Cancer Mutation Analysis and Visualization Suite.
The CancerMutAnalyzer is an R package designed to streamline and
enhance the workflow for analyzing mutation data in cancer research. The
package focuses on processing, visualizing, and analyzing mutation
frequencies and genomic contexts, particularly targeting single
nucleotide polymorphisms (SNPs) and other mutation types within cancer
datasets. By providing functions for data extraction, filtering,
sequence analysis, and visualizations (such as heatmaps of mutation
frequencies), the package enables researchers to quickly identify
mutation patterns and genomic signatures associated with different
cancer types or tumor samples.
This package adds to the
current bioinformatics workflow by simplifying mutation analysis tasks
that typically require multiple steps across various software. By
centralizing these steps in one package, researchers can conduct
analyses more efficiently, reduce the risk of errors from file
conversions, and gain insights through streamlined visualizations. One
unique feature of this package is the ability to examine the local
nucleotide context surrounding mutations, which can be used to detect
mutational hotspots or enrich mutation data with GC content or
sequence-based patterns. Additionally, it addresses common issues such
as data formatting, base filtering, and customized visualization for
specific mutation characteristics, making it a comprehensive and
user-friendly tool for cancer genomics.
The
CancerMutAnalyzer package was developed using
R version 4.4.1 (2024-06-14 ucrt), Platform: x86_64-w64-mingw32/x64
and Running under: Windows 11 x64 (build 22631).
To install the latest version of the package:
install.packages("devtools")
library("devtools")
devtools::install_github("martien2kk/CancerMutAnalyzer", build_vignettes = TRUE)
library("CancerMutAnalyzer")To run the Shiny app:
runCancerMutAnalyzer() # not for Assessment 4; only for Assessment 5ls("package:CancerMutAnalyzer")
data(package = "CancerMutAnalyzer")
browseVignettes("CancerMutAnalyzer")CancerMutAnalyzer contains 6 functions.
-
extractMutationData: allows users to specify and extract specific columns from a mutation dataset in Mutation Annotation Format (MAF) or tabular format. It has default settings for commonly used columns but is customizable, so users can focus on relevant mutation details.
-
filterMutations: provides flexible filtering based on user-specified conditions for any column. This allows users to subset mutation data based on exact values or numeric ranges for certain columns, such as specific genes or chromosomal regions.
-
extractMutationSequences: retrieves nucleotide sequences surrounding mutation sites from the hg19 genome based on specified genomic coordinates in a data frame. Users can customize the length of the sequence extracted by adjusting the padding parameter, with the default setting providing a trinucleotide sequence centered around each mutation.
-
visualizeMutationFrequencyBar: generates a bar plot to display the frequency of mutations based on a specified column in a mutation dataset. This function is ideal for visualizing the distribution of mutations by a single categorical variable, such as chromosome or variant type.
-
visualizeMutationFrequencyHeatmap: creates a heatmap that visualizes mutation frequency based on two specified columns, such as Reference_Allele and Tumor_Seq_Allele2. It only includes rows where both columns contain nucleotide bases (A, T, C, G). This function is particularly useful for examining mutation patterns between pairs of alleles, highlighting high-frequency mutations across different nucleotide pairs.
-
calculateGCContent: calculates the GC content (the percentage of G and C bases) for each sequence around a mutation site.
The package also contains two cancer mutation datasets, called UCS.mutations and filteredUCSFirst100SNP. Refer to package vignettes for more details. An overview of the package is illustrated below.
The author of the package is Keren Zhang. The author wrote the
extractMutationData function, which extracts specified columns from a
mutation dataset, returning key mutation details such as chromosome,
start and end positions, variant type, and allele information. This
function relies on dplyr functions for data manipulation and selection.
The extractMutationSequences function was also developed by
the author and extracts nucleotide sequences surrounding mutation sites
based on genomic coordinates. This function utilizes the
BSgenome.Hsapiens.UCSC.hg19 and GenomicRanges packages to access the
hg19 genome and create genomic ranges for accurate sequence extraction.
Additionally, the calculateGCContent function, written by the
author, calculates the GC content of extracted sequences to assess the
percentage of G and C bases. This function uses the Biostrings package
for efficient sequence handling and base frequency calculation, allowing
for streamlined GC content analysis.
The
visualizeMutationFrequencyBar and visualizeMutationFrequencyHeatmap
functions generate visualizations to explore mutation data. The bar plot
function visualizes mutation frequency by a single column, while the
heatmap function visualizes mutation frequency by two columns (e.g.,
Reference_Allele and Tumor_Seq_Allele2). Both functions use ggplot2 for
visualization and rely on dplyr for data preparation.
Generative AI tools, OpenAI’s ChatGPT, were used to provide suggestions
for brainstorming, debugging, and structuring the Roxygen documentation.
-
Attali D (2021). shinyjs: Easily Improve the User Experience of Your Shiny Apps in Seconds. R package version 2.1.0, https://CRAN.R-project.org/package=shinyjs.
-
Carey V, Harshfield B, Falcon S, Arora S, Shepherd L (2024). biocViews: Categorized views of R package repositories. doi:10.18129/B9.bioc.biocViews https://doi.org/10.18129/B9.bioc.biocViews, R package version 1.72.0, https://bioconductor.org/packages/biocViews
-
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 1.9.1.9000, commit 501b012b2b82c0c468dc921ff0a3d9a682c20623, https://github.com/rstudio/shiny
-
Kosinski M (2024). RTCGA.mutations: Mutations datasets from The Cancer Genome Atlas Project. doi:10.18129/B9.bioc.RTCGA.mutations https://doi.org/10.18129/B9.bioc.RTCGA.mutations, R package version 20151101.34.0, https://bioconductor.org/packages/RTCGA.mutations.
-
Lawrence M, Huber W, Pag`es H, Aboyoun P, Carlson M, et al. (2013) Software for Computing and Annotating Genomic Ranges. PLoS Comput Biol 9(8): e1003118. doi:10.1371/journal.pcbi.1003118
-
OpenAI. (2024). ChatGPT (Version 3.5). Retrieved from https://chat.openai.com/chat
-
Pagès H, Lawrence M, Aboyoun P (2024). S4Vectors: Foundation of vector-like and list-like containers in Bioconductor. doi:10.18129/B9.bioc.S4Vectors https://doi.org/10.18129/B9.bioc.S4Vectors, R package version 0.42.1, https://bioconductor.org/packages/S4Vectors.
-
R Core Team (2024). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.
-
Silva A. (2019). GitHub - anjalisilva/TestingPackage: R Package Illustrating Components of an R package for BCB410H - Applied Bioinformatics (2019-2023), University of Toronto, Canada. GitHub. https://github.com/anjalisilva/TestingPackage
-
Team TBD (2020). BSgenome.Hsapiens.UCSC.hg19: Full genome sequences for Homo sapiens (UCSC version hg19, based on GRCh37.p13). R package version 1.4.3.
-
Wickham, H., François, R., Henry, L., Müller, K., & Vaughan, D. (2023). dplyr: A Grammar of Data Manipulation (R package version 1.1.4). Retrieved from https://CRAN.R-project.org/package=dplyr
-
Wickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis. New York, NY: Springer-Verlag.
-
Wickham, H. and J. Bryan (2019). R Packages (2nd edition). Newton, Massachusetts: O’Reilly Media. https://r-pkgs.org/
This package was developed as part of an assessment for 2024 BCB410H:
Applied Bioinformatics course at the University of Toronto, Toronto,
CANADA. CancerMutAnalyzer welcomes issues, enhancement requests, and
other contributions. To submit an issue, use the GitHub
issues. Many
thanks to those who provided feedback to improve this package.
The sessionInfo used to develop this package is listed below:
R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 22631)
Matrix products: default
locale:
[1] LC_COLLATE=English_Canada.utf8 LC_CTYPE=English_Canada.utf8 LC_MONETARY=English_Canada.utf8
[4] LC_NUMERIC=C LC_TIME=English_Canada.utf8
time zone: America/Toronto
tzcode source: internal
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods base
other attached packages:
[1] CancerMutAnalyzer_0.1.0 knitr_1.48 Biostrings_2.72.1
[4] XVector_0.44.0 testthat_3.2.1.1 ggplot2_3.5.1
[7] GenomicRanges_1.56.2 GenomeInfoDb_1.40.1 IRanges_2.38.1
[10] S4Vectors_0.42.1 BiocGenerics_0.50.0 dplyr_1.1.4
[13] RTCGA.mutations_20151101.34.0 RTCGA_1.34.0 roxygen2_7.3.2
loaded via a namespace (and not attached):
[1] rstudioapi_0.17.1 jsonlite_1.8.9 magrittr_2.0.3
[4] farver_2.1.2 rmarkdown_2.29 fs_1.6.5
[7] BiocIO_1.14.0 zlibbioc_1.50.0 vctrs_0.6.5
[10] memoise_2.0.1 Rsamtools_2.20.0 RCurl_1.98-1.16
[13] rstatix_0.7.2 htmltools_0.5.8.1 S4Arrays_1.4.1
[16] usethis_3.0.0 curl_5.2.3 broom_1.0.7
[19] SparseArray_1.4.8 Formula_1.2-5 htmlwidgets_1.6.4
[22] desc_1.4.3 zoo_1.8-12 cachem_1.1.0
[25] commonmark_1.9.2 GenomicAlignments_1.40.0 whisker_0.4.1
[28] mime_0.12 lifecycle_1.0.4 pkgconfig_2.0.3
[31] Matrix_1.7-0 R6_2.5.1 fastmap_1.2.0
[34] GenomeInfoDbData_1.2.12 MatrixGenerics_1.16.0 shiny_1.9.1
[37] digest_0.6.37 colorspace_2.1-1 ps_1.8.1
[40] rprojroot_2.0.4 pkgload_1.4.0 ggpubr_0.6.0
[43] labeling_0.4.3 fansi_1.0.6 km.ci_0.5-6
[46] httr_1.4.7 abind_1.4-8 compiler_4.4.1
[49] remotes_2.5.0 withr_3.0.2 backports_1.5.0
[52] BiocParallel_1.38.0 carData_3.0-5 viridis_0.6.5
[55] pkgbuild_1.4.5 ggsignif_0.6.4 sessioninfo_1.2.2
[58] DelayedArray_0.30.1 rjson_0.2.23 BSgenome.Hsapiens.UCSC.hg19_1.4.3
[61] tools_4.4.1 httpuv_1.6.15 glue_1.8.0
[64] callr_3.7.6 restfulr_0.0.15 promises_1.3.0
[67] grid_4.4.1 generics_0.1.3 gtable_0.3.6
[70] BSgenome_1.72.0 KMsurv_0.1-5 tidyr_1.3.1
[73] survminer_0.5.0 data.table_1.16.2 xml2_1.3.6
[76] car_3.1-3 utf8_1.2.4 pillar_1.9.0
[79] stringr_1.5.1 later_1.3.2 splines_4.4.1
[82] lattice_0.22-6 survival_3.6-4 rtracklayer_1.64.0
[85] tidyselect_1.2.1 miniUI_0.1.1.1 gridExtra_2.3
[88] SummarizedExperiment_1.34.0 xfun_0.49 Biobase_2.64.0
[91] devtools_2.4.5 brio_1.1.5 matrixStats_1.4.1
[94] stringi_1.8.4 UCSC.utils_1.0.0 yaml_2.3.10
[97] evaluate_1.0.1 codetools_0.2-20 tibble_3.2.1
[100] BiocManager_1.30.25 cli_3.6.3 xtable_1.8-4
[103] processx_3.8.4 munsell_0.5.1 survMisc_0.5.6
[106] Rcpp_1.0.13-1 XML_3.99-0.17 parallel_4.4.1
[109] ellipsis_0.3.2 assertthat_0.2.1 profvis_0.4.0
[112] urlchecker_1.0.1 bitops_1.0-9 viridisLite_0.4.2
[115] ggthemes_5.1.0 scales_1.3.0 purrr_1.0.2
[118] crayon_1.5.3 rlang_1.1.4 rvest_1.0.4
[121] waldo_0.6.0