shiny_shm: Integrated Shiny App
In jianhaizhang/spatialHeatmap: spatialHeatmap: Visualizing Spatial Assays in Anatomical Images and Large-Scale Data Extensions
shiny_shm R Documentation
Integrated Shiny App
Description
In additon to generating spatial heatmaps and corresponding item (genes, proteins, metabolites, etc.) context plots from R, spatialHeatmap includes a Shiny App (https://shiny.rstudio.com/) that provides access to the same functionalities from an intuitive-to-use web browser interface. Apart from being very user-friendly, this App conveniently organizes the results of the entire visualization workflow in a single browser window with options to adjust the parameters of the individual components interactively. Upon launched, the app automatically displays a pre-formatted example.
To use this app, the data matrix (e.g. gene expression matrix) and aSVG image are uploaded as tabular text (e.g. in CSV or TSV format) and SVG file, respectively. To also allow users to upload data matrix stored in SummarizedExperiment objects, one can export them from R to a tabular file with the filter_data function. In this function call, the user sets a desired directory path under dir. Within this directory the tabular file will be written to "customComputedData/sub_matrix.txt" in TSV format. The column names in the exported tabular file preserve the experimental design information from the colData slot by concatenating the corresponding sample and condition information separated by double underscores. To interactively view functional descriptions by moving the cursor over network nodes, the corresponding annotation column needs to be present in the rowData slot and its column name assigned to the ann argument. In the exported tabular file the extra annotation column is appended to the expression matrix. See function filter_data for details.
If the subsetted data matrix in the Matrix Heatmap is too large, e.g. >10,000 rows, the "customComputedData" under "Step 1: data sets" is recommended. Since this subsetted matrix is fed to the Network, and the internal computation of adjacency matrix and module identification would be intensive. In order to protect the app from crash, the intensive computation should be performed outside the app, then upload the results under "customComputedData". When using "customComputedData", the data matrix to upload is the subsetted matrix "sub_matrix.txt" generated with submatrix, which is a TSV-tabular text file. The adjacency matrix and module assignment to upload are "adj.txt" and "mod.txt" generated in function adj_mod respectively. Note, "sub_matrix.txt", "adj.txt", and "mod.txt" are downstream to the same call on filter_data, so the three files should not be mixed between different filtering when uploading. See the instruction page in the app for details.
The large matrix issue could be resolved by increasing the subsetting strigency to get smaller matrix in submatrix in most cases. Only in rare cases users cannot avoid very large subsetted matrix, the "customComputedData" is recommended.
Usage
shiny_shm()
Value
A web browser based Shiny app.
Details
No argument is required, this function launches the Shiny app directly.
Author(s)
Jianhai Zhang jzhan067@ucr.edu
Dr. Thomas Girke thomas.girke@ucr.edu
References
https://www.w3schools.com/graphics/svg_intro.asp
https://shiny.rstudio.com/tutorial/
https://shiny.rstudio.com/articles/datatables.html
https://rstudio.github.io/DT/010-style.html
https://plot.ly/r/heatmaps/
https://www.gimp.org/tutorials/
https://inkscape.org/en/doc/tutorials/advanced/tutorial-advanced.en.html
http://www.microugly.com/inkscape-quickguide/
https://cran.r-project.org/web/packages/visNetwork/vignettes/Introduction-to-visNetwork.html
Winston Chang, Joe Cheng, JJ Allaire, Yihui Xie and Jonathan McPherson (2017). shiny: Web Application Framework for R. R package version 1.0.3. https://CRAN.R-project.org/package=shiny
Winston Chang and Barbara Borges Ribeiro (2017). shinydashboard: Create Dashboards with 'Shiny'. R package version 0.6.1. https://CRAN.R-project.org/package=shinydashboard
Paul Murrell (2009). Importing Vector Graphics: The grImport Package for R. Journal of Statistical Software, 30(4), 1-37. URL http://www.jstatsoft.org/v30/i04/
Jeroen Ooms (2017). rsvg: Render SVG Images into PDF, PNG, PostScript, or Bitmap Arrays. R package version 1.1. https://CRAN.R-project.org/package=rsvg
H. Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016.
Yihui Xie (2016). DT: A Wrapper of the JavaScript Library 'DataTables'. R package version 0.2. https://CRAN.R-project.org/package=DT
Baptiste Auguie (2016). gridExtra: Miscellaneous Functions for "Grid" Graphics. R package version 2.2.1. https://CRAN.R-project.org/package=gridExtra
Andrie de Vries and Brian D. Ripley (2016). ggdendro: Create Dendrograms and Tree Diagrams Using 'ggplot2'. R package version 0.1-20. https://CRAN.R-project.org/package=ggdendro
Langfelder P and Horvath S, WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 2008, 9:559 doi:10.1186/1471-2105-9-559
Peter Langfelder, Steve Horvath (2012). Fast R Functions for Robust Correlations and Hierarchical Clustering. Journal of Statistical Software, 46(11), 1-17. URL http://www.jstatsoft.org/v46/i11/
Simon Urbanek and Jeffrey Horner (2015). Cairo: R graphics device using cairo graphics library for creating high-quality bitmap (PNG, JPEG, TIFF), vector (PDF, SVG, PostScript) and display (X11 and Win32) output. R package version 1.5-9. https://CRAN.R-project.org/package=Cairo
R Core Team (2017). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/
Duncan Temple Lang and the CRAN Team (2017). XML: Tools for Parsing and Generating XML Within R and S-Plus. R package version 3.98-1.9. https://CRAN.R-project.org/package=XML
Carson Sievert, Chris Parmer, Toby Hocking, Scott Chamberlain, Karthik Ram, Marianne Corvellec and Pedro Despouy (NA). plotly: Create Interactive Web Graphics via 'plotly.js'. https://plot.ly/r, https://cpsievert.github.io/plotly_book/, https://github.com/ropensci/plotly
Matt Dowle and Arun Srinivasan (2017). data.table: Extension of 'data.frame'. R package version 1.10.4. https://CRAN.R-project.org/package=data.table
R. Gentleman, V. Carey, W. Huber and F. Hahne (2017). genefilter: genefilter: methods for filtering genes from high-throughput experiments. R package version 1.58.1.
Peter Langfelder, Steve Horvath (2012). Fast R Functions for Robust Correlations and Hierarchical Clustering. Journal of Statistical Software, 46(11), 1-17. URL http://www.jstatsoft.org/v46/i11/
Almende B.V., Benoit Thieurmel and Titouan Robert (2017). visNetwork: Network Visualization using 'vis.js' Library. R package version 2.0.1. https://CRAN.R-project.org/package=visNetwork
Zhang L (2023). _spsComps: 'systemPipeShiny' UI and Server Components_. R package version 0.3.3.0, <https://CRAN.R-project.org/package=spsComps>.
Examples
shiny_shm()
jianhaizhang/spatialHeatmap documentation built on Nov. 28, 2024, 4:44 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
| shiny_shm | R Documentation |
Integrated Shiny App
Description
In additon to generating spatial heatmaps and corresponding item (genes, proteins, metabolites, etc.) context plots from R, spatialHeatmap includes a Shiny App (https://shiny.rstudio.com/) that provides access to the same functionalities from an intuitive-to-use web browser interface. Apart from being very user-friendly, this App conveniently organizes the results of the entire visualization workflow in a single browser window with options to adjust the parameters of the individual components interactively. Upon launched, the app automatically displays a pre-formatted example.
To use this app, the data matrix (e.g. gene expression matrix) and aSVG image are uploaded as tabular text (e.g. in CSV or TSV format) and SVG file, respectively. To also allow users to upload data matrix stored in SummarizedExperiment objects, one can export them from R to a tabular file with the filter_data function. In this function call, the user sets a desired directory path under dir. Within this directory the tabular file will be written to "customComputedData/sub_matrix.txt" in TSV format. The column names in the exported tabular file preserve the experimental design information from the colData slot by concatenating the corresponding sample and condition information separated by double underscores. To interactively view functional descriptions by moving the cursor over network nodes, the corresponding annotation column needs to be present in the rowData slot and its column name assigned to the ann argument. In the exported tabular file the extra annotation column is appended to the expression matrix. See function filter_data for details.
If the subsetted data matrix in the Matrix Heatmap is too large, e.g. >10,000 rows, the "customComputedData" under "Step 1: data sets" is recommended. Since this subsetted matrix is fed to the Network, and the internal computation of adjacency matrix and module identification would be intensive. In order to protect the app from crash, the intensive computation should be performed outside the app, then upload the results under "customComputedData". When using "customComputedData", the data matrix to upload is the subsetted matrix "sub_matrix.txt" generated with submatrix, which is a TSV-tabular text file. The adjacency matrix and module assignment to upload are "adj.txt" and "mod.txt" generated in function adj_mod respectively. Note, "sub_matrix.txt", "adj.txt", and "mod.txt" are downstream to the same call on filter_data, so the three files should not be mixed between different filtering when uploading. See the instruction page in the app for details.
The large matrix issue could be resolved by increasing the subsetting strigency to get smaller matrix in submatrix in most cases. Only in rare cases users cannot avoid very large subsetted matrix, the "customComputedData" is recommended.
Usage
shiny_shm()
Value
A web browser based Shiny app.
Details
No argument is required, this function launches the Shiny app directly.
Author(s)
Jianhai Zhang jzhan067@ucr.edu
Dr. Thomas Girke thomas.girke@ucr.edu
References
https://www.w3schools.com/graphics/svg_intro.asp
https://shiny.rstudio.com/tutorial/
https://shiny.rstudio.com/articles/datatables.html
https://rstudio.github.io/DT/010-style.html
https://plot.ly/r/heatmaps/
https://www.gimp.org/tutorials/
https://inkscape.org/en/doc/tutorials/advanced/tutorial-advanced.en.html
http://www.microugly.com/inkscape-quickguide/
https://cran.r-project.org/web/packages/visNetwork/vignettes/Introduction-to-visNetwork.html
Winston Chang, Joe Cheng, JJ Allaire, Yihui Xie and Jonathan McPherson (2017). shiny: Web Application Framework for R. R package version 1.0.3. https://CRAN.R-project.org/package=shiny
Winston Chang and Barbara Borges Ribeiro (2017). shinydashboard: Create Dashboards with 'Shiny'. R package version 0.6.1. https://CRAN.R-project.org/package=shinydashboard
Paul Murrell (2009). Importing Vector Graphics: The grImport Package for R. Journal of Statistical Software, 30(4), 1-37. URL http://www.jstatsoft.org/v30/i04/
Jeroen Ooms (2017). rsvg: Render SVG Images into PDF, PNG, PostScript, or Bitmap Arrays. R package version 1.1. https://CRAN.R-project.org/package=rsvg
H. Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016.
Yihui Xie (2016). DT: A Wrapper of the JavaScript Library 'DataTables'. R package version 0.2. https://CRAN.R-project.org/package=DT
Baptiste Auguie (2016). gridExtra: Miscellaneous Functions for "Grid" Graphics. R package version 2.2.1. https://CRAN.R-project.org/package=gridExtra
Andrie de Vries and Brian D. Ripley (2016). ggdendro: Create Dendrograms and Tree Diagrams Using 'ggplot2'. R package version 0.1-20. https://CRAN.R-project.org/package=ggdendro
Langfelder P and Horvath S, WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 2008, 9:559 doi:10.1186/1471-2105-9-559
Peter Langfelder, Steve Horvath (2012). Fast R Functions for Robust Correlations and Hierarchical Clustering. Journal of Statistical Software, 46(11), 1-17. URL http://www.jstatsoft.org/v46/i11/
Simon Urbanek and Jeffrey Horner (2015). Cairo: R graphics device using cairo graphics library for creating high-quality bitmap (PNG, JPEG, TIFF), vector (PDF, SVG, PostScript) and display (X11 and Win32) output. R package version 1.5-9. https://CRAN.R-project.org/package=Cairo
R Core Team (2017). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/
Duncan Temple Lang and the CRAN Team (2017). XML: Tools for Parsing and Generating XML Within R and S-Plus. R package version 3.98-1.9. https://CRAN.R-project.org/package=XML
Carson Sievert, Chris Parmer, Toby Hocking, Scott Chamberlain, Karthik Ram, Marianne Corvellec and Pedro Despouy (NA). plotly: Create Interactive Web Graphics via 'plotly.js'. https://plot.ly/r, https://cpsievert.github.io/plotly_book/, https://github.com/ropensci/plotly
Matt Dowle and Arun Srinivasan (2017). data.table: Extension of 'data.frame'. R package version 1.10.4. https://CRAN.R-project.org/package=data.table
R. Gentleman, V. Carey, W. Huber and F. Hahne (2017). genefilter: genefilter: methods for filtering genes from high-throughput experiments. R package version 1.58.1.
Peter Langfelder, Steve Horvath (2012). Fast R Functions for Robust Correlations and Hierarchical Clustering. Journal of Statistical Software, 46(11), 1-17. URL http://www.jstatsoft.org/v46/i11/
Almende B.V., Benoit Thieurmel and Titouan Robert (2017). visNetwork: Network Visualization using 'vis.js' Library. R package version 2.0.1. https://CRAN.R-project.org/package=visNetwork Zhang L (2023). _spsComps: 'systemPipeShiny' UI and Server Components_. R package version 0.3.3.0, <https://CRAN.R-project.org/package=spsComps>.
Examples
shiny_shm()
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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