6 rows and 2 columns
#>                     Kingdom          Phylum
#>                 <character>     <character>
#> ABY1_OD1           Bacteria        ABY1_OD1
#> AC1                Bacteria             AC1
#> AD3                Bacteria             AD3
#> Acidobacteria      Bacteria   Acidobacteria
#> Actinobacteria     Bacteria  Actinobacteria
#> Armatimonadetes    Bacteria Armatimonadetes

# If the assay contains NAs, you might want to specify na.rm=TRUE,
# since summing-up NAs lead to NA
x5 <- GlobalPatterns
# Replace first value with NA
assay(x5)[1,1] <- NA
x6 <- agglomerateByRank(x5, "Kingdom")
head( assay(x6) )
#>             CL3     CC1    SV1 M31Fcsw M11Fcsw M31Plmr M11Plmr F21Plmr M31Tong
#> Archaea      NA    1248  28811      33      57      42     112     140     303
#> Bacteria 862815 1134209 668698 1543418 2076419  718901  433782  186157 2000099
#>          M11Tong LMEpi24M SLEpi20M  AQC1cm  AQC4cm  AQC7cm    NP2     NP3
#> Archaea       30      131      145    4459   24692   28051   1826   43197
#> Bacteria  100157  2117461  1217167 1163289 2332489 1671242 521808 1435768
#>              NP5 TRRsed1 TRRsed2 TRRsed3   TS28    TS29   Even1  Even2   Even3
#> Archaea    33996     843    8418   14250   1598    1690     150     23      91
#> Bacteria 1618758   57845  484708  265454 935868 1209381 1215987 971050 1078150
# Use na.rm=TRUE
x6 <- agglomerateByRank(x5, "Kingdom", na.rm = TRUE)
head( assay(x6) )
#>             CL3     CC1    SV1 M31Fcsw M11Fcsw M31Plmr M11Plmr F21Plmr M31Tong
#> Archaea    1262    1248  28811      33      57      42     112     140     303
#> Bacteria 862815 1134209 668698 1543418 2076419  718901  433782  186157 2000099
#>          M11Tong LMEpi24M SLEpi20M  AQC1cm  AQC4cm  AQC7cm    NP2     NP3
#> Archaea       30      131      145    4459   24692   28051   1826   43197
#> Bacteria  100157  2117461  1217167 1163289 2332489 1671242 521808 1435768
#>              NP5 TRRsed1 TRRsed2 TRRsed3   TS28    TS29   Even1  Even2   Even3
#> Archaea    33996     843    8418   14250   1598    1690     150     23      91
#> Bacteria 1618758   57845  484708  265454 935868 1209381 1215987 971050 1078150

## Look at enterotype dataset...
data(enterotype)
## Print the available taxonomic ranks. Shows only 1 available rank,
## not useful for agglomerateByRank
taxonomyRanks(enterotype)
#> [1] "Genus"

### Merge TreeSummarizedExperiments on rows and columns

data(esophagus)
esophagus
#> class: TreeSummarizedExperiment 
#> dim: 58 3 
#> metadata(0):
#> assays(1): counts
#> rownames(58): 59_8_22 59_5_13 ... 65_9_9 59_2_6
#> rowData names(0):
#> colnames(3): B C D
#> colData names(0):
#> reducedDimNames(0):
#> mainExpName: NULL
#> altExpNames(0):
#> rowLinks: a LinkDataFrame (58 rows)
#> rowTree: 1 phylo tree(s) (58 leaves)
#> colLinks: NULL
#> colTree: NULL
plot(rowTree(esophagus))

# get a factor for merging
f <- factor(regmatches(rownames(esophagus),
                       regexpr("^[0-9]*_[0-9]*",rownames(esophagus))))
merged <- agglomerateByVariable(esophagus, by = "rows", f,
                                update.tree = TRUE)
plot(rowTree(merged))

#
data(GlobalPatterns)
GlobalPatterns
#> class: TreeSummarizedExperiment 
#> dim: 19216 26 
#> metadata(0):
#> assays(1): counts
#> rownames(19216): 549322 522457 ... 200359 271582
#> rowData names(7): Kingdom Phylum ... Genus Species
#> colnames(26): CL3 CC1 ... Even2 Even3
#> colData names(7): X.SampleID Primer ... SampleType Description
#> reducedDimNames(0):
#> mainExpName: NULL
#> altExpNames(0):
#> rowLinks: a LinkDataFrame (19216 rows)
#> rowTree: 1 phylo tree(s) (19216 leaves)
#> colLinks: NULL
#> colTree: NULL
merged <- agglomerateByVariable(GlobalPatterns, by = "cols",
                                colData(GlobalPatterns)$SampleType)
merged
#> class: TreeSummarizedExperiment 
#> dim: 19216 9 
#> metadata(0):
#> assays(1): counts
#> rownames(19216): 549322 522457 ... 200359 271582
#> rowData names(7): Kingdom Phylum ... Genus Species
#> colnames(9): Feces Freshwater ... Soil Tongue
#> colData names(7): X.SampleID Primer ... SampleType Description
#> reducedDimNames(0):
#> mainExpName: NULL
#> altExpNames(0):
#> rowLinks: a LinkDataFrame (19216 rows)
#> rowTree: 1 phylo tree(s) (19216 leaves)
#> colLinks: NULL
#> colTree: NULL

data(GlobalPatterns)
# print the available taxonomic ranks
taxonomyRanks(GlobalPatterns)
#> [1] "Kingdom" "Phylum"  "Class"   "Order"   "Family"  "Genus"   "Species"

# agglomerateByRanks
# 
tse <- agglomerateByRanks(GlobalPatterns)
altExps(tse)
#> List of length 7
#> names(7): Kingdom Phylum Class Order Family Genus Species
altExp(tse,"Kingdom")
#> class: TreeSummarizedExperiment 
#> dim: 2 26 
#> metadata(1): agglomerated_by_rank
#> assays(1): counts
#> rownames(2): Archaea Bacteria
#> rowData names(7): Kingdom Phylum ... Genus Species
#> colnames(26): CL3 CC1 ... Even2 Even3
#> colData names(7): X.SampleID Primer ... SampleType Description
#> reducedDimNames(0):
#> mainExpName: NULL
#> altExpNames(0):
#> rowLinks: a LinkDataFrame (2 rows)
#> rowTree: 1 phylo tree(s) (19216 leaves)
#> colLinks: NULL
#> colTree: NULL
altExp(tse,"Species")
#> class: TreeSummarizedExperiment 
#> dim: 944 26 
#> metadata(1): agglomerated_by_rank
#> assays(1): counts
#> rownames(944): Abiotrophiadefectiva Achromatiumoxaliferum ...
#>   proteobacteriumsymbiontofOsedaxsp.MB4 symbiontofNoeetapupillata
#> rowData names(7): Kingdom Phylum ... Genus Species
#> colnames(26): CL3 CC1 ... Even2 Even3
#> colData names(7): X.SampleID Primer ... SampleType Description
#> reducedDimNames(0):
#> mainExpName: NULL
#> altExpNames(0):
#> rowLinks: a LinkDataFrame (944 rows)
#> rowTree: 1 phylo tree(s) (19216 leaves)
#> colLinks: NULL
#> colTree: NULL

# unsplitByRanks
tse <- unsplitByRanks(tse)
tse
#> class: TreeSummarizedExperiment 
#> dim: 2692 26 
#> metadata(0):
#> assays(1): counts
#> rownames(2692): Kingdom:Archaea Kingdom:Bacteria ...
#>   Species:proteobacteriumsymbiontofOsedaxsp.MB4
#>   Species:symbiontofNoeetapupillata
#> rowData names(8): Kingdom Phylum ... Species taxonomicLevel
#> colnames(26): CL3 CC1 ... Even2 Even3
#> colData names(7): X.SampleID Primer ... SampleType Description
#> reducedDimNames(0):
#> mainExpName: NULL
#> altExpNames(0):
#> rowLinks: NULL
#> rowTree: NULL
#> colLinks: NULL
#> colTree: NULL

Agglomerate data using taxonomic information or other grouping

Arguments

Value

Details

See also

Examples