In almeidasilvaf/cogeqc: Systematic quality checks on comparative genomics analyses

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Introduction

The identification of groups of homologous genes within and across species is a powerful tool for evolutionary genomics. The most widely used tools to identify orthogroups (i.e., groups of orthologous genes) are OrthoFinder [@emms2019orthofinder] and OrthoMCL [@li2003orthomcl]. However, these tools generate different results depending on the parameters used, such as mcl inflation parameter, E-value, maximum number of hits, and others. Here, we propose a protein domain-aware assessment of orthogroup inference. The goal is to maximize the percentage of shared protein domains for genes in the same orthogroup.

Installation

if(!requireNamespace('BiocManager', quietly = TRUE))
  install.packages('BiocManager')
BiocManager::install("cogeqc")

# Load package after installation
library(cogeqc)

Data description

Here, we will use orthogroups from the PLAZA 5.0 database [@van2021plaza], inferred with OrthoFinder [@emms2019orthofinder]. For the purpose of demonstration, the complete dataset was filtered to only keep orthogroups for the Brassicaceae species Arabidopsis thaliana and Brassica oleraceae. Interpro domain annotations were also retrieved from PLAZA 5.0.

# Orthogroups for Arabidopsis thaliana and Brassica oleraceae
data(og)
head(og)

# Interpro domain annotations
data(interpro_ath)
data(interpro_bol)

head(interpro_ath)
head(interpro_bol)

If you infer orthogroups with OrthoFinder, you can read and parse the output file Orthogroups.tsv with the function read_orthogroups(). For example:

# Path to the Orthogroups.tsv file created by OrthoFinder
og_file <- system.file("extdata", "Orthogroups.tsv.gz", package = "cogeqc") 

# Read and parse file
orthogroups <- read_orthogroups(og_file)
head(orthogroups)

Assessing orthogroups

In cogeqc, you can assess orthogroup inference with either a protein domain-based approach or a reference-based approach. Both approaches are described below.

Protein domain-based orthogroup assessment

The protein domain-based assessment of orthogroups is based on the formula below:

$$ \begin{aligned} Scores &= Homogeneity - Dispersal \ \end{aligned} $$

The $homogeneity$ term is the mean Sorensen-Dice index for all pairwise combinations of genes in an orthogroup. The Sorensen-Dice index measures how similar two genes are, and it ranges from 0 to 1, with 0 meaning that a gene pair does not share any protein domain, and 1 meaning that it shares all protein domains. In a formal definition:

$$ \begin{aligned} Homogeneity &= \frac{1}{N_{pairs}} \sum_{i=1}^{N_{pairs}} SDI_{i} \ \ SDI(A,B) &= \frac{2 \left| A \cap B \right|}{ \left|A \right| + \left| B \right|} \end{aligned} $$

where A and B are the set of protein domains associated to genes A and B. This way, if all genes in an orthogroup have the same protein domains, it will have $homogeneity = 1$. If each gene has a different protein domain, the orthogroup will have $homogeneity = 0$. If only some gene pairs share the same domain, $homogeneity$ will be somewhere between 0 and 1.

The $dispersal$ term aims to correct for overclustering (i.e., orthogroup assignments that break "true" gene families into an artificially large number of smaller subfamilies), and it is the relative frequency of dispersed domains (i.e., domains that are split into multiple orthogroups). This term penalizes orthogroup assignments where the same protein domains appears in multiple orthogroups. As orthogroups represent groups of genes that evolved from a common ancestor, a protein domain being present in multiple orthogroups indicates that this domain evolved multiple times in an independent way, which is not reasonable from a phylogenetic point of view, despite convergent evolution.

To calculate scores for each orthogroup, you can use the function assess_orthogroups(). This function takes as input a list of annotation data frames[^1] and an orthogroups data frame, and returns the relative homogeneity scores of each orthogroup for each species. If you do not want to calculate scores separately by species, you can also use the function calculate_H(). Note that if you don't want to take the dispersal into account, you can set correct_overclustering = FALSE.

[^1]: NOTE: The names of the list elements must match the species abbreviations in the column Species of the orthogroups data frame. For instance, if your orthogroups data frame contains the species Ath and Bol, the data frames in the annotation list must be named Ath and Bol (not necessarily in that order, but with these exact names).

# Create a list of annotation data frames
annotation <- list(Ath = interpro_ath, Bol = interpro_bol)
str(annotation) # This is what the list must look like

og_assessment <- assess_orthogroups(og, annotation)
head(og_assessment)

Now, we can calculate the mean score for this orthogroup inference.

mean(og_assessment$Mean_score)

Ideally, to have a reliable orthogroup inference, you should be able to run OrthoFinder with multiple combinations of parameters and assess each inference with assess_orthogroups(). The inference with the highest mean homonegeneity will be the best.[^2]

[^2]: Friendly tip: if you want to calculate homogeneity scores using a single species as a proxy (your orthogroups data frame will have only one species), you can use the function calculate_H().

Reference-based orthogroup assessment

In some cases, you may want to compare your orthogroup inference to a reference orthogroup inference. To do that, you can use the function compare_orthogroups(). For example, let's simulate a different orthogroup inference by shuffling some rows of the og data frame and compare it to the original data frame.

set.seed(123)

# Subset the top 5000 rows for demonstration purposes
og_subset <- og[1:5000, ]
ref <- og_subset

# Shuffle 100 genes to simulate a test set
idx_shuffle <- sample(seq_len(nrow(og_subset)), 100, replace = FALSE)
test <- og_subset
test$Gene[idx_shuffle] <- sample(
  test$Gene[idx_shuffle], size = length(idx_shuffle), replace = FALSE
)

# Compare test set to reference set
comparison <- compare_orthogroups(ref, test)
head(comparison)

# Calculating percentage of preservation
preserved <- sum(comparison$Preserved) / length(comparison$Preserved)
preserved

As we can see, r paste0(round(preserved * 100, 2), "%") of the orthogroups in the reference data set are preserved in the shuffled data set.

Visualizing summary statistics

Now that you have identified the best combination of parameters for your orthogroup inference, you can visually explore some of its summary statistics. OrthoFinder automatically saves summary statistics in a directory named Comparative_Genomics_Statistics. You can parse this directory in a list of summary statistics with the function read_orthofinder_stats(). To demonstrate it, let's read the output of OrthoFinder's example with model species.

stats_dir <- system.file("extdata", package = "cogeqc")
ortho_stats <- read_orthofinder_stats(stats_dir)
ortho_stats

Now, we can use this list to visually explore summary statistics.

Species tree

To start, one would usually want to look at the species tree to detect possible issues that would compromise the accuracy of orthologs detection. The tree file can be easily read with treeio::read.tree().

data(tree)
plot_species_tree(tree)

You can also include the number of gene duplications in each node.

plot_species_tree(tree, stats_list = ortho_stats)

Species-specific duplications

The species tree above shows duplications per node, but it does not show species-duplications. To visualize that, you can use the function plot_duplications().

plot_duplications(ortho_stats)

Genes in orthogroups

Visualizing the percentage of genes in orthogroups is particularly useful for quality check, since one would usually expect a large percentage of genes in orthogroups, unless there is a very distant species in OrthoFinder's input proteome data.

plot_genes_in_ogs(ortho_stats)

Species-specific orthogroups

To visualize the number of species-specific orthogroups, use the function plot_species_specific_ogs(). This plot can reveal a unique gene repertoire of a particular species if it has a large number of species-specific OGs as compared to the other ones.

plot_species_specific_ogs(ortho_stats)

All in one

To get a complete picture of OrthoFinder results, you can combine all plots together with plot_orthofinder_stats(), a wrapper that integrates all previously demonstrated plotting functions.

plot_orthofinder_stats(
  tree, 
  xlim = c(-0.1, 2),
  stats_list = ortho_stats
)

Orthogroup overlap

You can also visualize a heatmap of pairwise orthogroup overlap across species with plot_og_overlap().

plot_og_overlap(ortho_stats)

Orthogroup size per species

If you want to take a look at the distribution of OG sizes for each species, you can use the function plot_og_sizes. If you have many extreme values and want to visualize the shape of the distribution in a better way, you can log transform the OG sizes (with log = TRUE) and/or remove OG larger than a particular threshold (with max_size = 100, for example).

plot_og_sizes(og) 
plot_og_sizes(og, log = TRUE) # natural logarithm scale
plot_og_sizes(og, max_size = 100) # only OGs with <= 100 genes

Session information {.unnumbered}

This document was created under the following conditions:

sessioninfo::session_info()

References {.unnumbered}

almeidasilvaf/cogeqc documentation built on Jan. 29, 2024, 7:20 a.m.