# Enrichment Analyses

#### 2020-01-09

## Performing `Phylostratum` and `Divergence Stratum` Enrichment Analyses

`Phylostratum` and `Divergence Stratum` enrichment analyses have been performed by several studies to correlate organ or metabolic pathway evolution with the origin of organ or pathway specific genes (Sestak and Domazet-Loso, 2015).

In detail, `Phylostratum` and `Divergence Stratum` enrichment analyses can be performed analogously to Gene Ontology and Kegg enrichment analyses to study the enrichment of evolutionary age or sequence divergence in a set of selected genes against the entire genome/transcriptome. In case specific age categories are significantly over- or underrepresented in the selected gene set, assumptions or potential correlations between the evolutionary origin of a particular organ or metabolic pathway can be implied.

In this vignette we will use the data set published by Sestak and Domazet-Loso, 2015 to demonstrate how to perform enrichment analyses using `myTAI`.

## Enrichment Analyses using `PlotEnrichment()`

The `PlotEnrichment()` function implemented in `myTAI` computes and visualizes the significance of enriched (over- or underrepresented) `Phylostrata` or `Divergence Strata` within an input set of process/tissue specific genes. In detail this function takes the `Phylostratum` or `Divergence Stratum` distribution of all genes stored in the input `ExpressionSet` as background set and the `Phylostratum` or `Divergence Stratum` distribution of the specific gene set and performs a Fisher’s exact test for each `Phylostratum` or `Divergence Stratum` to quantify the statistical significance of over- or under-represented `Phylostrata` or `Divergence Strata` within the set of selected genes. In other words, the frequency distribution of `Phylostrata` or `Divergence Strata` in the complete sample is compared with the frequency distribution of `Phylostrata` or `Divergence Strata` in the set of selected genes and over- or under-representation is visualized by log-odds (or odds), where a log-odd of zero means that both frequency distributions are equal (see also Sestak and Domazet-Loso, 2015).

### Example Data Set Retrieval

Before using the `PlotEnrichment()` function, we need to download the example data set from Sestak and Domazet-Loso, 2015.

Download the `Phylostratigraphic Map` of D. rerio:

    # download the Phylostratigraphic Map of Danio rerio
    # from Sestak and Domazet-Loso, 2015
    download.file( url      = "http://mbe.oxfordjournals.org/content/suppl/2014/11/17/msu319.DC1/TableS3-2.xlsx",
                   destfile = "MBE_2015a_Drerio_PhyloMap.xlsx" )

Read the `*.xlsx` file storing the `Phylostratigraphic Map` of D. rerio and format it for the use with `myTAI`:

    # install the readxl package
    install.packages("readxl")

    # load package readxl
    library(readxl)

    # read the excel file
    DrerioPhyloMap.MBEa <- read_excel("MBE_2015a_Drerio_PhyloMap.xlsx", sheet = 1, skip = 4)

    # format Phylostratigraphic Map for use with myTAI
    Drerio.PhyloMap <- DrerioPhyloMap.MBEa[ , 1:2]

    # have a look at the final format
    head(Drerio.PhyloMap)

      Phylostrata            ZFIN_ID
    1           1 ZDB-GENE-000208-13
    2           1 ZDB-GENE-000208-17
    3           1 ZDB-GENE-000208-18
    4           1 ZDB-GENE-000208-23
    5           1  ZDB-GENE-000209-3
    6           1  ZDB-GENE-000209-4

Now, `Drerio.PhyloMap` stores the `Phylostratigraphic Map` of D. rerio which is used as background set to perform enrichment analyses with `PlotEnrichment()`.

### Enrichment Analyses

Now, the `PlotEnrichment()` function visualizes the over- and underrepresented `Phylostrata` of brain specific genes when compared with the total number of genes stored in the `Phylostratigraphic Map` of D. rerio.

    # read expression data (organ specific genes) from Sestak and Domazet-Loso, 2015
    Drerio.OrganSpecificExpression <- read_excel("MBE_2015a_Drerio_PhyloMap.xlsx", sheet = 2, skip = 3)

    # select only brain specific genes
    Drerio.Brain.Genes <- unique(na.omit(Drerio.OrganSpecificExpression[ , "brain"]))

    # visualize enriched Phylostrata of genes annotated as brain specific
    PlotEnrichment(Drerio.PhyloMap,
                   test.set     = Drerio.Brain.Genes,
                   measure      = "log-foldchange",
                   use.only.map = TRUE,
                   legendName   = "PS")

Here, the first argument is either a standard `ExpressionSet` object (in case `use.only.map = FALSE`: default) or a `Phylostratigraphic Map` or `Divergence Map` (in case `use.only.map = TRUE`; see Introduction for details). The second argument `test.set` specifies the gene ids also stored in the corresponding `ExpressionSet` or `Phylostratigraphic Map/ Divergence Map` for which enrichment shall be quantified and visualized.

To visualize the odds or log-odds of over- or underrepresented genes within the `test.set` the following procedure is performed:

- \(N_{ij}\) denotes the number of genes in group j and deriving from PS \(i\), with \(i = 1, .. , n\) and where \(j = 1\) denotes the background set and \(j = 2\) denotes the `test.set`

- \(N_{i.}\) denotes the total number of genes within PS \(i\)

- \(N_{.j}\) denotes the total number of genes within group \(j\)

- \(N_{..}\) is the total number of genes within all groups \(j\) and all PS \(i\)

- \(f_{ij}\) = \(N_{ij}\) / \(N_{..}\) and \(g_{ij}\) = \(f_{ij}\) / \(f_{.j}\) denote relative frequencies between groups

- \(f_{i.}\) denotes the between group sum of \(f_{ij}\)

The result is the fold-change value (odds; `measure = "foldchange"`) denoted as \(C_2 = g_{i2} / f_{i.}\) which is visualized above and below zero or the log fold-change value (log-odds; `measure = "log-foldchange"`), where \(log_2\)(C) = \(log_2\)(\(g_{i2}\)) - \(log_2\)(\(f_{i.}\)) which is visualized symmetrically above and below zero by `PlotEnrichment()`. Analogously, \(C_1 = g_{i1} / f_{i.}\) but is not visualized by this function.

Internally, `PlotEnrichment()` performs a Fisher’s exact test for each `Phylostratum` or `Divergence Stratum` separately, to quantify the significance of over- or under-representation of corresponding `Phylostrata` or `Divergence Strata` within the `test.set` when compared with the entire `ExpressionSet`. `PlotEnrichment()` visualizes significantly enriched (over- or underrepresented) `Phylostrata` or `Divergence Strata` with asterisks ’*’.

Notation:

- ’*’ = P-Value \(\leq\) 0.05
- ’**’ = P-Value \(\leq\) 0.005
- ’***’ = P-Value \(\leq\) 0.0005

Users will notice that when performing the `PlotEnrichment()` function, the p-values and the enrichment matrix (storing \(C_1\) and \(C_2\)) will be returned.

    PlotEnrichment(Drerio.PhyloMap,
                   test.set     = Drerio.Brain.Genes,
                   measure      = "log-foldchange",
                   use.only.map = TRUE,
                   legendName   = "PS")

    $p.values
             PS1          PS2          PS3          PS4          PS5          PS6
    8.283490e-01 8.362880e-05 6.778981e-02 1.373816e-02 7.946309e-13 6.017041e-01
             PS7          PS8          PS9         PS10         PS11         PS12
    2.185021e-03 2.281194e-03 8.943147e-01 5.699612e-01 4.717058e-02 9.367759e-01
            PS13         PS14
    3.929949e-03 1.593834e-05

    $enrichment.matrix
               BG_Set     Test_Set
    PS1  -0.001132832  0.007668216
    PS2   0.023733936 -0.172380714
    PS3  -0.040879607  0.250587496
    PS4  -0.048920465  0.294399729
    PS5  -0.114888949  0.603817643
    PS6   0.008678915 -0.060350168
    PS7  -0.062948352  0.367240944
    PS8   0.115630474 -1.206210187
    PS9  -0.007353969  0.048964218
    PS10 -0.031971192  0.200141519
    PS11  0.039742253 -0.303363314
    PS12 -0.002418079  0.016311853
    PS13  0.101449988 -0.984621732
    PS14  0.098211044 -0.938724783

In case users are only interested in the p-values of the Fisher test and the enrichment matrix without illustrating the bar plot, they can specify the `plot.bars = FALSE` argument to only retrieve the numeric results.

    # specify plot.bars = FALSE to retrieve only numeric results
    EnrichmentResult <- PlotEnrichment(Drerio.PhyloMap,
                                       test.set     = Drerio.Brain.Genes,
                                       measure      = "log-foldchange",
                                       use.only.map = TRUE,
                                       legendName   = "PS",
                                       plot.bars    = FALSE)

    # access p-values quantifying the enrichment for each Phylostratum
    EnrichmentResult$p.values

             PS1          PS2          PS3          PS4          PS5          PS6
    8.283490e-01 8.362880e-05 6.778981e-02 1.373816e-02 7.946309e-13 6.017041e-01
             PS7          PS8          PS9         PS10         PS11         PS12
    2.185021e-03 2.281194e-03 8.943147e-01 5.699612e-01 4.717058e-02 9.367759e-01
            PS13         PS14
    3.929949e-03 1.593834e-05

    # access enrichment matrix storing C_1 and C_2
    EnrichmentResult$enrichment.matrix

               BG_Set     Test_Set
    PS1  -0.001132832  0.007668216
    PS2   0.023733936 -0.172380714
    PS3  -0.040879607  0.250587496
    PS4  -0.048920465  0.294399729
    PS5  -0.114888949  0.603817643
    PS6   0.008678915 -0.060350168
    PS7  -0.062948352  0.367240944
    PS8   0.115630474 -1.206210187
    PS9  -0.007353969  0.048964218
    PS10 -0.031971192  0.200141519
    PS11  0.039742253 -0.303363314
    PS12 -0.002418079  0.016311853
    PS13  0.101449988 -0.984621732
    PS14  0.098211044 -0.938724783

### Defining the Background Set

The Fisher test which is performed inside `PlotEnrichment()` assumes that all genes stored in the input `ExpressionSet` or `Phylostratigraphic Map`/`Divergence Map` are used to define the background set for constructing the test statistic. However, since in most cases the `test.set` is an subset of the input `ExpressionSet` or `Phylostratigraphic Map`/`Divergence Map` one could also specify the `complete.bg` argument to remove all `test.set` genes from the background set when performing the Fisher test and visualization.

The following two examples allow users to compare the results when retaining all genes as background set compared with the option to remove `test.set` genes from the background set.

    # complete.bg = TRUE (default) -> retain test.set genes in background set
    PlotEnrichment(Drerio.PhyloMap,
                   test.set     = Drerio.Brain.Genes,
                   measure      = "log-foldchange",
                   complete.bg  = TRUE,
                   use.only.map = TRUE,
                   legendName   = "PS")

    # complete.bg = FALSE -> remove test.set genes from background set
    PlotEnrichment(Drerio.PhyloMap,
                   test.set     = Drerio.Brain.Genes,
                   measure      = "log-foldchange",
                   complete.bg  = FALSE,
                   use.only.map = TRUE,
                   legendName   = "PS")

Users will notice that although some p-values change, the qualitative result did not change. In border line cases however, the results might influence whether or not some `Phylostrata` or `Divergence Strata` are denoted as significantly enriched or not. So always be aware of the interpretation when retaining or removing the `test.set` from the background set, because both options are valid and have advantages and disadvantages and depend on a valid interpretation.

## Interpretation of Enrichment Results

For the D. rerio brain genes example you can see that PS4, PS5, and PS7 are significantly over-represented in the set of brain specific genes.

    PlotEnrichment(Drerio.PhyloMap,
                   test.set     = Drerio.Brain.Genes,
                   measure      = "foldchange",
                   complete.bg  = TRUE,
                   use.only.map = TRUE,
                   legendName   = "PS")

Again, we retrieve the D. rerio specific taxonomy represented by PS1-14 using the `taxonomy()` funtion (see Introduction and Taxonomy for details).

    # retrieve the taxonomy of D. rerio
    taxonomy(organism = "Danio rerio")

                        id         name    rank
    1   cellular organisms      no rank  131567
    2            Eukaryota superkingdom    2759
    3         Opisthokonta      no rank   33154
    4              Metazoa      kingdom   33208
    5            Eumetazoa      no rank    6072
    6            Bilateria      no rank   33213
    7        Deuterostomia      no rank   33511
    8             Chordata       phylum    7711
    9             Craniata    subphylum   89593
    10          Vertebrata      no rank    7742
    11       Gnathostomata      no rank    7776
    12          Teleostomi      no rank  117570
    13        Euteleostomi      no rank  117571
    14      Actinopterygii   superclass    7898
    15         Actinopteri        class  186623
    16         Neopterygii     subclass   41665
    17           Teleostei   infraclass   32443
    18 Osteoglossocephalai      no rank 1489341
    19       Clupeocephala      no rank  186625
    20           Otomorpha      no rank  186634
    21        Ostariophysi      no rank   32519
    22            Otophysa      no rank  186626
    23       Cypriniphysae   superorder  186627
    24       Cypriniformes        order    7952
    25         Cyprinoidea  superfamily   30727
    26          Cyprinidae       family    7953
    27               Danio        genus    7954
    28         Danio rerio      species    7955

Sestak and Domazet-Loso, 2015 collapsed these 28 taxonomic nodes into 14 taxonomic nodes (see `Figure 2` in Sestak and Domazet-Loso, 2015) and labelled them as phylostrata 1 to phylostrata 14, where PS1 represents `cellular organisms` and PS14 represents `D. rerio` specific genes. Based on the phylostratum categorization of Sestak and Domazet-Loso, 2015, PS4 represents `Holozoa (= Metazoa + allies)`, PS5 represents `Metazoa`, and PS7 represents `Bilateria`.

Now, the over-representation results of brain specific genes returned by `PlotEnrichment()` provide evidence, that brain specific genes might indeed have originated during the emergence of the nervous system at the metazoan-eumetazoan transition leading to the interpretation that the vertebrate brain has a step wise adaptive history where most of its extant organization was already present in the chordate ancestor as argued by Sestak and Domazet-Loso, 2015.

This example shall illustrate how the `PlotEnrichment()` function can be used to trace the evolutionary origin of tissue or process specific genes by investigating their age enrichment.

In case users have an `ExpressionSet` storing the `Phylostratigraphic Map` of D. rerio as well as an expression set, they can furthermore use the `PlotGeneSet()` function implemented in `myTAI` to visualize the expression levels of brain specific genes which have been shown to be significantly enriched in `Metazoa` specific phylostrata.

Example:

    # the best parameter setting to visualize this plot:
    # png("DrerioBrainSpecificGeneExpression.png",700,400)
    PlotGeneSet(ExpressionSet = DrerioPhyloExpressionSet,
                gene.set      = Drerio.Brain.Genes,
                plot.legend   = FALSE,
                type          = "l",
                lty           = 1,
                lwd           = 4,
                xlab          = "Ontogeny",
                ylab          = "Expression Level")

    # dev.off()

Here `DrerioPhyloExpressionSet` denotes a hypothetical `ExpressionSet` of D. rerio development.

Additionally, the `SelectGeneSet()` function allows users to obtain the `ExpresisonSet` subset of selected genes (`gene.set`) for subsequent analyses.

    # select the ExpressionSet subset of Brain specific genes
    Brain.PhyloExpressionSet <- SelectGeneSet( ExpressionSet = DrerioPhyloExpressionSet,
                                               gene.set      = Drerio.Brain.Genes )

    head(Brain.PhyloExpressionSet)

### Adjust P-values for Multiple Comparisons

In case a large number of Phylostrata or Divergence Strata is included in the input `ExpressionSet`, p-values returned by `PlotEnrichment()` should be adjusted for multiple comparisons. For this purpose `PlotEnrichment()` includes the argument `p.adjust.method`. Here, all methods implemented in `?p.adjust` can be specified:

    # adjust p-values for multiple comparisons with Benjamini & Hochberg (1995)
    PlotEnrichment(Drerio.PhyloMap,
                   test.set        = Drerio.Brain.Genes,
                   measure         = "log-foldchange",
                   complete.bg     = FALSE,
                   use.only.map    = TRUE,
                   legendName      = "PS",
                   p.adjust.method = "BH")

Please consult these reviews (Biostatistics Handbook, Gelman et al., 2008, and Slides) to decide whether or not to apply p-value adjustment to your own dataset.

## References

Sestak MS and Domazet-Loso T. Phylostratigraphic Profiles in Zebrafish Uncover Chordate Origins of the Vertebrate Brain. Mol. Biol. Evol. (2015) 32 (2): 299-312.