Advanced examples

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$size == 0)
#> [1] 172623
sum(sizes_element$size != 0)
#> [1] 20759

sizes_set <- set_size(h2GO_TS) %>% 
    arrange(desc(size))
sum(sizes_set$size == 0)
#> [1] 23750
sum(sizes_set$size != 0)
#> [1] 18692

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)
#>          sets  size probability Ontology
#> 1  GO:0005515 12707           1       MF
#> 2  GO:0005634  5658           1       CC
#> 3  GO:0005829  5446           1       CC
#> 4  GO:0005886  5209           1       CC
#> 5  GO:0005737  5109           1       CC
#> 6  GO:0005654  3905           1       CC
#> 7  GO:0016020  3227           1       CC
#> 8  GO:0046872  2374           1       MF
#> 9  GO:0070062  2212           1       CC
#> 10 GO:0005576  1984           1       CC

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()
#>         sets cardinality
#> 1 GO:0005515    12709.91
#> 2 GO:0005634     5664.00
#> 3 GO:0005829     5450.78
#> 4 GO:0005886     5214.60
#> 5 GO:0005737     5114.10
#> 6 GO:0005654     3908.26

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()
#>         sets size probability
#> 1 GO:0003674    0        0.98
#> 2 GO:0003674    1        0.02
#> 3 GO:0005575    0        0.98
#> 4 GO:0005575    1        0.02
#> 5 GO:0008152    0        0.99
#> 6 GO:0008152    1        0.01

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") 
#>   elements       sets fuzzy     Type
#> 1      IEA GO:0008152  0.01 evidence
#> 2       ND GO:0005575  0.02 evidence
#> 3       ND GO:0003674  0.02 evidence

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")]))
#>    Mode   FALSE    TRUE 
#> logical  130356   14427
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$size == 0)
#> [1] 182291
sum(sizes_element$size != 0)
#> [1] 11365

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")
#> Warning in scale_y_log10(): log-10 transformation introduced
#> infinite values.
#> Warning: Removed 286 rows containing missing values or values outside the scale range
#> (`geom_bar()`).


sizes_set %>% 
    ggplot() +
    geom_histogram(aes(size), binwidth = 1) +
    scale_y_log10() +
    theme_minimal() +
    labs(x = "# elements per set", y = "Count")
#> Warning in scale_y_log10(): log-10 transformation introduced
#> infinite values.
#> Warning: Removed 2350 rows containing missing values or values outside the scale range
#> (`geom_bar()`).

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)
#>             sets size probability
#> 1   R-HSA-162582 2601           1
#> 2  R-HSA-1430728 2145           1
#> 3   R-HSA-392499 2085           1
#> 4  R-HSA-1643685 2075           1
#> 5   R-HSA-168256 2068           1
#> 6    R-HSA-74160 1546           1
#> 7   R-HSA-597592 1472           1
#> 8  R-HSA-1266738 1397           1
#> 9    R-HSA-73857 1376           1
#> 10 R-HSA-5663205 1273           1

Session info

#> R version 4.4.1 (2024-06-14)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04 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=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
#>  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
#>  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
#>  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
#> 
#> time zone: Etc/UTC
#> tzcode source: system (glibc)
#> 
#> attached base packages:
#> [1] stats4    stats     graphics  grDevices utils     datasets  methods  
#> [8] base     
#> 
#> other attached packages:
#>  [1] reactome.db_1.88.0   forcats_1.0.0        ggplot2_3.5.1       
#>  [4] GO.db_3.19.1         org.Hs.eg.db_3.19.1  AnnotationDbi_1.67.0
#>  [7] IRanges_2.39.1       S4Vectors_0.43.1     Biobase_2.65.0      
#> [10] BiocGenerics_0.51.0  dplyr_1.1.4          BaseSet_0.9.0.9002  
#> 
#> loaded via a namespace (and not attached):
#>  [1] KEGGREST_1.45.1         gtable_0.3.5            xfun_0.46              
#>  [4] bslib_0.7.0             generics_0.1.3          vctrs_0.6.5            
#>  [7] tools_4.4.1             tibble_3.2.1            fansi_1.0.6            
#> [10] RSQLite_2.3.7           highr_0.11              blob_1.2.4             
#> [13] pkgconfig_2.0.3         desc_1.4.3              graph_1.83.0           
#> [16] lifecycle_1.0.4         GenomeInfoDbData_1.2.12 farver_2.1.2           
#> [19] compiler_4.4.1          textshaping_0.4.0       Biostrings_2.73.1      
#> [22] munsell_0.5.1           GenomeInfoDb_1.41.1     htmltools_0.5.8.1      
#> [25] sass_0.4.9              yaml_2.3.9              pkgdown_2.1.0          
#> [28] pillar_1.9.0            crayon_1.5.3            jquerylib_0.1.4        
#> [31] cachem_1.1.0            tidyselect_1.2.1        digest_0.6.36          
#> [34] labeling_0.4.3          fastmap_1.2.0           grid_4.4.1             
#> [37] colorspace_2.1-0        cli_3.6.3               magrittr_2.0.3         
#> [40] XML_3.99-0.17           utf8_1.2.4              GSEABase_1.67.0        
#> [43] withr_3.0.0             UCSC.utils_1.1.0        scales_1.3.0           
#> [46] bit64_4.0.5             rmarkdown_2.27          XVector_0.45.0         
#> [49] httr_1.4.7              bit_4.0.5               ragg_1.3.2             
#> [52] png_0.1-8               memoise_2.0.1           evaluate_0.24.0.9000   
#> [55] knitr_1.48              rlang_1.1.4             xtable_1.8-4           
#> [58] glue_1.7.0              DBI_1.2.3               annotate_1.83.0        
#> [61] jsonlite_1.8.8          R6_2.5.1                systemfonts_1.1.0      
#> [64] fs_1.6.4                zlibbioc_1.51.1

Lluís Revilla

2024 Jul 18

Initial setup

Human gene ontology

Using fuzzy values

Human pathways

Session info

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