Contents

1 Installation

if (!requireNamespace("BiocManager", quietly = TRUE))
     install.packages("BiocManager") 
# orthogene is only available on Bioconductor>=3.14
if(BiocManager::version()<"3.14") 
  BiocManager::install(update = TRUE, ask = FALSE)

BiocManager::install("orthogene")
library(orthogene)

data("exp_mouse")
# Setting to "homologene" for the purposes of quick demonstration.
# We generally recommend using method="gprofiler" (default).
method <- "homologene"  

2 Introduction

It’s not always clear whether a dataset is using the original species gene names, human gene names, or some other species’ gene names.

infer_species takes a list/matrix/data.frame with genes and infers the species that they best match to!

For the sake of speed, the genes extracted from gene_df are tested against genomes from only the following 6 test_species by default: - human - monkey - rat - mouse - zebrafish - fly

However, you can supply your own list of test_species, which will be automatically be mapped and standardised using map_species.

3 Examples

3.1 Mouse genes

3.1.1 Infer the species

matches <- orthogene::infer_species(gene_df = exp_mouse, 
                                    method = method)
## Preparing gene_df.
## sparseMatrix format detected.
## Extracting genes from rownames.
## 15,259 genes extracted.
## Testing for gene overlap with: human
## Retrieving all genes using: homologene.
## Retrieving all organisms available in homologene.
## Mapping species name: human
## Common name mapping found for human
## 1 organism identified from search: 9606
## Gene table with 19,129 rows retrieved.
## Returning all 19,129 genes from human.
## Testing for gene overlap with: monkey
## Retrieving all genes using: homologene.
## Retrieving all organisms available in homologene.
## Mapping species name: monkey
## Common name mapping found for monkey
## 1 organism identified from search: 9544
## Gene table with 16,843 rows retrieved.
## Returning all 16,843 genes from monkey.
## Testing for gene overlap with: rat
## Retrieving all genes using: homologene.
## Retrieving all organisms available in homologene.
## Mapping species name: rat
## Common name mapping found for rat
## 1 organism identified from search: 10116
## Gene table with 20,616 rows retrieved.
## Returning all 20,616 genes from rat.
## Testing for gene overlap with: mouse
## Retrieving all genes using: homologene.
## Retrieving all organisms available in homologene.
## Mapping species name: mouse
## Common name mapping found for mouse
## 1 organism identified from search: 10090
## Gene table with 21,207 rows retrieved.
## Returning all 21,207 genes from mouse.
## Testing for gene overlap with: zebrafish
## Retrieving all genes using: homologene.
## Retrieving all organisms available in homologene.
## Mapping species name: zebrafish
## Common name mapping found for zebrafish
## 1 organism identified from search: 7955
## Gene table with 20,897 rows retrieved.
## Returning all 20,897 genes from zebrafish.
## Testing for gene overlap with: fly
## Retrieving all genes using: homologene.
## Retrieving all organisms available in homologene.
## Mapping species name: fly
## Common name mapping found for fly
## 1 organism identified from search: 7227
## Gene table with 8,438 rows retrieved.
## Returning all 8,438 genes from fly.
## Top match:
##   - species: mouse 
##   - percent_match: 92%

3.2 Rat genes

3.2.1 Create example data

To create an example dataset, turn the gene names into rat genes.

exp_rat <- orthogene::convert_orthologs(gene_df = exp_mouse, 
                                        input_species = "mouse", 
                                        output_species = "rat",
                                        method = method)

3.2.2 Infer the species

matches <- orthogene::infer_species(gene_df = exp_rat, 
                                    method = method)

3.3 Human genes

3.3.1 Create example data

To create an example dataset, turn the gene names into human genes.

exp_human <- orthogene::convert_orthologs(gene_df = exp_mouse, 
                                          input_species = "mouse", 
                                          output_species = "human",
                                          method = method)

3.3.2 Infer the species

matches <- orthogene::infer_species(gene_df = exp_human, 
                                    method = method)

4 Additional test_species

You can even supply test_species with the name of one of the R packages that orthogene gets orthologs from. This will test against all species available in that particular R package.

For example, by setting test_species="homologene" we automatically test for % gene matches in each of the 20+ species available in homologene.

matches <- orthogene::infer_species(gene_df = exp_human, 
                                    test_species = method, 
                                    method = method)

5 Session Info

utils::sessionInfo()
R version 4.2.1 (2022-06-23)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Mojave 10.14.6

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib

locale:
[1] C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] orthogene_1.2.1  BiocStyle_2.24.0

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.9                ape_5.6-2                
 [3] lattice_0.20-45           tidyr_1.2.1              
 [5] assertthat_0.2.1          digest_0.6.29            
 [7] utf8_1.2.2                R6_2.5.1                 
 [9] backports_1.4.1           evaluate_0.16            
[11] highr_0.9                 httr_1.4.4               
[13] ggplot2_3.3.6             pillar_1.8.1             
[15] ggfun_0.0.7               yulab.utils_0.0.5        
[17] rlang_1.0.6               lazyeval_0.2.2           
[19] data.table_1.14.2         car_3.1-0                
[21] jquerylib_0.1.4           magick_2.7.3             
[23] Matrix_1.5-1              rmarkdown_2.16           
[25] labeling_0.4.2            stringr_1.4.1            
[27] htmlwidgets_1.5.4         munsell_0.5.0            
[29] broom_1.0.1               gprofiler2_0.2.1         
[31] compiler_4.2.1            xfun_0.33                
[33] pkgconfig_2.0.3           gridGraphics_0.5-1       
[35] htmltools_0.5.3           tidyselect_1.1.2         
[37] tibble_3.1.8              bookdown_0.29            
[39] viridisLite_0.4.1         fansi_1.0.3              
[41] dplyr_1.0.10              ggpubr_0.4.0             
[43] grid_4.2.1                nlme_3.1-159             
[45] jsonlite_1.8.2            gtable_0.3.1             
[47] lifecycle_1.0.2           DBI_1.1.3                
[49] magrittr_2.0.3            scales_1.2.1             
[51] tidytree_0.4.1            cli_3.4.1                
[53] stringi_1.7.8             cachem_1.0.6             
[55] carData_3.0-5             farver_2.1.1             
[57] ggsignif_0.6.3            ggtree_3.4.4             
[59] bslib_0.4.0               ellipsis_0.3.2           
[61] generics_0.1.3            vctrs_0.4.2              
[63] treeio_1.20.2             tools_4.2.1              
[65] homologene_1.4.68.19.3.27 ggplotify_0.1.0          
[67] glue_1.6.2                purrr_0.3.4              
[69] abind_1.4-5               parallel_4.2.1           
[71] fastmap_1.1.0             yaml_2.3.5               
[73] babelgene_22.9            colorspace_2.0-3         
[75] BiocManager_1.30.18       rstatix_0.7.0            
[77] aplot_0.1.7               plotly_4.10.0            
[79] knitr_1.40                patchwork_1.1.2          
[81] sass_0.4.2