diff --git a/paper/jats/paper.jats b/paper/jats/paper.jats index 6ff3960..4b3a50d 100644 --- a/paper/jats/paper.jats +++ b/paper/jats/paper.jats @@ -95,20 +95,22 @@ a Creative Commons Attribution 4.0 International License (CC BY Summary -

The ggoncoplot R package generates interactive oncoplots (also - called oncoprints) to visualize mutational patterns across patient - cancer cohorts +

The ggoncoplot R package generates interactive oncoplots to + visualize mutational patterns across patient cancer cohorts ([fig:oncoplot]). - Oncoplots reveal patterns of gene co-mutation and include marginal - plots that indicate co-occurrence of gene mutations and tumour - features. It is useful to relate gene mutation patterns seen in an - oncoplot to patterns seen in other plot types, including gene - expression t-SNE plots or methylation UMAPs. There are, however, no - existing oncoplot-generating R packages that support dynamic data - linkage between different plots. To addresses this gap and enable - rapid exploration of a variety of data types we constructed the - ggoncoplot package for the production of oncoplots that are easily - integrated with custom visualisations and that support synchronised + Oncoplots, also called oncoprints, reveal patterns of gene co-mutation + and include marginal plots that indicate co-occurrence of gene + mutations with tumour and clinical features. It is useful to relate + gene mutation patterns seen in an oncoplot to patterns in other plot + types, including gene expression t-SNE plots or methylation UMAPs. The + simplest and most intuitive approach to examining such relations is to + link plots dynamically such that samples selected in an oncoplot can + be highlighted in other plots. There are, however, no existing + oncoplot-generating R packages that support dynamic data linkage + between different plots. To address this gap and enable rapid + exploration of a variety of data types we constructed the ggoncoplot + package for the production of oncoplots that are easily integrated + with custom visualisations and that support synchronised data-selections across plots ([fig:multimodal_selection]). ggoncoplot is available on GitHub at @@ -150,44 +152,50 @@ a Creative Commons Attribution 4.0 International License (CC BY Statement of Need -

Oncoplots are highly effectively for visualising mutation data in +

Oncoplots are highly effective for visualising mutation data in cancer cohorts but are challenging to generate with the major R plotting systems (base, lattice, or ggplot2) due to their algorithmic - and graphical complexity. Simplifying the process would make oncoplots - more accessible to researchers. Packages like ComplexHeatmap + and graphical complexity. Simplifying the process of generating + oncoplots would make them more accessible to researchers. Existing + packages including ComplexHeatmap (Gu, 2022), maftools (Mayakonda et al., 2018), and genVisR (Skidmore et al., 2016) all make static oncoplots easier to create, but - there is still a significant unmet need for an easy method of creating - oncoplots with the following features:

+ there is still a significant unmet need for user-friendly method of + creating oncoplots with the following features:

Interactive plots: Customizable tooltips, cross-selection of samples across different plots, and - auto-copying of sample identifiers on click.

+ auto-copying of sample identifiers on click. This enables + exploration of trends in multiomic datasets as shown in + [fig:multimodal_selection].

Support for tidy datasets: Compatibility with tidy, tabular mutation-level formats (MAF files or relational - databases), typical of cancer cohort datasets.

+ databases), typical of cancer cohort datasets. This greatly + improves the range of datasets that can be quickly and easily + visualised in an oncoplot.

 -

Auto colouring: Automatic selection of colour - palettes for datasets where consequence annotations are aligned - with standard variant effect dictionaries (PAVE, SO, or MAF).

+

Auto-colouring: Automatic selection of colour + palettes for datasets where the consequence annotations are + aligned with standard variant effect dictionaries (PAVE, SO, or + MAF).

Versatility: The ability to visualize entities - other than gene mutations, including noncoding features (e.g., - enhancers) and non-genomic entities (e.g., microbial presence in - microbiome datasets).

+ other than gene mutations, such as noncoding features (e.g., + promoter or enhancer mutations) and non-genomic entities (e.g., + microbial presence in microbiome datasets).

 -

We developed ggoncoplot as the first R package that addresses all - these challenges simultaneously +

We developed ggoncoplot as the first R package to address all these + challenges together ([fig:comparison]). Examples of all key features are available in the ggoncoplot diff --git a/paper/paper.docx b/paper/paper.docx index 963f1d0..1b97b4a 100644 Binary files a/paper/paper.docx and b/paper/paper.docx differ diff --git a/paper/paper.md b/paper/paper.md index be03e66..f956b5a 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -30,7 +30,7 @@ affiliations: # Summary -The ggoncoplot R package generates interactive oncoplots (also called oncoprints) to visualize mutational patterns across patient cancer cohorts (\autoref{fig:oncoplot}). Oncoplots reveal patterns of gene co-mutation and include marginal plots that indicate co-occurrence of gene mutations and tumour features. It is useful to relate gene mutation patterns seen in an oncoplot to patterns seen in other plot types, including gene expression t-SNE plots or methylation UMAPs. There are, however, no existing oncoplot-generating R packages that support dynamic data linkage between different plots. To addresses this gap and enable rapid exploration of a variety of data types we constructed the ggoncoplot package for the production of oncoplots that are easily integrated with custom visualisations and that support synchronised data-selections across plots (\autoref{fig:multimodal_selection}). ggoncoplot is available on GitHub at . +The ggoncoplot R package generates interactive oncoplots to visualize mutational patterns across patient cancer cohorts (\autoref{fig:oncoplot}). Oncoplots, also called oncoprints, reveal patterns of gene co-mutation and include marginal plots that indicate co-occurrence of gene mutations with tumour and clinical features. It is useful to relate gene mutation patterns seen in an oncoplot to patterns in other plot types, including gene expression t-SNE plots or methylation UMAPs. The simplest and most intuitive approach to examining such relations is to link plots dynamically such that samples selected in an oncoplot can be highlighted in other plots. There are, however, no existing oncoplot-generating R packages that support dynamic data linkage between different plots. To address this gap and enable rapid exploration of a variety of data types we constructed the ggoncoplot package for the production of oncoplots that are easily integrated with custom visualisations and that support synchronised data-selections across plots (\autoref{fig:multimodal_selection}). ggoncoplot is available on GitHub at . ![ggoncoplot output visualising mutational trends in the TCGA breast carcinoma cohort. Individual patient samples are plotted on the x-axis, hierarchically sorted so that samples with the most frequent gene mutations appear on the leftmost side. The plot indicates that PIK3CA is the most frequently mutated gene, followed by TP53. Marginal plots indicate the total number of mutations per sample (top), and the number of samples showing mutations in each gene, coloured by mutation type (right). A range of clinical features, including progesterone and estrogen receptor status are shown on the marginal plot at the bottom. A detailed description of the ggoncoplot sorting algorithm is available [here](https://selkamand.github.io/ggoncoplot/articles/sorting_algorithm.html). \label{fig:oncoplot}](oncoplot.pdf) @@ -41,18 +41,18 @@ The ggoncoplot R package generates interactive oncoplots (also called oncoprints # Statement of Need -Oncoplots are highly effectively for visualising mutation data in cancer cohorts but are challenging to generate with the major R plotting systems (base, lattice, or ggplot2) due to their algorithmic and graphical complexity. Simplifying the process would make oncoplots more accessible to researchers. Packages like ComplexHeatmap [@Gu:2022], maftools [@Mayakonda:2018], and genVisR [@Skidmore:2016] all make static oncoplots easier to create, but there is still a significant unmet need for an easy method of creating oncoplots with the following features: +Oncoplots are highly effective for visualising mutation data in cancer cohorts but are challenging to generate with the major R plotting systems (base, lattice, or ggplot2) due to their algorithmic and graphical complexity. Simplifying the process of generating oncoplots would make them more accessible to researchers. Existing packages including ComplexHeatmap [@Gu:2022], maftools [@Mayakonda:2018], and genVisR [@Skidmore:2016] all make static oncoplots easier to create, but there is still a significant unmet need for user-friendly method of creating oncoplots with the following features: -- **Interactive plots**: Customizable tooltips, cross-selection of samples across different plots, and auto-copying of sample identifiers on click. +- **Interactive plots**: Customizable tooltips, cross-selection of samples across different plots, and auto-copying of sample identifiers on click. This enables exploration of trends in multiomic datasets as shown in \autoref{fig:multimodal_selection}. -- **Support for tidy datasets**: Compatibility with tidy, tabular mutation-level formats (MAF files or relational databases), typical of cancer cohort datasets. +- **Support for tidy datasets**: Compatibility with tidy, tabular mutation-level formats (MAF files or relational databases), typical of cancer cohort datasets. This greatly improves the range of datasets that can be quickly and easily visualised in an oncoplot. -- **Auto colouring**: Automatic selection of colour palettes for datasets where consequence annotations are aligned with standard variant effect dictionaries (PAVE, SO, or MAF). +- **Auto-colouring**: Automatic selection of colour palettes for datasets where the consequence annotations are aligned with standard variant effect dictionaries (PAVE, SO, or MAF). -- **Versatility**: The ability to visualize entities other than gene mutations, including noncoding features (e.g., enhancers) and non-genomic entities (e.g., microbial presence in microbiome datasets). +- **Versatility**: The ability to visualize entities other than gene mutations, such as noncoding features (e.g., promoter or enhancer mutations) and non-genomic entities (e.g., microbial presence in microbiome datasets). -We developed ggoncoplot as the first R package that addresses all these challenges simultaneously (\autoref{fig:comparison}). Examples of all key features are available in the [ggoncoplot manual](https://selkamand.github.io/ggoncoplot/articles/manual.html). +We developed ggoncoplot as the first R package to address all these challenges together (\autoref{fig:comparison}). Examples of all key features are available in the [ggoncoplot manual](https://selkamand.github.io/ggoncoplot/articles/manual.html). ![Comparison of R packages for creating oncoplots. ^1^Requires the shiny and interactiveComplexHeatmap packages. ^2^Exclusively colours tiles based on mutation impact which must be described using valid MAF variant classification terms. ^3^Requires the user to first summarise mutations at the gene level and format as a sample by gene matrix with mutations separated by semicolons (wide format). ^4^For MAF inputs the most severe consequence is chosen, however for non-MAF datasets users must manually define the mutation impact hierarchy. ^5^Non-unique mutation types are treated as one observation, however if different mutation types affect one gene, the individual mutations can be plotted with different shapes or sizes in a user-configured manner. \label{fig:comparison}](ggoncoplot_comparison.pdf) diff --git a/paper/paper.pdf b/paper/paper.pdf index 778e107..99e7452 100644 Binary files a/paper/paper.pdf and b/paper/paper.pdf differ