# Advanced examples

#### Lluís Revilla

lluis.revilla@gmail.com

#### 2023 ago 19

Abstract

This vignette assumes you are familiar with set operations from the basic vignette.

# Initial setup

To show compatibility with tidy workflows we will use magrittr pipe operator and the dplyr verbs.

    library("BaseSet", quietly = TRUE)
    library("dplyr", quietly = TRUE)

# Human gene ontology

We will explore the genes with assigned gene ontology terms. These terms describe what is the process and role of the genes. The links are annotated with different evidence codes to indicate how such annotation is supported.

    # We load some libraries
    library("org.Hs.eg.db", quietly = TRUE)
    library("GO.db", quietly = TRUE)
    library("ggplot2", quietly = TRUE)
    # Prepare the data
    h2GO_TS <- tidySet(org.Hs.egGO)
    h2GO <- as.data.frame(org.Hs.egGO)

We can now explore if there are differences in evidence usage for each ontology in gene ontology:

    library("forcats", include.only = "fct_reorder2", quietly = TRUE)
    h2GO %>%
        group_by(Evidence, Ontology) %>%
        count(name = "Freq") %>%
        ungroup() %>%
        mutate(Evidence = fct_reorder2(Evidence, Ontology, -Freq),
               Ontology = case_match(Ontology,
                                     "CC" ~ "Cellular Component",
                                     "MF" ~ "Molecular Function",
                                     "BP" ~ "Biological Process",
                                     .default = NA)) %>%
        ggplot() +
        geom_col(aes(Evidence, Freq)) +
        facet_grid(~Ontology) +
        theme_minimal() +
        coord_flip() +
        labs(x = element_blank(), y = element_blank(),
             title = "Evidence codes for each ontology")

We can see that biological process are more likely to be defined by IMP evidence code that means inferred from mutant phenotype. While inferred from physical interaction (IPI) is almost exclusively used to assign molecular functions.

This graph doesn’t consider that some relationships are better annotated than other:

    h2GO_TS %>%
        relations() %>%
        group_by(elements, sets) %>%
        count(sort = TRUE, name = "Annotations") %>%
        ungroup() %>%
        count(Annotations, sort = TRUE) %>%
        ggplot() +
        geom_col(aes(Annotations, n)) +
        theme_minimal() +
        labs(x = "Evidence codes", y = "Annotations",
             title = "Evidence codes for each annotation",
             subtitle = "in human") +
        scale_x_continuous(breaks = 1:7)

We can see that mostly all the annotations are done with a single evidence code. So far we have explored the code that it is related to a gene but how many genes don’t have any annotation?

    # Add all the genes and GO terms
    h2GO_TS <- add_elements(h2GO_TS, keys(org.Hs.eg.db)) %>%
        add_sets(grep("^GO:", keys(GO.db), value = TRUE))

    sizes_element <- element_size(h2GO_TS) %>%
        arrange(desc(size))
    sum(sizes_element$size == 0)
    sum(sizes_element$size != 0)

    sizes_set <- set_size(h2GO_TS) %>%
        arrange(desc(size))
    sum(sizes_set$size == 0)
    sum(sizes_set$size != 0)

So we can see that both there are more genes without annotation and more gene ontology terms without a (direct) gene annotated.

    sizes_element %>%
        filter(size != 0) %>%
        ggplot() +
        geom_histogram(aes(size), binwidth = 1) +
        theme_minimal() +
        labs(x = "# sets per element", y = "Count")

    sizes_set %>%
        filter(size != 0) %>%
        ggplot() +
        geom_histogram(aes(size), binwidth = 1) +
        theme_minimal() +
        labs(x = "# elements per set", y = "Count")

As you can see on the second plot we have very large values but that are on associated on many genes:

    head(sizes_set, 10)

## Using fuzzy values

This could radically change if we used fuzzy values. We could assign a fuzzy value to each evidence code given the lowest fuzzy value for the IEA (Inferred from Electronic Annotation) evidence. The highest values would be for evidence codes coming from experiments or alike.

    nr <- h2GO_TS %>%
        relations() %>%
        dplyr::select(sets, Evidence) %>%
        distinct() %>%
        mutate(fuzzy = case_match(Evidence,
                                  "EXP" ~ 0.9,
                                  "IDA" ~ 0.8,
                                  "IPI" ~ 0.8,
                                  "IMP" ~ 0.75,
                                  "IGI" ~ 0.7,
                                  "IEP" ~ 0.65,
                                  "HEP" ~ 0.6,
                                  "HDA" ~ 0.6,
                                  "HMP" ~ 0.5,
                                  "IBA" ~ 0.45,
                                  "ISS" ~ 0.4,
                                  "ISO" ~ 0.32,
                                  "ISA" ~ 0.32,
                                  "ISM" ~ 0.3,
                                  "RCA" ~ 0.2,
                                  "TAS" ~ 0.15,
                                  "NAS" ~ 0.1,
                                  "IC" ~ 0.02,
                                  "ND" ~ 0.02,
                                  "IEA" ~ 0.01,
                                  .default = 0.01)) %>%
        dplyr::select(sets = "sets", elements = "Evidence", fuzzy = fuzzy)

We have several evidence codes for the same ontology, this would result on different fuzzy values for each relation. Instead, we extract this and add them as new sets and elements and add an extra column to classify what are those elements:

    ts <- h2GO_TS %>%
        relations() %>%
        dplyr::select(-Evidence) %>%
        rbind(nr) %>%
        tidySet() %>%
        mutate_element(Type = ifelse(grepl("^[0-9]+$", elements), "gene", "evidence"))

Now we can see which gene ontologies are more supported by the evidence:

    ts %>%
        dplyr::filter(Type != "Gene") %>%
        cardinality() %>%
        arrange(desc(cardinality)) %>%
        head()

Surprisingly the most supported terms are protein binding, nucleus and cytosol. I would expect them to be the top three terms for cellular component, biological function and molecular function.

Calculating set sizes would be interesting but it requires computing a big number of combinations that make it last long and require many memory available.

    ts %>%
        filter(sets %in% c("GO:0008152", "GO:0003674", "GO:0005575"),
               Type != "gene") %>%
        set_size()

Unexpectedly there is few evidence for the main terms:

    go_terms <- c("GO:0008152", "GO:0003674", "GO:0005575")
    ts %>%
        filter(sets %in% go_terms & Type != "gene")

In fact those terms are arbitrarily decided or inferred from electronic analysis.

# Human pathways

Now we will repeat the same analysis with pathways:

    # We load some libraries
    library("reactome.db")

    # Prepare the data (is easier, there isn't any ontoogy or evidence column)
    h2p <- as.data.frame(reactomeEXTID2PATHID)
    colnames(h2p) <- c("sets", "elements")
    # Filter only for human pathways
    h2p <- h2p[grepl("^R-HSA-", h2p$sets), ]

    # There are duplicate relations with different evidence codes!!:
    summary(duplicated(h2p[, c("elements", "sets")]))
    h2p <- unique(h2p)
    # Create a TidySet and
    h2p_TS <- tidySet(h2p) %>%
        # Add all the genes
        add_elements(keys(org.Hs.eg.db))

Now that we have everything ready we can start measuring some things…

    sizes_element <- element_size(h2p_TS) %>%
        arrange(desc(size))
    sum(sizes_element$size == 0)
    sum(sizes_element$size != 0)

    sizes_set <- set_size(h2p_TS) %>%
        arrange(desc(size))

We can see there are more genes without pathways than genes with pathways.

    sizes_element %>%
        filter(size != 0) %>%
        ggplot() +
        geom_histogram(aes(size), binwidth = 1) +
        scale_y_log10() +
        theme_minimal() +
        labs(x = "# sets per element", y = "Count")

    sizes_set %>%
        ggplot() +
        geom_histogram(aes(size), binwidth = 1) +
        scale_y_log10() +
        theme_minimal() +
        labs(x = "# elements per set", y = "Count")

As you can see on the second plot we have very large values but that are on associated on many genes:

    head(sizes_set, 10)

# Session info

    #> R version 4.3.1 (2023-06-16)
    #> Platform: x86_64-pc-linux-gnu (64-bit)
    #> Running under: Ubuntu 22.04.3 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-r0.3.20.so;  LAPACK version 3.10.0
    #>
    #> locale:
    #>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C
    #>  [3] LC_TIME=es_ES.UTF-8        LC_COLLATE=C
    #>  [5] LC_MONETARY=es_ES.UTF-8    LC_MESSAGES=en_US.UTF-8
    #>  [7] LC_PAPER=es_ES.UTF-8       LC_NAME=C
    #>  [9] LC_ADDRESS=C               LC_TELEPHONE=C
    #> [11] LC_MEASUREMENT=es_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_1.1.2   BaseSet_0.9.0
    #>
    #> loaded via a namespace (and not attached):
    #>  [1] vctrs_0.6.3       cli_3.6.1         knitr_1.43        rlang_1.1.1
    #>  [5] xfun_0.40         generics_0.1.3    jsonlite_1.8.7    glue_1.6.2
    #>  [9] htmltools_0.5.6   sass_0.4.7        fansi_1.0.4       rmarkdown_2.24
    #> [13] evaluate_0.21     jquerylib_0.1.4   tibble_3.2.1      fastmap_1.1.1
    #> [17] yaml_2.3.7        lifecycle_1.0.3   compiler_4.3.1    pkgconfig_2.0.3
    #> [21] rstudioapi_0.15.0 digest_0.6.33     R6_2.5.1          tidyselect_1.2.0
    #> [25] utf8_1.2.3        pillar_1.9.0      magrittr_2.0.3    bslib_0.5.1
    #> [29] tools_4.3.1       cachem_1.0.8