Filtering Tool to Reduce Population Structure Signal in Bacterial Genotype-Phenotype Analysis Data
BacPoSSR (Bacterial Population Structure Signal Reducer) is a tool to
help users filter their single nucleotide polymorphism (SNP) or k-mer
data to reduce population structure signal in the dataset before it is
used in bacterial genotype-phenotype association or predictive studies.
It aims to improve the flow of these studies, especially methods that
use machine learning models to compute these mappings because scientists
must build in their own methods to account for population structure
within their models, unlike more established genome-wide association
studies (GWAS) tools such as pyseer (Lees et al., 2018). When population
structure is not accounted for, association/predictive studies can
easily lead to false positives by inferring phenotypes from population
structure (Karlsen et al., 2023).
This package was developed in the following environment:
R Version:
4.4.1 (2024-06-14) – “Race for Your Life”
Platform: macOS Sonoma
14.4
To install the latest version of the package:
install.packages("devtools")
library("devtools")
devtools::install_github("izumiando/BacPoSSR", build_vignettes = TRUE)
library("BacPoSSR")To run the Shiny app:
runBacPoSSR()ls("package:BacPoSSR") # lists the functions in BacPoSSR
browseVignettes("BacPoSSR") # view all vignettesBacPoSSR contains 3 functions. For further details on how to use each
function, please refer to the vignette and help documentation.
- plotMultCompAnalysis : use this function to conduct Multiple
Component Analysis and plots its results to examine the population
structure signal of your dataset.
- filterProcAnalysis : use this
function to filter your feature matrix using Procrustes Analysis.
-
filterGenRelatedness : use this function to filter your feature matrix
based on the features’ genetic relatedness. Using SNPs data is
recommended for this function.
BacPoSSR can be contextualized
in the GWAS / ML predictive studies pipeline as illustrated in the image
below.
The author of BacPoSSR is Izumi Ando. BacPoSSR contains three functions,
all of which were written by the author while making use of functions
from third-party R packages. The plotMultCompAnalysis function formats
the input data (feature matrix and group assignments) and utilizes the
MCA function from the FactoMineR package to conduct Multiple
Component Analysis, the fviz_mca_ind function from the factoextra
package to plot the results, and ggsave from the ggplot2 package to
save the image. The filterProcAnalysis function filters the input
feature matrix based on the Procrustes distances of each feature,
computed by conducting the analysis on the features against the
phenotype data. The function utilizes the procrustes function from the
vegan package to conduct the Procrustes Analysis. The code to
computate of the distances and to do the actual filtering was written by
the author. The filterGenRelatedness function filters the input
feature matrix based on the features’ genetic relatedness. This function
does not rely on any third party packages. ChatGPT developed by OpenAI
was used in the creation of this package only for the purpose of
debugging the code.
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Chang W, Cheng J, Allaire J, Sievert C, Schloerke B, Xie Y, Allen J, McPherson J, Dipert A, Borges B (2024). shiny: Web Application Framework for R. R package version 1.9.1, https://CRAN.R-project.org/package=shiny.
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Karlsen, S. T., Rau, M. H., Sánchez, B. J., Jensen, K., & Zeidan, A. A. (2023). From genotype to phenotype: Computational approaches for inferring microbial traits relevant to the food industry. FEMS Microbiology Reviews, 47(4), fuad030. https://doi.org/10.1093/femsre/fuad030
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Lê S, Josse J, Husson F (2008). “FactoMineR: A Package for Multivariate Analysis.” Journal of Statistical Software, 25(1), 1–18. doi:10.18637/jss.v025.i01.
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Lees, J. A., Galardini, M., Bentley, S. D., Weiser, J. N., & Corander, J. (2018). pyseer: A comprehensive tool for microbial pangenome-wide association studies. Bioinformatics, 34(24), 4310–4312. https://doi.org/10.1093/bioinformatics/bty539
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Oksanen J, Simpson G, Blanchet F, Kindt R, Legendre P, Minchin P, O’Hara R, Solymos P, Stevens M, Szoecs E, Wagner H, Barbour M, Bedward M, Bolker B, Borcard D, Carvalho G, Chirico M, De Caceres M, Durand S, Evangelista H, FitzJohn R, Friendly M, Furneaux B, Hannigan G, Hill M, Lahti L, McGlinn D, Ouellette M, Ribeiro Cunha E, Smith T, Stier A, Ter Braak C, Weedon J (2024). vegan: Community Ecology Package. R package version 2.6-8, https://CRAN.R-project.org/package=vegan.
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R Core Team (2024). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.
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Sievert C, Cheng J, Aden-Buie G (2024). bslib: Custom ‘Bootstrap’ ‘Sass’ Themes for ‘shiny’ and ‘rmarkdown’. R package version 0.8.0, https://CRAN.R-project.org/package=bslib.
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Wickham H (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. ISBN 978-3-319-24277-4, https://ggplot2.tidyverse.org.
This package was developed as a part of an assessment for 2024 BCB410H: Applied Bioinformatics course at the University of Toronto, Toronto, CANADA. BacPoSSR welcomes issues, enhancement requests, and other contributions. To submit an issue, use GitHub issues.