Getting started

This vignette explains how to work with sets using this package. The package provides a class to store the information efficiently and functions to work with it.

The TidySet class

To create a TidySet object, to store associations between elements and sets image we have several genes associated with a characteristic.

library("BaseSet")
gene_lists <- list(
    geneset1 = c("A", "B"),
    geneset2 = c("B", "C", "D")
)
tidy_set <- tidySet(gene_lists)
tidy_set
#>   elements     sets fuzzy
#> 1        A geneset1     1
#> 2        B geneset1     1
#> 3        B geneset2     1
#> 4        C geneset2     1
#> 5        D geneset2     1

This is then stored internally in three slots relations(), elements(), and sets() slots.

If you have more information for each element or set it can be added:

gene_data <- data.frame(
    stat1     = c( 1,   2,   3,   4 ),
    info1     = c("a", "b", "c", "d")
)

tidy_set <- add_column(tidy_set, "elements", gene_data)
set_data <- data.frame(
    Group     = c( 100 ,  200 ),
    Column     = c("abc", "def")
)
tidy_set <- add_column(tidy_set, "sets", set_data)
tidy_set
#>   elements     sets fuzzy Group Column stat1 info1
#> 1        A geneset1     1   100    abc     1     a
#> 2        B geneset1     1   100    abc     2     b
#> 3        B geneset2     1   200    def     2     b
#> 4        C geneset2     1   200    def     3     c
#> 5        D geneset2     1   200    def     4     d

This data is stored in one of the three slots, which can be directly accessed using their getter methods:

relations(tidy_set)
#>   elements     sets fuzzy
#> 1        A geneset1     1
#> 2        B geneset1     1
#> 3        B geneset2     1
#> 4        C geneset2     1
#> 5        D geneset2     1
elements(tidy_set)
#>   elements stat1 info1
#> 1        A     1     a
#> 2        B     2     b
#> 3        C     3     c
#> 4        D     4     d
sets(tidy_set)
#>       sets Group Column
#> 1 geneset1   100    abc
#> 2 geneset2   200    def

You can add as much information as you want, with the only restriction for a “fuzzy” column for the relations(). See the Fuzzy sets vignette: vignette("Fuzzy sets", "BaseSet").

You can also use the standard R approach with [:

gene_data <- data.frame(
    stat2     = c( 4,   4,   3,   5 ),
    info2     = c("a", "b", "c", "d")
)

tidy_set$info1 <- NULL
tidy_set[, "elements", c("stat2", "info2")] <- gene_data
tidy_set[, "sets", "Group"] <- c("low", "high")
tidy_set
#>   elements     sets fuzzy Group Column stat1 stat2 info2
#> 1        A geneset1     1   low    abc     1     4     a
#> 2        B geneset1     1   low    abc     2     4     b
#> 3        B geneset2     1  high    def     2     4     b
#> 4        C geneset2     1  high    def     3     3     c
#> 5        D geneset2     1  high    def     4     5     d

Observe that one can add, replace or delete

Creating a TidySet

As you can see it is possible to create a TidySet from a list. More commonly you can create it from a data.frame:

relations <- data.frame(elements = c("a", "b", "c", "d", "e", "f"), 
                        sets = c("A", "A", "A", "A", "A", "B"), 
                        fuzzy = c(1, 1, 1, 1, 1, 1))
TS <- tidySet(relations)
TS
#>   elements sets fuzzy
#> 1        a    A     1
#> 2        b    A     1
#> 3        c    A     1
#> 4        d    A     1
#> 5        e    A     1
#> 6        f    B     1

It is also possible from a matrix:

m <- matrix(c(0, 0, 1, 1, 1, 1, 0, 1, 0), ncol = 3, nrow = 3,  
               dimnames = list(letters[1:3], LETTERS[1:3]))
m
#>   A B C
#> a 0 1 0
#> b 0 1 1
#> c 1 1 0
tidy_set <- tidySet(m)
tidy_set
#>   elements sets fuzzy
#> 1        c    A     1
#> 2        a    B     1
#> 3        b    B     1
#> 4        c    B     1
#> 5        b    C     1

Or they can be created from a GeneSet and GeneSetCollection objects. Additionally it has several function to read files related to sets like the OBO files (getOBO) and GAF (getGAF)

Converting to other formats

It is possible to extract the gene sets as a list, for use with functions such as lapply.

as.list(tidy_set)
#> $A
#> c 
#> 1 
#> 
#> $B
#> a b c 
#> 1 1 1 
#> 
#> $C
#> b 
#> 1

Or if you need to apply some network methods and you need a matrix, you can create it with incidence:

incidence(tidy_set)
#>   A B C
#> c 1 1 0
#> a 0 1 0
#> b 0 1 1

Operations with sets

To work with sets several methods are provided. In general you can provide a new name for the resulting set of the operation, but if you don’t one will be automatically provided using naming(). All methods work with fuzzy and non-fuzzy sets

Union

You can make a union of two sets present on the same object.

BaseSet::union(tidy_set, sets = c("C", "B"), name = "D")
#>   elements sets fuzzy
#> 1        a    D     1
#> 2        b    D     1
#> 3        c    D     1

Intersection

intersection(tidy_set, sets = c("A", "B"), name = "D", keep = TRUE)
#>   elements sets fuzzy
#> 1        c    A     1
#> 2        a    B     1
#> 3        b    B     1
#> 4        c    B     1
#> 5        b    C     1
#> 6        c    D     1

The keep argument used here is if you want to keep all the other previous sets:

intersection(tidy_set, sets = c("A", "B"), name = "D", keep = FALSE)
#>   elements sets fuzzy
#> 1        c    D     1

Complement

We can look for the complement of one or several sets:

complement_set(tidy_set, sets = c("A", "B"))
#>   elements sets fuzzy
#> 1        c    A     1
#> 2        a    B     1
#> 3        b    B     1
#> 4        c    B     1
#> 5        b    C     1
#> 6        c ∁A∪B     0
#> 7        a ∁A∪B     0
#> 8        b ∁A∪B     0

Observe that we haven’t provided a name for the resulting set but we can provide one if we prefer to

complement_set(tidy_set, sets = c("A", "B"), name = "F")
#>   elements sets fuzzy
#> 1        c    A     1
#> 2        a    B     1
#> 3        b    B     1
#> 4        c    B     1
#> 5        b    C     1
#> 6        c    F     0
#> 7        a    F     0
#> 8        b    F     0

Subtract

This is the equivalent of setdiff, but clearer:

out <- subtract(tidy_set, set_in = "A", not_in = "B", name = "A-B")
out
#>   elements sets fuzzy
#> 1        c    A     1
#> 2        a    B     1
#> 3        b    B     1
#> 4        c    B     1
#> 5        b    C     1
name_sets(out)
#> [1] "A"   "B"   "C"   "A-B"
subtract(tidy_set, set_in = "B", not_in = "A", keep = FALSE)
#>   elements sets fuzzy
#> 1        a  B∖A     1
#> 2        b  B∖A     1

See that in the first case there isn’t any element present in B not in set A, but the new set is stored. In the second use case we focus just on the elements that are present on B but not in A.

Additional information

The number of unique elements and sets can be obtained using the nElements() and nSets() methods.

nElements(tidy_set)
#> [1] 3
nSets(tidy_set)
#> [1] 3
nRelations(tidy_set)
#> [1] 5

If you wish to know all in a single call you can use dim(tidy_set): 3, 5, 3. This summary doesn’t provide the number of relations of each set. You can quickly obtain that with lengths(tidy_set): 1, 3, 1

The size of each set can be obtained using the set_size() method.

set_size(tidy_set)
#>   sets size probability
#> 1    A    1           1
#> 2    B    3           1
#> 3    C    1           1

Conversely, the number of sets associated with each gene is returned by the element_size() function.

element_size(tidy_set)
#>   elements size probability
#> 1        c    2           1
#> 2        a    1           1
#> 3        b    2           1

The identifiers of elements and sets can be inspected and renamed using name_elements and

name_elements(tidy_set)
#> [1] "c" "a" "b"
name_elements(tidy_set) <- paste0("Gene", seq_len(nElements(tidy_set)))
name_elements(tidy_set)
#> [1] "Gene1" "Gene2" "Gene3"
name_sets(tidy_set)
#> [1] "A" "B" "C"
name_sets(tidy_set) <- paste0("Geneset", seq_len(nSets(tidy_set)))
name_sets(tidy_set)
#> [1] "Geneset1" "Geneset2" "Geneset3"

Using dplyr verbs

You can also use mutate(), filter(), select(), group_by() and other dplyr verbs with TidySets. You usually need to activate which three slots you want to affect with activate():

library("dplyr")
#> 
#> Attaching package: 'dplyr'
#> The following object is masked from 'package:BaseSet':
#> 
#>     union
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
m_TS <- tidy_set %>% 
  activate("relations") %>% 
  mutate(Important = runif(nRelations(tidy_set)))
m_TS
#>   elements     sets fuzzy Important
#> 1    Gene1 Geneset1     1 0.4760721
#> 2    Gene2 Geneset2     1 0.6663765
#> 3    Gene3 Geneset2     1 0.8283936
#> 4    Gene1 Geneset2     1 0.7318597
#> 5    Gene3 Geneset3     1 0.9536425

You can use activate to select what are the verbs modifying:

set_modified <- m_TS %>% 
  activate("elements") %>% 
  mutate(Pathway = if_else(elements %in% c("Gene1", "Gene2"), 
                           "pathway1", 
                           "pathway2"))
set_modified
#>   elements     sets fuzzy Important  Pathway
#> 1    Gene1 Geneset1     1 0.4760721 pathway1
#> 2    Gene2 Geneset2     1 0.6663765 pathway1
#> 3    Gene3 Geneset2     1 0.8283936 pathway2
#> 4    Gene1 Geneset2     1 0.7318597 pathway1
#> 5    Gene3 Geneset3     1 0.9536425 pathway2
set_modified %>% 
  deactivate() %>% # To apply a filter independently of where it is
  filter(Pathway == "pathway1")
#>   elements     sets fuzzy Important  Pathway
#> 1    Gene1 Geneset1     1 0.4760721 pathway1
#> 2    Gene2 Geneset2     1 0.6663765 pathway1
#> 3    Gene1 Geneset2     1 0.7318597 pathway1

If you think you need group_by usually this could mean that you need a new set. You can create a new one with group.

# A new group of those elements in pathway1 and with Important == 1
set_modified %>% 
  deactivate() %>% 
  group(name = "new", Pathway == "pathway1")
#>   elements     sets fuzzy Important  Pathway
#> 1    Gene1 Geneset1     1 0.4760721 pathway1
#> 2    Gene2 Geneset2     1 0.6663765 pathway1
#> 3    Gene3 Geneset2     1 0.8283936 pathway2
#> 4    Gene1 Geneset2     1 0.7318597 pathway1
#> 5    Gene3 Geneset3     1 0.9536425 pathway2
#> 6    Gene1      new     1        NA pathway1
#> 7    Gene2      new     1        NA pathway1
set_modified %>% 
  group("pathway1", elements %in% c("Gene1", "Gene2"))
#>   elements     sets fuzzy Important  Pathway
#> 1    Gene1 Geneset1     1 0.4760721 pathway1
#> 2    Gene2 Geneset2     1 0.6663765 pathway1
#> 3    Gene3 Geneset2     1 0.8283936 pathway2
#> 4    Gene1 Geneset2     1 0.7318597 pathway1
#> 5    Gene3 Geneset3     1 0.9536425 pathway2
#> 6    Gene1 pathway1     1        NA pathway1
#> 7    Gene2 pathway1     1        NA pathway1

You can use group_by() but it won’t return a TidySet.

set_modified %>% 
    deactivate() %>% 
    group_by(Pathway, sets) %>%  
    count()
#> # A tibble: 4 × 3
#> # Groups:   Pathway, sets [4]
#>   Pathway  sets         n
#>   <chr>    <chr>    <int>
#> 1 pathway1 Geneset1     1
#> 2 pathway1 Geneset2     2
#> 3 pathway2 Geneset2     1
#> 4 pathway2 Geneset3     1

After grouping or mutating sometimes we might be interested in moving a column describing something to other places. We can do by this with:

elements(set_modified)
#>   elements  Pathway
#> 1    Gene1 pathway1
#> 2    Gene2 pathway1
#> 3    Gene3 pathway2
out <- move_to(set_modified, "elements", "relations", "Pathway")
relations(out)
#>   elements     sets fuzzy Important  Pathway
#> 1    Gene1 Geneset1     1 0.4760721 pathway1
#> 2    Gene2 Geneset2     1 0.6663765 pathway1
#> 3    Gene3 Geneset2     1 0.8283936 pathway2
#> 4    Gene1 Geneset2     1 0.7318597 pathway1
#> 5    Gene3 Geneset3     1 0.9536425 pathway2

Session info

#> R Under development (unstable) (2025-02-14 r87718)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.2 LTS
#> 
#> Matrix products: default
#> BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0
#> 
#> locale:
#>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
#>  [3] LC_TIME=ca_ES.UTF-8        LC_COLLATE=C              
#>  [5] LC_MONETARY=ca_ES.UTF-8    LC_MESSAGES=en_US.UTF-8   
#>  [7] LC_PAPER=ca_ES.UTF-8       LC_NAME=C                 
#>  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
#> [11] LC_MEASUREMENT=ca_ES.UTF-8 LC_IDENTIFICATION=C       
#> 
#> time zone: Europe/Madrid
#> tzcode source: system (glibc)
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] dplyr_1.1.4   BaseSet_1.0.0
#> 
#> loaded via a namespace (and not attached):
#>  [1] vctrs_0.6.5       cli_3.6.3         knitr_1.49        rlang_1.1.5      
#>  [5] xfun_0.50         generics_0.1.3    jsonlite_1.8.9    glue_1.8.0       
#>  [9] htmltools_0.5.8.1 sass_0.4.9        rmarkdown_2.29    evaluate_1.0.3   
#> [13] jquerylib_0.1.4   tibble_3.2.1      fastmap_1.2.0     yaml_2.3.10      
#> [17] lifecycle_1.0.4   compiler_4.5.0    pkgconfig_2.0.3   rstudioapi_0.17.1
#> [21] digest_0.6.37     R6_2.5.1          tidyselect_1.2.1  utf8_1.2.4       
#> [25] pillar_1.10.1     magrittr_2.0.3    bslib_0.9.0       tools_4.5.0      
#> [29] cachem_1.1.0