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<!DOCTYPE html>





  
  

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<html>





  
  

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<head>





  
  

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<meta charset="utf-8" />





  
  

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<meta name="generator" content="pandoc" />





  
  

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<meta http-equiv="X-UA-Compatible" content="IE=EDGE" />





  
  

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<title>Image Registration</title>





  
  

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<script src="site_libs/header-attrs-2.29/header-attrs.js"></script>





  
  

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<script src="site_libs/jquery-3.6.0/jquery-3.6.0.min.js"></script>





  
  

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<meta name="viewport" content="width=device-width, initial-scale=1" />





  
  

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<link href="site_libs/bootstrap-3.3.5/css/flatly.min.css" rel="stylesheet" />





  
  

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<script src="site_libs/bootstrap-3.3.5/js/bootstrap.min.js"></script>





  
  

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<script src="site_libs/bootstrap-3.3.5/shim/html5shiv.min.js"></script>





  
  

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<script src="site_libs/bootstrap-3.3.5/shim/respond.min.js"></script>





  
  

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<style>h1 {font-size: 34px;}





  
  

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       h1.title {font-size: 38px;}





  
  

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       h2 {font-size: 30px;}





  
  

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       h3 {font-size: 24px;}





  
  

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       h4 {font-size: 18px;}





  
  

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       h5 {font-size: 16px;}





  
  

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       h6 {font-size: 12px;}





  
  

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       code {color: inherit; background-color: rgba(0, 0, 0, 0.04);}





  
  

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       pre:not([class]) { background-color: white }</style>





  
  

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<script src="site_libs/jqueryui-1.13.2/jquery-ui.min.js"></script>





  
  

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<link href="site_libs/tocify-1.9.1/jquery.tocify.css" rel="stylesheet" />





  
  

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<script src="site_libs/tocify-1.9.1/jquery.tocify.js"></script>





  
  

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<script src="site_libs/navigation-1.1/tabsets.js"></script>





  
  

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<link href="site_libs/highlightjs-9.12.0/textmate.css" rel="stylesheet" />





  
  

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<script src="site_libs/highlightjs-9.12.0/highlight.js"></script>





  
  

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<link href="site_libs/font-awesome-6.5.2/css/all.min.css" rel="stylesheet" />





  
  

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<link href="site_libs/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet" />





  
  

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<style type="text/css">





  
  

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  code{white-space: pre-wrap;}





  
  

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  span.smallcaps{font-variant: small-caps;}





  
  

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  span.underline{text-decoration: underline;}





  
  

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  div.column{display: inline-block; vertical-align: top; width: 50%;}





  
  

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  div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;}





  
  

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  ul.task-list{list-style: none;}





  
  

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    </style>





  
  

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<style type="text/css">code{white-space: pre;}</style>





  
  

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<script type="text/javascript">





  
  

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if (window.hljs) {





  
  

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  hljs.configure({languages: []});





  
  

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  hljs.initHighlightingOnLoad();





  
  

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    window.setTimeout(function() { hljs.initHighlighting(); }, 0);





  
  

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  }





  
  

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}





  
  

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</script>





  
  

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<style type = "text/css">





  
  

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.main-container {





  
  

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  max-width: 940px;





  
  

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  margin-right: auto;





  
  

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}





  
  

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img {





  
  

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  max-width:100%;





  
  

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}





  
  

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  padding-top: 12px;





  
  

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}





  
  

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.html-widget {





  
  

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  margin-bottom: 20px;





  
  

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}





  
  

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button.code-folding-btn:focus {





  
  

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  outline: none;





  
  

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}





  
  

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summary {





  
  

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}





  
  

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details > summary > p:only-child {





  
  

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pre code {





  
  

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</style>





  
  

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<style type="text/css">





  
  

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.dropdown-submenu {





  
  

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  position: relative;





  
  

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}





  
  

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.dropdown-submenu>.dropdown-menu {





  
  

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  top: 0;





  
  

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  left: 100%;





  
  

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  margin-top: -6px;





  
  

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  margin-left: -1px;





  
  

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  border-radius: 0 6px 6px 6px;





  
  

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}





  
  

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.dropdown-submenu:hover>.dropdown-menu {





  
  

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  display: block;





  
  

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}





  
  

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.dropdown-submenu>a:after {





  
  

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  display: block;





  
  

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  content: " ";





  
  

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  float: right;





  
  

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  width: 0;





  
  

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  height: 0;





  
  

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  border-color: transparent;





  
  

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  border-style: solid;





  
  

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  border-width: 5px 0 5px 5px;





  
  

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  border-left-color: #cccccc;





  
  

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  margin-top: 5px;





  
  

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  margin-right: -10px;





  
  

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}





  
  

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.dropdown-submenu:hover>a:after {





  
  

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  border-left-color: #adb5bd;





  
  

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}





  
  

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.dropdown-submenu.pull-left {





  
  

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  float: none;





  
  

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}





  
  

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.dropdown-submenu.pull-left>.dropdown-menu {





  
  

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  left: -100%;





  
  

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  margin-left: 10px;





  
  

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  border-radius: 6px 0 6px 6px;





  
  

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}





  
  

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</style>





  
  

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<script type="text/javascript">





  
  

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// manage active state of menu based on current page





  
  

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$(document).ready(function () {





  
  

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  // active menu anchor





  
  

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  href = window.location.pathname





  
  

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  href = href.substr(href.lastIndexOf('/') + 1)





  
  

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  if (href === "")





  
  

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    href = "index.html";





  
  

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  var menuAnchor = $('a[href="' + href + '"]');





  
  

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  // mark the anchor link active (and if it's in a dropdown, also mark that active)





  
  

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  var dropdown = menuAnchor.closest('li.dropdown');





  
  

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  if (window.bootstrap) { // Bootstrap 4+





  
  

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    menuAnchor.addClass('active');





  
  

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    dropdown.find('> .dropdown-toggle').addClass('active');





  
  

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  } else { // Bootstrap 3





  
  

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    menuAnchor.parent().addClass('active');





  
  

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    dropdown.addClass('active');





  
  

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  }





  
  

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  // Navbar adjustments





  
  

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  var navHeight = $(".navbar").first().height() + 15;





  
  

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  var style = document.createElement('style');





  
  

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  var pt = "padding-top: " + navHeight + "px; ";





  
  

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  var mt = "margin-top: -" + navHeight + "px; ";





  
  

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  var css = "";





  
  

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  // offset scroll position for anchor links (for fixed navbar)





  
  

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  for (var i = 1; i <= 6; i++) {





  
  

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    css += ".section h" + i + "{ " + pt + mt + "}\n";





  
  

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  }





  
  

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  style.innerHTML = "body {" + pt + "padding-bottom: 40px; }\n" + css;





  
  

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  document.head.appendChild(style);





  
  

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});





  
  

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</script>





  
  

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<!-- tabsets -->





  
  

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<style type="text/css">





  
  

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.tabset-dropdown > .nav-tabs {





  
  

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  display: inline-table;





  
  

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  max-height: 500px;





  
  

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  min-height: 44px;





  
  

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  overflow-y: auto;





  
  

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  border: 1px solid #ddd;





  
  

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  border-radius: 4px;





  
  

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}





  
  

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.tabset-dropdown > .nav-tabs > li.active:before, .tabset-dropdown > .nav-tabs.nav-tabs-open:before {





  
  

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  content: "\e259";





  
  

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  font-family: 'Glyphicons Halflings';





  
  

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  display: inline-block;





  
  

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  padding: 10px;





  
  

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  border-right: 1px solid #ddd;





  
  

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}





  
  

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.tabset-dropdown > .nav-tabs.nav-tabs-open > li.active:before {





  
  

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  content: "\e258";





  
  

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  font-family: 'Glyphicons Halflings';





  
  

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  border: none;





  
  

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}





  
  

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.tabset-dropdown > .nav-tabs > li.active {





  
  

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  display: block;





  
  

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}





  
  

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.tabset-dropdown > .nav-tabs > li > a,





  
  

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.tabset-dropdown > .nav-tabs > li > a:focus,





  
  

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.tabset-dropdown > .nav-tabs > li > a:hover {





  
  

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  border: none;





  
  

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  display: inline-block;





  
  

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  border-radius: 4px;





  
  

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  background-color: transparent;





  
  

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}





  
  

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.tabset-dropdown > .nav-tabs.nav-tabs-open > li {





  
  

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  display: block;





  
  

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  float: none;





  
  

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}





  
  

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.tabset-dropdown > .nav-tabs > li {





  
  

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  display: none;





  
  

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}





  
  

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</style>





  
  

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<!-- code folding -->





  
  

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<style type="text/css">





  
  

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#TOC {





  
  

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  margin: 25px 0px 20px 0px;





  
  

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}





  
  

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@media (max-width: 768px) {





  
  

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#TOC {





  
  

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  position: relative;





  
  

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  width: 100%;





  
  

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}





  
  

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}





  
  

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@media print {





  
  

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.toc-content {





  
  

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  /* see https://github.com/w3c/csswg-drafts/issues/4434 */





  
  

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  float: right;





  
  

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}





  
  

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}





  
  

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.toc-content {





  
  

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  padding-left: 30px;





  
  

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  padding-right: 40px;





  
  

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}





  
  

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div.main-container {





  
  

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  max-width: 1200px;





  
  

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}





  
  

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div.tocify {





  
  

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  width: 20%;





  
  

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  max-width: 260px;





  
  

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  max-height: 85%;





  
  

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}





  
  

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@media (min-width: 768px) and (max-width: 991px) {





  
  

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  div.tocify {





  
  

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    width: 25%;





  
  

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  }





  
  

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}





  
  

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@media (max-width: 767px) {





  
  

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  div.tocify {





  
  

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    width: 100%;





  
  

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    max-width: none;





  
  

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  }





  
  

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}





  
  

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.tocify ul, .tocify li {





  
  

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  line-height: 20px;





  
  

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}





  
  

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.tocify-subheader .tocify-item {





  
  

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  font-size: 0.90em;





  
  

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}





  
  

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.tocify .list-group-item {





  
  

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  border-radius: 0px;





  
  

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}





  
  

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.tocify-subheader {





  
  

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  display: inline;





  
  

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}





  
  

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.tocify-subheader .tocify-item {





  
  

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  font-size: 0.95em;





  
  

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}





  
  

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</style>





  
  

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</head>





  
  

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<body>





  
  

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<div class="container-fluid main-container">





  
  

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<!-- setup 3col/9col grid for toc_float and main content  -->





  
  

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<div class="row">





  
  

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<div class="col-xs-12 col-sm-4 col-md-3">





  
  

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<div id="TOC" class="tocify">





  
  

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</div>





  
  

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</div>





  
  

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<div class="toc-content col-xs-12 col-sm-8 col-md-9">





  
  

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<div class="navbar navbar-default  navbar-fixed-top" role="navigation">





  
  

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  <div class="container">





  
  

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    <div class="navbar-header">





  
  

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      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-bs-toggle="collapse" data-target="#navbar" data-bs-target="#navbar">





  
  

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        <span class="icon-bar"></span>





  
  

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        <span class="icon-bar"></span>





  
  

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        <span class="icon-bar"></span>





  
  

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      </button>





  
  

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      <a class="navbar-brand" href="index.html">VoltRon</a>





  
  

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    </div>





  
  

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    <div id="navbar" class="navbar-collapse collapse">





  
  

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      <ul class="nav navbar-nav">





  
  

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        <li>





  
  

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  <a href="tutorials.html">Explore</a>





  
  

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</li>





  
  

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<li class="dropdown">





  
  

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  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">





  
  

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    Vignette





  
  

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    <span class="caret"></span>





  
  

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  </a>





  
  

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  <ul class="dropdown-menu" role="menu">





  
  

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    <li class="dropdown-submenu">





  
  

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      <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">Spatial Data Integration</a>





  
  

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      <ul class="dropdown-menu" role="menu">





  
  

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        <li>





  
  

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          <a href="registration.html">Spatial Data Alignment</a>





  
  

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        </li>





  
  

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        <li>





  
  

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          <a href="multiomic.html">Multi-omic Integration</a>





  
  

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        </li>





  
  

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        <li>





  
  

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          <a href="nicheclustering.html">Niche Clustering</a>





  
  

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        </li>





  
  

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      </ul>





  
  

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    </li>





  
  

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    <li class="dropdown-submenu">





  
  

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      <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">Downstream Analysis</a>





  
  

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      <ul class="dropdown-menu" role="menu">





  
  

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        <li>





  
  

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          <a href="roianalysis.html">ROI Analysis</a>





  
  

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        </li>





  
  

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        <li>





  
  

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          <a href="spotanalysis.html">Cell/Spot Analysis</a>





  
  

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        </li>





  
  

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        <li>





  
  

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          <a href="moleculeanalysis.html">Molecule Analysis</a>





  
  

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        </li>





  
  

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        <li>





  
  

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          <a href="pixelanalysis.html">Pixels (Image Only) Analysis</a>





  
  

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        </li>





  
  

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      </ul>





  
  

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    </li>





  
  

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    <li class="dropdown-submenu">





  
  

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      <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">Utilities</a>





  
  

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      <ul class="dropdown-menu" role="menu">





  
  

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        <li>





  
  

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          <a href="interactive.html">Interactive Utilities</a>





  
  

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        </li>





  
  

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        <li>





  
  

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          <a href="importingdata.html">Importing Spatial Data</a>





  
  

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        </li>





  
  

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        <li>





  
  

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          <a href="voltronobjects.html">Working with VoltRon Objects</a>





  
  

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        </li>





  
  

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        <li>





  
  

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          <a href="conversion.html">Converting VoltRon Objects</a>





  
  

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        </li>





  
  

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        <li>





  
  

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          <a href="ondisk.html">OnDisk-based Analysis Utilities</a>





  
  

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        </li>





  
  

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      </ul>





  
  

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    </li>





  
  

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  </ul>





  
  

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</li>





  
  

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      </ul>





  
  

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<h1 class="title toc-ignore">Image Registration</h1>





  
  

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</div>





  
  

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<p><br></p>





  
  

  461
  
<div id="spatial-data-alignment" class="section level1">





  
  

  462
  
<h1>Spatial Data Alignment</h1>





  
  

  463
  
<p>Spatial genomic technologies often generate diverse images and





  
  

  464
  
spatial readouts, even though the tissue slices are from adjacent





  
  

  465
  
sections of a single tissue block. Hence, the alignment of images and





  
  

  466
  
spatial coordinates across tissue sections are of utmost importance to





  
  

  467
  
dissect the correct spatial closeness across these sections.</p>





  
  

  468
  
<p>VoltRon allows users to <strong>align spatial omics datasets of these





  
  

  469
  
serial sections</strong> for data transfer and 3 dimensional stack





  
  

  470
  
alignment. The order of the tissue/sample slices should be provided by





  
  

  471
  
the user. VoltRon provides a fully embedded <strong>shiny





  
  

  472
  
application</strong> to either automatically or manually align images.





  
  

  473
  
The automatic alignment is achieved with the <strong>OpenCV</strong>’s





  
  

  474
  
C++ library fully embedded in the VoltRon package.</p>





  
  

  475
  
<table>





  
  

  476
  
<tbody>





  
  

  477
  
<tr style="vertical-align: center">





  
  

  478
  
<td style="width:43%; vertical-align: center">





  
  

  479
  
<img src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/manualregistration.png" class="center">





  
  

  480
  
</td>





  
  

  481
  
<td style="width:43%; vertical-align: center">





  
  

  482
  
<img src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/autoregistration.png" class="center">





  
  

  483
  
</td>





  
  

  484
  
</tr>





  
  

  485
  
</tbody>





  
  

  486
  
</table>





  
  

  487
  
<p><br></p>





  
  

  488
  
<div id="alignment-of-xenium-and-visium" class="section level2">





  
  

  489
  
<h2>Alignment of Xenium and Visium</h2>





  
  

  490
  
<p>In this use case, we will align <strong>immunofluorescence





  
  

  491
  
(IF)</strong> and <strong>H&amp;E images</strong> of the <strong>Xenium





  
  

  492
  
In Situ</strong> and <strong>Visium CytAssist</strong> platforms





  
  

  493
  
readouts. Three tissue sections are derived from a single





  
  

  494
  
formalin-fixed, paraffin-embedded (FFPE) breast cancer tissue block. A 5





  
  

  495
  
<span class="math inline">\(\mu\)</span>m section was taken for Visium





  
  

  496
  
CytAssist and two replicate 5 <span class="math inline">\(\mu\)</span>m





  
  

  497
  
sections were taken for the Xenium replicates. More information on the





  
  

  498
  
spatial datasets and the study can be also be found on the <a





  
  

  499
  
href="https://www.biorxiv.org/content/10.1101/2022.10.06.510405v1">BioArxiv





  
  

  500
  
preprint</a>.</p>





  
  

  501
  
<p>You can download the Xenium and Visium readouts from the <a





  
  

  502
  
href="https://www.10xgenomics.com/products/xenium-in-situ/preview-dataset-human-breast">10x





  
  

  503
  
Genomics website</a> (specifically, import <strong>In Situ Replicate 1/2





  
  

  504
  
and Visium Spatial</strong>). Alternatively, you can <strong>download a





  
  

  505
  
zipped collection of three Visium and Xenium readouts</strong> from <a





  
  

  506
  
href="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/SpatialDataAlignment/Xenium_vs_Visium/10X_Xenium_Visium.zip">here</a>.</p>





  
  

  507
  
<p>VoltRon includes built-in functions for converting readouts from both





  
  

  508
  
Xenium and Visium platforms into VoltRon objects. We will import both





  
  

  509
  
Xenium replicates alongside with the Visium CytAssist data so that we





  
  

  510
  
can register images of these assays and merge them into one VoltRon





  
  

  511
  
object.</p>





  
  

  512
  
<pre class="r watch-out"><code>library(VoltRon)





  
  

  513
  
Xen_R1 &lt;- importXenium(&quot;Xenium_R1/outs&quot;, sample_name = &quot;XeniumR1&quot;)





  
  

  514
  
Xen_R2 &lt;- importXenium(&quot;Xenium_R2/outs&quot;, sample_name = &quot;XeniumR2&quot;)





  
  

  515
  
Vis &lt;- importVisium(&quot;Visium/&quot;, sample_name = &quot;VisiumR1&quot;)</code></pre>





  
  

  516
  
<p>Before moving on to image alignment, we can inspect both Xenium and





  
  

  517
  
Visium images. We use the <strong>vrImages</strong> function to call and





  
  

  518
  
visualize reference images of all VoltRon objects.</p>





  
  

  519
  
<pre class="r watch-out"><code>vrImages(Xen_R1)





  
  

  520
  
vrImages(Xen_R2)





  
  

  521
  
vrImages(Vis)</code></pre>





  
  

  522
  
<table>





  
  

  523
  
<tbody>





  
  

  524
  
<tr style="vertical-align: center">





  
  

  525
  
<td style="width:33%; vertical-align: center">





  
  

  526
  
<img src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/xeniumr1.png" class="center">





  
  

  527
  
</td>





  
  

  528
  
<td style="width:33%; vertical-align: center">





  
  

  529
  
<img src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/xeniumr2.png" class="center">





  
  

  530
  
</td>





  
  

  531
  
<td style="width:33%; vertical-align: center">





  
  

  532
  
<img src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/visium.png" class="center">





  
  

  533
  
</td>





  
  

  534
  
</tr>





  
  

  535
  
</tbody>





  
  

  536
  
</table>





  
  

  537
  
<p><br></p>





  
  

  538
  
<p>Although images of the first Xenium replicate and the Visium assay





  
  

  539
  
are workable, we have to adjust the brightness of the second Xenium





  
  

  540
  
replicate before image alignment. You can use





  
  

  541
  
<strong>modulateImage</strong> function to change the brightness and`





  
  

  542
  
saturation of the reference image of this VoltRon object. This





  
  

  543
  
functionality is optional for VoltRon objects and should be used when





  
  

  544
  
images require further adjustments.</p>





  
  

  545
  
<pre class="r watch-out"><code>Xen_R2 &lt;- modulateImage(Xen_R2, brightness = 800)





  
  

  546
  
vrImages(Xen_R2)</code></pre>





  
  

  547
  
<p><img width="40%" height="40%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/xeniumr2_new.png" class="center"></p>





  
  

  548
  
<p><br></p>





  
  

  549
  
<div id="automated-image-alignment" class="section level3">





  
  

  550
  
<h3>Automated Image Alignment</h3>





  
  

  551
  
<p>In order to achieve data transfer and integration across these two





  
  

  552
  
modalities, we need to first make sure that spatial coordinates of these





  
  

  553
  
three datasets are perfectly aligned. To this end, we will make use of





  
  

  554
  
the <strong>registerSpatialData</strong> function which calls a





  
  

  555
  
<strong>shiny app</strong> embedded into VoltRon. The function takes a





  
  

  556
  
single list as an input where the order of VoltRon objects in the list





  
  

  557
  
should be the same as the <strong>order of serial sections</strong>.</p>





  
  

  558
  
<p>We will make use of the <strong>registerSpatialData</strong> function





  
  

  559
  
to <strong>automatically register two Xenium assays onto the Visium





  
  

  560
  
assay</strong>. The Visium CytAssist image (or the <strong>image on the





  
  

  561
  
center</strong> of the list) would be taken as the image of reference,





  
  

  562
  
and hence all other images (or spatial datasets) are to be aligned to





  
  

  563
  
the Visium data. Then, registerSpatialData will return a list of VoltRon





  
  

  564
  
objects whose assays include both the original and registered versions





  
  

  565
  
of spatial coordinates. The shiny app will provide <strong>two





  
  

  566
  
images</strong> for this task:</p>





  
  

  567
  
<ul>





  
  

  568
  
<li>An image that shows the matched points across two images, and</li>





  
  

  569
  
<li>A slideshow with of the reference and registered images that





  
  

  570
  
demonstrates the alignment accuracy.</li>





  
  

  571
  
</ul>





  
  

  572
  
<p>We will select <strong>FLANN</strong> method for automated alignment





  
  

  573
  
which incorporates the <strong>SIFT</strong> method for automated





  
  

  574
  
keypoints selection and utilizes the <strong>Fast library for





  
  

  575
  
Approximate Nearest Neighbors (FLANN) algorithm</strong> for matching





  
  

  576
  
keypoints. <strong>NOTE:</strong> For better alignment performance,





  
  

  577
  
users can incorporate image manipulation tools above each image and sync





  
  

  578
  
images into the same orientation by rotating, flipping (horizontally and





  
  

  579
  
vertically) and negating these images. We always negate DAPI images to





  
  

  580
  
align them onto H&amp;E images.</p>





  
  

  581
  
<pre class="r watch-out"><code>xen_reg &lt;- registerSpatialData(object_list = list(Xen_R1, Vis, Xen_R2))</code></pre>





  
  

  582
  
<p><img width="100%" height="100%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/XeniumVisiumRegistration_FLANN.gif" class="center"></p>





  
  

  583
  
<p><br></p>





  
  

  584
  
<p>You can save and use the same parameters later, and reproduce the





  
  

  585
  
alignment without choosing parameters the second time.</p>





  
  

  586
  
<pre class="r watch-out"><code>mapping_parameters &lt;- xen_reg$mapping_parameters





  
  

  587
  
xen_reg &lt;- registerSpatialData(object_list = list(Xen_R1, Vis, Xen_R2), 





  
  

  588
  
                               mapping_parameters = mapping_parameters)</code></pre>





  
  

  589
  
<p>You can find a presaved set of parameters <a





  
  

  590
  
href="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/SpatialDataAlignment/Xenium_vs_Visium/mapping_parameters.rds">here</a>.</p>





  
  

  591
  
<pre class="r watch-out"><code>mapping_parameters &lt;- readRDS(&quot;mapping_parameters.rds&quot;)





  
  

  592
  
xen_reg &lt;- registerSpatialData(object_list = list(Xen_R1, Vis, Xen_R2), 





  
  

  593
  
                               mapping_parameters = mapping_parameters)</code></pre>





  
  

  594
  
<p>If the pre-saved parameters are available, the registration can also





  
  

  595
  
be performed without using the shiny app. By using <strong>interactive =





  
  

  596
  
FALSE</strong>, we can register images and VoltRon objects directly.</p>





  
  

  597
  
<pre class="r watch-out"><code>mapping_parameters &lt;- xen_reg$mapping_parameters





  
  

  598
  
xen_reg &lt;- registerSpatialData(object_list = list(Xen_R1, Vis, Xen_R2), 





  
  

  599
  
                               mapping_parameters = mapping_parameters, 





  
  

  600
  
                               interactive = FALSE)</code></pre>





  
  

  601
  
<p>In case there are only two images, <strong>the first image will be





  
  

  602
  
taken as the image of reference</strong>. Hence, in order to align the





  
  

  603
  
first Xenium Replicate to the Visium dataset, we can create a list of





  
  

  604
  
two VoltRon objects as given below.</p>





  
  

  605
  
<pre class="r watch-out"><code>xen_reg &lt;- registerSpatialData(object_list = list(Vis, Xen_R2))</code></pre>





  
  

  606
  
<p><br></p>





  
  

  607
  
</div>





  
  

  608
  
<div id="manual-image-alignment" class="section level3">





  
  

  609
  
<h3>Manual Image Alignment</h3>





  
  

  610
  
<p>Given the diverse types of tissue sections and their complex





  
  

  611
  
morphology, we need an alternative alignment strategy if automated





  
  

  612
  
registration may fail. VoltRon allows <strong>manually choosing





  
  

  613
  
keypoints (or landmarks)</strong> on images that are locations on the





  
  

  614
  
tissue with structural/morphological similarity. Similar to the





  
  

  615
  
automated mode, <strong>the image on the center</strong> will be taken





  
  

  616
  
as reference and the users will be able to observe the quality of the





  
  

  617
  
registration and remove/reselect keypoints as they see fit.</p>





  
  

  618
  
<pre class="r watch-out"><code>xen_reg &lt;- registerSpatialData(object_list = list(Xen_R1, Vis, Xen_R2))</code></pre>





  
  

  619
  
<p><img width="100%" height="100%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/XeniumVisiumRegistration.gif" class="center"></p>





  
  

  620
  
<p><br></p>





  
  

  621
  
<p>You can save and use the same keypoints later, and reproduce the





  
  

  622
  
manual alignment without choosing keypoints for the second time.</p>





  
  

  623
  
<pre class="r watch-out"><code>mapping_parameters &lt;- xen_reg$mapping_parameters





  
  

  624
  
xen_reg &lt;- registerSpatialData(object_list = list(Xen_R1, Vis, Xen_R2), 





  
  

  625
  
                               mapping_parameters = mapping_parameters)</code></pre>





  
  

  626
  
<p>You can find a presaved set of parameters with selected manual





  
  

  627
  
keypoints <a





  
  

  628
  
href="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/SpatialDataAlignment/Xenium_vs_Visium/mapping_parameters_manual.rds">here</a>.</p>





  
  

  629
  
<pre class="r watch-out"><code>mapping_parameters &lt;- readRDS(&quot;mapping_parameters_manual.rds&quot;)





  
  

  630
  
xen_reg &lt;- registerSpatialData(object_list = list(Xen_R1, Vis, Xen_R2), 





  
  

  631
  
                               mapping_parameters = mapping_parameters)</code></pre>





  
  

  632
  
<p>If the pre-saved keypoints are available with parameters, the





  
  

  633
  
registration can also be performed without using the shiny app. By using





  
  

  634
  
<strong>interactive = FALSE</strong>, we can register images and VoltRon





  
  

  635
  
objects directly.</p>





  
  

  636
  
<pre class="r watch-out"><code>mapping_parameters &lt;- xen_reg$mapping_parameters





  
  

  637
  
xen_reg &lt;- registerSpatialData(object_list = list(Xen_R1, Vis, Xen_R2), 





  
  

  638
  
                               mapping_parameters = mapping_parameters, 





  
  

  639
  
                               interactive = FALSE)</code></pre>





  
  

  640
  
<p>In case there are only two images, <strong>the first image will be





  
  

  641
  
taken as the image of reference</strong>. Hence, in order to align the





  
  

  642
  
first Xenium Replicate to the Visium dataset. We can create a list of





  
  

  643
  
two VoltRon objects as given below.</p>





  
  

  644
  
<pre class="r watch-out"><code>xen_reg &lt;- registerSpatialData(object_list = list(Vis, Xen_R2))</code></pre>





  
  

  645
  
<p><br></p>





  
  

  646
  
</div>





  
  

  647
  
<div id="combine-voltron-object" class="section level3">





  
  

  648
  
<h3>Combine VoltRon object</h3>





  
  

  649
  
<p>Now that the VoltRon objects of Xenium and Visium datasets are





  
  

  650
  
accurately aligned, we can combine these objects to create <strong>one





  
  

  651
  
VoltRon object with three layers</strong>. Since all sections are





  
  

  652
  
derived from the same tissue block, we want them to be associated with





  
  

  653
  
the same sample, hence we define the sample name as well. VoltRon will





  
  

  654
  
recognize that all layers are originated from the same sample/block, and





  
  

  655
  
choose the majority assay as the main assay.</p>





  
  

  656
  
<pre class="r watch-out"><code>merge_list &lt;- xen_reg$registered_spat





  
  

  657
  
VRBlock &lt;- merge(merge_list[[1]], merge_list[-1], samples = &quot;10XBlock&quot;)





  
  

  658
  
VRBlock</code></pre>





  
  

  659
  
<pre><code>10XBlock: 





  
  

  660
  
  Layers: Section1 Section2 Section3 





  
  

  661
  
Assays: Xenium(Main) Visium





  
  

  662
  
Features: RNA(Main) </code></pre>





  
  

  663
  
<p>Here, we can quickly check the change in spatial coordinate systems





  
  

  664
  
in the new tissue block. The <code>registerSpatialData</code> function





  
  

  665
  
syncronizes the coordinate systems of all VoltRon objects in the list





  
  

  666
  
before merging. Both Xenium sections have now two coordinate system





  
  

  667
  
where the registered system <strong>main_reg</strong> is the default





  
  

  668
  
one.</p>





  
  

  669
  
<pre class="r watch-out"><code>vrSpatialNames(VRBlock, assay = &quot;all&quot;)</code></pre>





  
  

  670
  
<pre><code>        Assay    Layer   Sample       Spatial     Main





  
  

  671
  
Assay1 Xenium Section1 10XBlock main,main_reg main_reg





  
  

  672
  
Assay2 Visium Section2 10XBlock          main     main





  
  

  673
  
Assay3 Xenium Section3 10XBlock main,main_reg main_reg</code></pre>





  
  

  674
  
<p><br></p>





  
  

  675
  
</div>





  
  

  676
  
<div id="datalabel-transfer-across-layers" class="section level3">





  
  

  677
  
<h3>Data/Label Transfer Across Layers</h3>





  
  

  678
  
<p>The combined VoltRon object of Visium and Xenium datasets can be used





  
  

  679
  
to transfer information across layers and assays. This is accomplished





  
  

  680
  
by aggregating and summarizing, for example, gene counts of cells from





  
  

  681
  
the Xenium assay aligned to Visium spots. Either labels or cell types





  
  

  682
  
can be summarized to generate:</p>





  
  

  683
  
<ul>





  
  

  684
  
<li>pseudo cell type abundance assays or</li>





  
  

  685
  
<li>pseudo gene expression assays.</li>





  
  

  686
  
</ul>





  
  

  687
  
<p><br></p>





  
  

  688
  
<div id="data-transfer-cells-spots" class="section level4">





  
  

  689
  
<h4>Data Transfer (Cells-&gt;Spots)</h4>





  
  

  690
  
<p>We must first determine the names of the assays where labels are





  
  

  691
  
transfered <strong>from</strong> one <strong>to</strong> the other. For





  
  

  692
  
the sake of this tutorial, we can select Assay1 of <strong>Xenium as the





  
  

  693
  
source</strong> assay and the Assay2 of <strong>Visium as the





  
  

  694
  
destination</strong> assay.</p>





  
  

  695
  
<pre class="r watch-out"><code>SampleMetadata(VRBlock)</code></pre>





  
  

  696
  
<pre><code>        Assay    Layer   Sample





  
  

  697
  
Assay1 Xenium Section1 10XBlock





  
  

  698
  
Assay2 Visium Section2 10XBlock





  
  

  699
  
Assay3 Xenium Section3 10XBlock</code></pre>





  
  

  700
  
<p>The <strong>transferData</strong> function detects the types of both





  
  

  701
  
the <strong>source (from)</strong> and the <strong>destination





  
  

  702
  
(to)</strong> assays and determines the how the data should be





  
  

  703
  
transfered. We can first transfer data from the Xenium assay to the





  
  

  704
  
Visium assay (hence <strong>Cells -&gt; Spots</strong>), the raw count





  
  

  705
  
data of each cell in the source Xenium assay will be aggregated into





  
  

  706
  
spots in a newly create pseudo Visium assay. The new assay with





  
  

  707
  
aggregated counts will be named <strong>Visium_pseudo</strong>.</p>





  
  

  708
  
<pre class="r watch-out"><code>VRBlock &lt;- transferData(VRBlock, from = &quot;Assay1&quot;, to = &quot;Assay2&quot;)</code></pre>





  
  

  709
  
<p>VoltRon supports multiple feature type within each assay. Now, the





  
  

  710
  
Visium assay includes two spot-type features:</p>





  
  

  711
  
<ul>





  
  

  712
  
<li>the original Visium spot feature counts,</li>





  
  

  713
  
<li>a pseudo Visium feature count matrix with aggregated Xenium raw





  
  

  714
  
counts.</li>





  
  

  715
  
</ul>





  
  

  716
  
<pre class="r watch-out"><code>vrMainAssay(VRBlock) &lt;- &quot;Visium&quot;





  
  

  717
  
VRBlock</code></pre>





  
  

  718
  
<pre><code>VoltRon Object 





  
  

  719
  
10XBlock: 





  
  

  720
  
  Layers: Section1 Section2 Section3 





  
  

  721
  
Assays: Visium(Main) Xenium 





  
  

  722
  
Features: RNA(Main) RNA_pseudo </code></pre>





  
  

  723
  
<p>We can now visualize both the original and aggregated counts of a





  
  

  724
  
gene, such as ERBB2 and ESR1 that marks ductal carcinoma in situ (DCIS)





  
  

  725
  
regions, to validate the correlation of gene signatures across adjacent





  
  

  726
  
tissue sections, and to validate the accuracy of the automated image





  
  

  727
  
alignment. Here, PGR is also expressed at a small DCIS region found on





  
  

  728
  
adipocyte niche of the tissue.</p>





  
  

  729
  
<pre class="r watch-out"><code>library(patchwork)





  
  

  730
  
vrMainFeatureType(VRBlock, assay = &quot;Visium&quot;) &lt;- &quot;RNA&quot;





  
  

  731
  
g1 &lt;- vrSpatialFeaturePlot(VRBlock,  





  
  

  732
  
                           features = c(&quot;ERBB2&quot;, &quot;ESR1&quot;, &quot;PGR&quot;), crop = FALSE, 





  
  

  733
  
                           norm = FALSE, ncol = 3)





  
  

  734
  
vrMainFeatureType(VRBlock, assay = &quot;Visium&quot;) &lt;- &quot;RNA_pseudo&quot;





  
  

  735
  
g2 &lt;- vrSpatialFeaturePlot(VRBlock, 





  
  

  736
  
                           features = c(&quot;ERBB2&quot;, &quot;ESR1&quot;, &quot;PGR&quot;), crop = FALSE, 





  
  

  737
  
                           norm = FALSE, ncol = 3)





  
  

  738
  
g1 / g2</code></pre>





  
  

  739
  
<p><img width="100%" height="100%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/registration_TACSTD2.png" class="center"></p>





  
  

  740
  
</div>





  
  

  741
  
<div id="data-transfer-spots-cells" class="section level4">





  
  

  742
  
<h4>Data Transfer (Spots-&gt;Cells)</h4>





  
  

  743
  
<p>A similar transfer can be achieved on the opposite direction. We can





  
  

  744
  
select Assay2 of <strong>Visium as the source</strong> assay and Assay1





  
  

  745
  
of <strong>Xenium as the destination</strong>, thus we can transfer





  
  

  746
  
whole transcriptome counts of the Visium assays to Xenium to create new





  
  

  747
  
feature sets for Xenium data with more features originally available in





  
  

  748
  
the Xenium panel.</p>





  
  

  749
  
<pre class="r watch-out"><code>vrMainFeatureType(VRBlock, assay = &quot;Visium&quot;) &lt;- &quot;RNA&quot;





  
  

  750
  
VRBlock &lt;- transferData(VRBlock, from = &quot;Assay2&quot;, to = &quot;Assay1&quot;)</code></pre>





  
  

  751
  
<p>We now set the main feature set of the Xenium assays.</p>





  
  

  752
  
<pre class="r watch-out"><code>vrMainFeatureType(VRBlock, assay = &quot;Xenium&quot;) &lt;- &quot;RNA_pseudo&quot;





  
  

  753
  
vrMainFeatureType(VRBlock, assay = &quot;all&quot;)</code></pre>





  
  

  754
  
<pre><code>   Assay    Feature





  
  

  755
  
1 Assay1 RNA_pseudo





  
  

  756
  
2 Assay2        RNA





  
  

  757
  
3 Assay3        RNA</code></pre>





  
  

  758
  
<pre class="r watch-out"><code>library(patchwork)





  
  

  759
  
g1 &lt;- vrSpatialFeaturePlot(VRBlock, 





  
  

  760
  
                           assay = &quot;Assay1&quot;, features = c(&quot;ERBB2&quot;, &quot;ESR1&quot;, &quot;PGR&quot;), 





  
  

  761
  
                           crop = TRUE, norm = FALSE, alpha = 1, n.tile = 300, ncol = 3)





  
  

  762
  
g2 &lt;- vrSpatialFeaturePlot(VRBlock, 





  
  

  763
  
                           assay = &quot;Assay2&quot;, features = c(&quot;ERBB2&quot;, &quot;ESR1&quot;, &quot;PGR&quot;), 





  
  

  764
  
                           crop = TRUE, norm = FALSE, alpha = 1, ncol = 3)





  
  

  765
  
g1 / g2</code></pre>





  
  

  766
  
<p><img width="100%" height="100%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/registration_spot2cell.png" class="center"></p>





  
  

  767
  
</div>





  
  

  768
  
<div id="label-transfer-cells-spots" class="section level4">





  
  

  769
  
<h4>Label Transfer (Cells-&gt;Spots)</h4>





  
  

  770
  
<p>The <strong>transferData</strong> function can also transfer





  
  

  771
  
<strong>metadata features</strong> across layers and assays. In this





  
  

  772
  
case, we will transfer cell type labels that were trained on the Xenium





  
  

  773
  
sections onto the Visium sections. We will use the cluster labels





  
  

  774
  
generated at the end of the Xenium analysis section of workflow from <a





  
  

  775
  
href="spotanalysis.html">Cell/Spot Analysis</a>. You can download the





  
  

  776
  
VoltRon object with clustered and annotated Xenium cells along with the





  
  

  777
  
Visium assay from <a





  
  

  778
  
href="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/SpatialDataAlignment/Xenium_vs_Visium/VRBlock_data_clustered.rds">here</a>.</p>





  
  

  779
  
<pre class="r watch-out"><code>VRBlock &lt;- readRDS(&quot;VRBlock_data_clustered.rds&quot;)





  
  

  780
  
vrSpatialPlot(VRBlock, assay = &quot;Xenium&quot;, group.by = &quot;CellType&quot;, pt.size = 0.4)</code></pre>





  
  

  781
  
<p><img width="100%" height="100%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/cellspot_spatial_xenium_annotated.png" class="center"></p>





  
  

  782
  
<p>Here, we can see that both Xenium layers are clustered and annotated





  
  

  783
  
where we can use these cell annotations and transfer them to the Visium





  
  

  784
  
assay to create an assay of <strong>estimated cell type





  
  

  785
  
abundances</strong>. If the features argument is specified, and if its a





  
  

  786
  
single metadata feature with, e.g. cell types, then the each spot at the





  
  

  787
  
new pseudo Visium will be collection of abundances of the categories





  
  

  788
  
within that metadata feature.</p>





  
  

  789
  
<pre class="r watch-out"><code>VRBlock &lt;- transferData(VRBlock, from = &quot;Assay1&quot;, to = &quot;Assay2&quot;, features = &quot;CellType&quot;, 





  
  

  790
  
                        new_assay_name = &quot;Visium_CellType&quot;)





  
  

  791
  
VRBlock</code></pre>





  
  

  792
  
<pre><code>VoltRon Object 





  
  

  793
  
10XBlock: 





  
  

  794
  
  Layers: Section1 Section2 Section3 





  
  

  795
  
Assays: Visium(Main) Xenium 





  
  

  796
  
Features: RNA_pseudo(Main) RNA Visium_CellType </code></pre>





  
  

  797
  
<p>By visualizing the transferred labels on the Visium spots, we can see





  
  

  798
  
abundance of some DCIS and invasive tumor subtypes.</p>





  
  

  799
  
<pre class="r watch-out"><code>vrMainFeatureType(VRBlock) &lt;- &quot;Visium_CellType&quot;





  
  

  800
  
vrSpatialFeaturePlot(VRBlock, assay = &quot;Visium&quot;,





  
  

  801
  
                     features = c(&quot;IT_1&quot;,&quot;DCIS_2&quot;), 





  
  

  802
  
                     crop = TRUE, alpha = 1, ncol = 3)</code></pre>





  
  

  803
  
<p><img width="100%" height="100%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/registration_CellType.png" class="center"></p>





  
  

  804
  
<p><br></p>





  
  

  805
  
</div>





  
  

  806
  
<div id="label-transfer-rois-" class="section level4">





  
  

  807
  
<h4>Label Transfer (ROIs-&gt;…)</h4>





  
  

  808
  
<p>VoltRon allows users to annotate regions of interests (ROIs) in a





  
  

  809
  
given assay and transfer the annotations to these ROIs across other





  
  

  810
  
assays within the same tissue block. Let us annotate two specific tumor





  
  

  811
  
regions in the Visium section. In the process, a new assay called





  
  

  812
  
<strong>ROIAnnotation</strong> will be added to the VoltRon object.</p>





  
  

  813
  
<pre class="r watch-out"><code>VRBlock &lt;- annotateSpatialData(VRBlock, assay = &quot;Visium&quot;, 





  
  

  814
  
                               label = &quot;annotation&quot;, use.image.only = TRUE)</code></pre>





  
  

  815
  
<p><img width="80%" height="80%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/registration_ROIAnnotation.png" class="center"></p>





  
  

  816
  
<p><br></p>





  
  

  817
  
<p>You can observe the changes in the object and check the assay ID of





  
  

  818
  
this new ROI type assay using <code>SampleMetadata</code> function.</p>





  
  

  819
  
<pre class="r watch-out"><code>VRBlock</code></pre>





  
  

  820
  
<pre><code>VoltRon Object 





  
  

  821
  
10XBlock: 





  
  

  822
  
  Layers: Section1 Section2 Section3 





  
  

  823
  
Assays: Xenium(Main) Visium ROIAnnotation 





  
  

  824
  
Features: RNA(Main) </code></pre>





  
  

  825
  
<pre class="r watch-out"><code>SampleMetadata(VRBlock)</code></pre>





  
  

  826
  
<pre><code>               Assay    Layer   Sample





  
  

  827
  
Assay1        Xenium Section1 10XBlock





  
  

  828
  
Assay2        Visium Section2 10XBlock





  
  

  829
  
Assay3        Xenium Section3 10XBlock





  
  

  830
  
Assay4 ROIAnnotation Section2 10XBlock</code></pre>





  
  

  831
  
<p>The metadata of the ROI assay will include the annotation of the ROIs





  
  

  832
  
as well.</p>





  
  

  833
  
<pre class="r watch-out"><code>Metadata(VRBlock, assay = &quot;ROIAnnotation&quot;)</code></pre>





  
  

  834
  
<pre><code>                               Assay    Layer   Sample      annotation





  
  

  835
  
InvasiveTumor_Assay4   ROIAnnotation Section2 10XBlock   InvasiveTumor





  
  

  836
  
DuctalCarcinoma_Assay4 ROIAnnotation Section2 10XBlock DuctalCarcinoma</code></pre>





  
  

  837
  
<p>Now we can transfer the ROI labels from the





  
  

  838
  
<strong>annotation</strong> metadata column and define the same metadata





  
  

  839
  
column in the remaining assays.</p>





  
  

  840
  
<pre class="r watch-out"><code>VRBlock &lt;- transferData(object = VRBlock, from = &quot;Assay4&quot;, to = &quot;Assay1&quot;, 





  
  

  841
  
                        features = &quot;annotation&quot;)





  
  

  842
  
VRBlock &lt;- transferData(object = VRBlock, from = &quot;Assay4&quot;, to = &quot;Assay3&quot;, 





  
  

  843
  
                        features = &quot;annotation&quot;)</code></pre>





  
  

  844
  
<p>Let us observe the changes across all assays.</p>





  
  

  845
  
<pre class="r watch-out"><code>vrSpatialPlot(VRBlock, group.by = &quot;annotation&quot;, assay = &quot;Xenium&quot;, crop = TRUE)





  
  

  846
  
vrSpatialPlot(VRBlock, group.by = &quot;annotation&quot;, assay = &quot;Visium&quot;, crop = TRUE)</code></pre>





  
  

  847
  
<p><img width="100%" height="100%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/registration_ROI_xenium.png" class="center"></p>





  
  

  848
  
<p><img width="50%" height="50%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/registration_ROI_visium.png" class="center"></p>





  
  

  849
  
<p><br></p>





  
  

  850
  
<p>You can also use the <strong>addSpatialLayer</strong> function to





  
  

  851
  
overlay annotation segments to the spatial plot of the Xenium data.</p>





  
  

  852
  
<pre class="r watch-out"><code>vrSpatialPlot(VRBlock_new2, group.by = &quot;CellType&quot;, assay = &quot;Assay1&quot;, crop = TRUE) |&gt;





  
  

  853
  
  addSpatialLayer(VRBlock_new2, assay = &quot;ROIAnnotation&quot;, group.by = &quot;annotation&quot;, spatial = &quot;main&quot;, alpha = 0.4)</code></pre>





  
  

  854
  
<p><img width="50%" height="50%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/registration_ROI_xenium_overlay.png" class="center"></p>





  
  

  855
  
<p><br></p>





  
  

  856
  
</div>





  
  

  857
  
</div>





  
  

  858
  
</div>





  
  

  859
  
<div id="alignment-of-xenium-and-he" class="section level2">





  
  

  860
  
<h2>Alignment of Xenium and H&amp;E</h2>





  
  

  861
  
<p>In this use case, we will align <strong>immunofluorescence





  
  

  862
  
(IF)</strong> of the <strong>Xenium In Situ</strong> platform to an





  
  

  863
  
<strong>H&amp;E images</strong> generated from the same sections as the





  
  

  864
  
Xenium. VoltRon provides built-in utilities to import images as spatial





  
  

  865
  
datasets where <strong>tiles</strong> are the spatial points. We will





  
  

  866
  
import both Xenium and H&amp;E images into two separate VoltRon objects





  
  

  867
  
and overlay H&amp;E images.</p>





  
  

  868
  
<p>You can download the Xenium readout and the H&amp;E image of the same





  
  

  869
  
tissue section from the <a





  
  

  870
  
href="https://www.10xgenomics.com/products/xenium-in-situ/preview-dataset-human-breast">10x





  
  

  871
  
Genomics website</a> (specifically, import <strong>In Situ Replicate





  
  

  872
  
1</strong> and <strong>Supplemental: Post-Xenium H&amp;E image





  
  

  873
  
(TIFF)</strong>).</p>





  
  

  874
  
<pre class="r watch-out"><code>library(VoltRon)





  
  

  875
  





  
  

  876
  
# import Xenium 





  
  

  877
  
Xen_R1 &lt;- importXenium(&quot;Xenium_R1/outs&quot;, sample_name = &quot;XeniumR1&quot;)





  
  

  878
  





  
  

  879
  
# import H&amp;E image and build a VoltRon object





  
  

  880
  
Xen_R1_image &lt;- importImageData(&quot;Xenium_FFPE_Human_Breast_Cancer_Rep1_he_image.tif&quot;,





  
  

  881
  
                                sample_name = &quot;XeniumR1image&quot;, 





  
  

  882
  
                                channel_names = &quot;H&amp;E&quot;)





  
  

  883
  
Xen_R1_image</code></pre>





  
  

  884
  
<pre><code>VoltRon Object 





  
  

  885
  
XeniumR1image: 





  
  

  886
  
  Layers: Section1 





  
  

  887
  
Assays: ImageData(Main) </code></pre>





  
  

  888
  
<p>Lets take a look at the image of the Xen_R1_image object</p>





  
  

  889
  
<pre class="r watch-out"><code>vrImages(Xen_R1_image)</code></pre>





  
  

  890
  
<p><img width="70%" height="70%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/importdata_HE.png" class="center"></p>





  
  

  891
  
<p><br></p>





  
  

  892
  
<div id="automated-image-alignment-1" class="section level3">





  
  

  893
  
<h3>Automated Image Alignment</h3>





  
  

  894
  
<p>We can use the <strong>registerSpatialData</strong> function to





  
  

  895
  
warp/align images across multiple VoltRon objects and define these





  
  

  896
  
aligned images additional channels of existing coordinate systems of





  
  

  897
  
assays in one of these VoltRon objects.</p>





  
  

  898
  
<p>First we align the H&amp;E image to the DAPI image of the Xenium





  
  

  899
  
replicate. Similar to the first use case, we need to negate the DAPI





  
  

  900
  
image and change the alignment of the image to match it with the H&amp;E





  
  

  901
  
image. We can also scale the resolution of the H&amp;E image to





  
  

  902
  
9103.71x6768.63.</p>





  
  

  903
  
<pre class="r watch-out"><code>xen_reg &lt;- registerSpatialData(object_list = list(Xen_R1, Xen_R1_image))</code></pre>





  
  

  904
  
<p><img width="92%" height="92%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/registration_HE_function.png" class="center"></p>





  
  

  905
  
<p><br></p>





  
  

  906
  
<p>Now we create a new channel for the existing coordinate system of the





  
  

  907
  
Xenium data. Here, the spatial key of the registered H&amp;E image will





  
  

  908
  
be <strong>main_reg</strong>. We choose the destination of the





  
  

  909
  
registered image which is the first Assay of the Xenium data





  
  

  910
  
(i.e. <strong>Assay1</strong>). The original DAPI coordinate system, and





  
  

  911
  
we give a name for the new image/channel which is





  
  

  912
  
<strong>H&amp;E</strong>.</p>





  
  

  913
  
<pre class="r watch-out"><code>Xen_R1_image_reg &lt;- xen_reg$registered_spat[[2]]





  
  

  914
  
vrImages(Xen_R1[[&quot;Assay1&quot;]], channel = &quot;H&amp;E&quot;) &lt;- vrImages(Xenium_reg, name = &quot;main_reg&quot;, channel = &quot;H&amp;E&quot;)</code></pre>





  
  

  915
  
<p>We can now observe the new channels (H&amp;E) available for the





  
  

  916
  
Xenium assay using <strong>vrImageChannelNames</strong>.</p>





  
  

  917
  
<pre class="r watch-out"><code>vrImageChannelNames(Xen_R1)</code></pre>





  
  

  918
  
<pre><code>       Assay    Layer           Sample Spatial                                               Channels





  
  

  919
  
Assay1 GeoMx Section1 prolonged case 4    main scanimage,DNA,PanCK,CD45,Alpha Smooth Muscle Actin,H&amp;E</code></pre>





  
  

  920
  
<p>We can call the registered H&amp;E image of the Xenium data or later





  
  

  921
  
put the aligned H&amp;E when calling <strong>vrSpatialPlot</strong> or





  
  

  922
  
<strong>vrSpatialFeaturePlot</strong>.</p>





  
  

  923
  
<pre class="r watch-out"><code>vrImages(Xen_R1, channel = &quot;H&amp;E&quot;, scale.perc = 5)</code></pre>





  
  

  924
  
<p><img width="70%" height="70%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/registration_HE.png" class="center"></p>





  
  

  925
  
<p><br></p>





  
  

  926
  
</div>





  
  

  927
  
</div>





  
  

  928
  
<div id="alignment-of-visium-and-visium" class="section level2">





  
  

  929
  
<h2>Alignment of Visium and Visium</h2>





  
  

  930
  
<p>In the next use case, we will align <strong>H&amp;E images</strong>





  
  

  931
  
associated with Visium data generated from tissue block sections of





  
  

  932
  
<strong>adult humans with postmortem dorsolateral prefrontal cortex





  
  

  933
  
(DLPFC)</strong>. Two pairs of adjacent sections was obtained from the





  
  

  934
  
tissue block of the third donor. Each pair are composed of two 10 <span





  
  

  935
  
class="math inline">\(\mu\)</span>m serial tissue sections, and pairs





  
  

  936
  
are located 300 <span class="math inline">\(\mu\)</span>m apart from





  
  

  937
  
each other. Hence, we align each pair individually. The datasets can be





  
  

  938
  
downloaded from <a





  
  

  939
  
href="https://research.libd.org/spatialLIBD/">here</a>.</p>





  
  

  940
  
<pre class="r watch-out"><code>library(VoltRon)





  
  

  941
  
DLPFC_1 &lt;- importVisium(&quot;DLPFC/151673&quot;, sample_name = &quot;DLPFC_1&quot;)





  
  

  942
  
DLPFC_2 &lt;- importVisium(&quot;DLPFC/151674&quot;, sample_name = &quot;DLPFC_2&quot;)





  
  

  943
  
DLPFC_3 &lt;- importVisium(&quot;DLPFC/151675&quot;, sample_name = &quot;DLPFC_3&quot;)





  
  

  944
  
DLPFC_4 &lt;- importVisium(&quot;DLPFC/151676&quot;, sample_name = &quot;DLPFC_4&quot;)</code></pre>





  
  

  945
  
<p><br></p>





  
  

  946
  
<div id="automated-image-alignment-2" class="section level3">





  
  

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<h3>Automated Image Alignment</h3>





  
  

  948
  
<p>We will again use the registerSpatialData function to





  
  

  949
  
<strong>automatically register two Visium assays (two H&amp;E





  
  

  950
  
images)</strong>. This time, we will use the





  
  

  951
  
<strong>BRUTE-FORCE</strong> method for automated alignment which we





  
  

  952
  
found to be more accurate compared to FLANN when aligning two H&amp;E





  
  

  953
  
images. The shiny app also provides two tuning parameters that used by





  
  

  954
  
the the BRUTE-FORCE workflow:</p>





  
  

  955
  
<ul>





  
  

  956
  
<li><strong># of Features</strong> option specifies the number of





  
  

  957
  
maximum image features spotted within each image which later be used to





  
  

  958
  
match to the other image.</li>





  
  

  959
  
<li><strong>Match %</strong> specifies the percentage of these features





  
  

  960
  
matching at max which in turn used to compute the





  
  

  961
  
registration/transformation matrix.</li>





  
  

  962
  
</ul>





  
  

  963
  
<p>We will use <strong>1000 features</strong> for this alignment, set





  
  

  964
  
<strong>Match %</strong> to 20% of the features to be matched across





  
  

  965
  
images. The quality of the alignment will be determined by the fine





  
  

  966
  
tuning of these parameters where users will immediately observe the





  
  

  967
  
alignment quality looking at the slideshow.</p>





  
  

  968
  
<pre class="r watch-out"><code>DLPFC_list &lt;- list(DLPFC_1, DLPFC_2)





  
  

  969
  
reg1and2 &lt;- registerSpatialData(object_list = DLPFC_list)</code></pre>





  
  

  970
  
<p><img width="100%" height="100%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/VisiumDLFPCRegistration.gif" class="center"></p>





  
  

  971
  
<p><br></p>





  
  

  972
  
<p>We can now apply a similar alignment across the second pair of





  
  

  973
  
VoltRon objects. We will use <strong>800 features</strong> for this





  
  

  974
  
alignment, set <strong>Match %</strong> to 50% of the features to be





  
  

  975
  
matched across images.</p>





  
  

  976
  
<pre class="r watch-out"><code>DLPFC_list &lt;- list(DLPFC_3, DLPFC_4)





  
  

  977
  
reg3and4 &lt;- registerSpatialData(object_list = DLPFC_list)</code></pre>





  
  

  978
  
<p><br></p>





  
  

  979
  
</div>





  
  

  980
  
<div id="d-spot-clustering" class="section level3">





  
  

  981
  
<h3>3D Spot Clustering</h3>





  
  

  982
  
<p>We can now combine all sections into one VoltRon object. There are





  
  

  983
  
two pairs of serial tissue sections, but both pairs (thus 4 sections)





  
  

  984
  
are from the same tissue block. Hence, we can combine these two lists





  
  

  985
  
into one list and merge VoltRon objects even though sections were





  
  

  986
  
aligned separately.</p>





  
  

  987
  
<pre class="r watch-out"><code>merge_list &lt;- c(reg1and2$registered_spat, reg3and4$registered_spat)





  
  

  988
  
SRBlock &lt;- merge(merge_list[[1]], merge_list[-1], samples = &quot;DLPFC_Block&quot;)





  
  

  989
  
SRBlock</code></pre>





  
  

  990
  
<pre><code>VoltRon Object 





  
  

  991
  
DLPFC_Block: 





  
  

  992
  
  Layers: Section1 Section2 Section3 Section4 





  
  

  993
  
Assays: Visium(Main) </code></pre>





  
  

  994
  
<p><br></p>





  
  

  995
  
<p>Aligning spots along the z dimension allows us to cluster these spots





  
  

  996
  
using both the gene expression similarities and spatial adjacency (both





  
  

  997
  
along the x-y direction and in the z direction). We first generate a





  
  

  998
  
spatial neighborhood graph and use this graph along with the gene





  
  

  999
  
expression neighborhood graph <strong>(under development)</strong>.</p>





  
  

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