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+} +body { + text-align: justify; +} +.center { + display: block; + margin-left: auto; + margin-right: auto; +} +.main-container { + max-width: 1200px; + margin-left: auto; + margin-right: auto; +} +p.maintext { + font-size: 1.2em; +} +table td, table td * { + vertical-align: top; +} +ul.maintext2 { + font-size: 1.1em; +} +ul ul.maintext2 { + font-size: 1.1em; +} +li.maintext2 { + font-size: 1.1em; +} +</style> +<p><br></p> +<!-- ## VoltRon: An R package for Spatial Data Analysis and Integration --> +<p><br></p> +<div style="width:76%; margin-left: auto; margin-right: auto; "> +<p><img width="100%" height="100%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/voltron_framework_box_io.png" class="center"></p> +<p><br></p> +<p class="maintext"> +<strong> VoltRon </strong> is a spatial omic analysis toolbox for +multi-omics integration using spatial image registration. VoltRon is +also capable of analyzing multiple types of spatially-aware data +modalities. +</p> +<ul class="maintext2"> +<li style="padding-bottom: 10px"> +<strong> Unique data structure </strong> of VoltRon allows users to +seamlessly define tissue blocks, layers and multiple assay types in one +R object. +</li> +<li style="padding-bottom: 10px"> +<strong> End-to-end downstream data analysis </strong> for distinct +spatial biology technologies are supported. VoltRon visualizes and +analyzes regions of interests (ROIs), spots, cells, molecules and tiles +<strong>(under development)</strong>. +</li> +<li style="padding-bottom: 10px"> +<strong> Automated Image Registration </strong> incorporates +<a href="https://opencv.org/">OpenCV</a> (fully embedded into the +package using <a href="https://www.rcpp.org/">Rcpp</a>) to detect common +features across images and achieves registration. Users may interact +with built-in mini shiny apps to change alignment parameters and +validate alignment accuracy. +</li> +<li style="padding-bottom: 10px"> +<strong> Manual Image Registration </strong> helps users to select +common features across spatial datasets using reference images stored in +VoltRon objects. In case automated image registration doesn’t work, you +can still align images by manually picking landmark points. +</li> +<li style="padding-bottom: 10px"> +<p style="padding-bottom: 3px"> +<strong> Spatially Aware Analysis </strong> allows detecting spatial +patterns across cells, spots, molecules and other entities. +</p> +<ul class="maintext3"> +<li style="padding-bottom: 10px padding-top: 12px"> +<strong>(Niche Clustering: Spots)</strong> VoltRon allows integration to +single cell RNA datasets using +<a href="https://satijalab.org/seurat/">Seurat</a>, +<a href="https://www.bioconductor.org/packages/release/bioc/vignettes/SingleCellExperiment/inst/doc/intro.html">SingleCellExperiment</a> +and <a href="https://github.com/dmcable/spacexr">spacexr</a> for spot +deconvolution. Estimated cell type abundances are then used to cluster +spots into groups of cell type niches which are defined as spots with +distinct composition of cell types. +</li> +<li style="padding-bottom: 2px"> +<strong>(Niche Clustering: Cells)</strong> VoltRon creates spatial +neighborhoods around cells to cluster local cellular compositions around +all cells which in turn informs users on cell types that are likely +within proximity to each other. +</li> +<li style="padding-bottom: 10px"> +<strong>(Hot Spot Detection)</strong> VoltRon detects region of locally +spatial patterns of cells/molecules/spots that are abundant in +biological events and/or features. +</li> +</ul> +</li> +<li style="padding-bottom: 10px"> +<p> +<strong> Support for Big Data </strong> for VoltRon objects enables +storing large feature data matrices and large microscopic images of +tissues on disk without overloading memory, thus allowing analysis on +large datasets with ease. VoltRon stores large images as pyramid +structures to speed up visualization and data retrieval. +</p> +</li> +<li style="padding-bottom: 10px"> +<p> +<strong> Interoperability across R/Python frameworks </strong> allows +users to convert VoltRon objects to a large number of objects used by +other spatial omic platforms such as Seurat, Squidpy (AnnData), +SpatialExperiment (BioConductor) and Giotto. +</p> +</li> +</ul> +</div> +<div style="width:70%; margin-left: auto; margin-right: auto"> +<div style="float:left; margin-left: auto; margin-right: auto"> +<p><br></p> +<div id="staying-up-to-date" class="section level2"> +<h2>Staying up-to-date</h2> +<p>To ask questions please use VoltRon discussion forum on google +groups.</p> +<ul> +<li><a href="https://groups.google.com/forum/#!forum/voltron_discussion" +class="uri">https://groups.google.com/forum/#!forum/voltron_discussion</a></li> +</ul> +<p><br></p> +</div> +<div id="installation" class="section level2"> +<h2>Installation</h2> +<p>Install from the GitHub repository using devtools (with R version +4.3.0 or higher):</p> +<pre class="r"><code>if (!require("devtools", quietly = TRUE)) + install.packages("devtools") +devtools::install_github("BIMSBbioinfo/VoltRon")</code></pre> +<p>Depending on the number of required dependencies, installation may be +completed under a minute or may take a few minutes.</p> +<p>On <strong>Windows</strong> and <strong>MacOS</strong>, OpenCV will +be downloaded automatically upon installation. However, <a +href="https://cran.r-project.org/bin/windows/Rtools/rtools43/rtools.html">Rtools</a> +may be required to be downloaded too, hence this may take some time!</p> +<p>On <strong>Ubuntu</strong> we provide a set of instructions that may +help users to build OpenCV with necessary headers [here](<a +href="https://github.com/BIMSBbioinfo/VoltRon/blob/main/inst/extdata/install_ubuntu.md" +class="uri">https://github.com/BIMSBbioinfo/VoltRon/blob/main/inst/extdata/install_ubuntu.md</a>.</p> +<p>On <strong>Fedora</strong> you may need <a +href="https://src.fedoraproject.org/rpms/opencv"><code>opencv-devel</code></a>:</p> +<pre class="sh"><code>yum install opencv-devel</code></pre> +<p><br></p> +</div> +<div id="dependencies" class="section level2"> +<h2>Dependencies</h2> +<p>VoltRon incorporates <code>RBioformats</code> package to import +images from <code>ome.tiff</code> files, which requires <a +href="https://www.oracle.com/java/technologies/downloads/?er=221886">Java +JDK</a> to be available in your system:</p> +<p>See <a +href="https://cran.r-project.org/web/packages/rJava">https://cran.r-project.org/web/packages/rJava</a> +for more information.</p> +<p><br></p> +</div> +<div id="docker-hub" class="section level2"> +<h2>Docker Hub</h2> +<p>You can also run VoltRon from a container already available in <a +href="https://hub.docker.com/repository/docker/amanukyan1385/rstudio-voltron/general">Docker +Hub</a>. The docker image is based on the <a +href="https://rocker-project.org/">Rocker Project</a> and can be run +from the terminal like below:</p> +<pre><code>docker run --rm -ti -e PASSWORD=<yourpassword> -p 8787:8787 amanukyan1385/rstudio-voltron:main</code></pre> +<p>Then, start the RStudio session from the browser at +<code>http://localhost:8787/</code> and enter <code>rstudio</code> as +username and <code><yourpassword></code> as password.</p> +<p>See <a +href="https://github.com/BIMSBbioinfo/VoltRon/blob/main/inst/extdata/docker_desktop_instructions.md">here</a> +for more instructions on how to run the container using <a +href="https://www.docker.com/products/docker-desktop/">Docker +Desktop</a>.</p> +<p><br></p> +</div> +<div id="references" class="section level2"> +<h2>References</h2> +<p>Manukyan, A., Bahry, E., Wyler, E., Becher, E., Pascual-Reguant, A., +Plumbom, I., … & Akalin, A. (2023). <a +href="https://www.biorxiv.org/content/10.1101/2023.12.15.571667v1">VoltRon: +A Spatial Omics Analysis Platform for Multi-Resolution and Multi-omics +Integration using Image Registration</a>. bioRxiv, 2023-12. d</p> +<p>To ask questions please use VoltRon discussion forum on <a +href="https://groups.google.com/forum/#!forum/voltron_discussion">Google +groups</a>.</p> +</div> +</div> +</div> + + + + +</div> + +<script> + +// add bootstrap table styles to pandoc tables +function bootstrapStylePandocTables() { + $('tr.odd').parent('tbody').parent('table').addClass('table table-condensed'); +} +$(document).ready(function () { + bootstrapStylePandocTables(); +}); + + +</script> + +<!-- tabsets --> + +<script> +$(document).ready(function () { + window.buildTabsets("TOC"); +}); + +$(document).ready(function () { + $('.tabset-dropdown > .nav-tabs > li').click(function () { + $(this).parent().toggleClass('nav-tabs-open'); + }); +}); +</script> + +<!-- code folding --> + + +<!-- dynamically load mathjax for compatibility with self-contained --> +<script> + (function () { + var script = document.createElement("script"); + script.type = "text/javascript"; + script.src = "https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"; + document.getElementsByTagName("head")[0].appendChild(script); + })(); +</script> + +</body> +</html>