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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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<title>ondisk</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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<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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<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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if (document.readyState && document.readyState === "complete") { |
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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-left: auto; |
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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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.tabbed-pane { |
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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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display: list-item; |
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} |
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details > summary > p:only-child { |
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display: inline; |
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} |
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pre code { |
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padding: 0; |
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} |
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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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<ul class="nav navbar-nav navbar-right"> |
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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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<span class="fa fa-envelope-o"></span> |
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Contact |
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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> |
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<a href="https://bioinformatics.mdc-berlin.de">Altuna Lab/BIMSB Bioinfo</a> |
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</li> |
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<li> |
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<a href="https://www.mdc-berlin.de/landthaler">Landthaler Lab/BIMSB</a> |
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</li> |
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</ul> |
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</li> |
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<li class="dropdown"> |
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GitHub |
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</a> |
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<ul class="dropdown-menu" role="menu"> |
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<li> |
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<a href="https://github.com/BIMSBbioinfo/VoltRon">VoltRon</a> |
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</li> |
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<li> |
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<a href="https://github.com/BIMSBbioinfo">BIMSB Bioinfo</a> |
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</li> |
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</ul> |
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</div><!--/.nav-collapse --> |
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<div id="header"> |
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<h1 class="title toc-ignore">ondisk</h1> |
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</div> |
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<style> |
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.title{ |
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display: none; |
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} |
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body { |
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text-align: justify |
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} |
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.center { |
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display: block; |
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margin-left: auto; |
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margin-right: auto; |
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} |
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</style> |
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<style type="text/css"> |
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.watch-out { |
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color: black; |
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} |
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</style> |
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<p><br></p> |
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<div id="import-visiumhd-data" class="section level2"> |
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<h2>Import VisiumHD Data</h2> |
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<p>We first have to download some packages that are necessary to import |
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datasets from <code>.parquet</code> and <code>.h5</code> files provided |
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by the VisiumHD readouts.</p> |
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<pre class="r watch-out"><code>install.packages("arrow") |
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BiocManager::install("rhdf5") |
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library(arrow) |
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library(rhdf5)</code></pre> |
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<p>We use the <strong>importVisiumHD</strong> function to start |
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analyzing the data. The data has 393401 spots which we will use |
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OnDisk-backed methods to efficiently manipulate, analyze and visualize |
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these spots.</p> |
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<p>The VisiumHD readouts provide multiple bin sizes which are aggregated |
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versions of the original 2<span class="math inline">\(\mu\)</span>m |
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<span class="math inline">\(x\)</span> 2<span |
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class="math inline">\(\mu\)</span>m capture spots. The default bin sizes |
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are <strong>(i)</strong> 2<span class="math inline">\(\mu\)</span>m |
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<span class="math inline">\(x\)</span> 2<span |
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class="math inline">\(\mu\)</span>m, <strong>(ii)</strong> 8<span |
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class="math inline">\(\mu\)</span>m <span |
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class="math inline">\(x\)</span> 8<span |
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class="math inline">\(\mu\)</span>m and <strong>(iii)</strong> 16<span |
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class="math inline">\(\mu\)</span>m <span |
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class="math inline">\(x\)</span> 16<span |
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class="math inline">\(\mu\)</span>m.</p> |
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<pre class="r watch-out"><code>hddata <- importVisiumHD(dir.path = "VisiumHD/outs/", |
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bin.size = "8", |
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resolution_level = "hires")</code></pre> |
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<p><br></p> |
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</div> |
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<div id="savingloading-voltron-objects" class="section level2"> |
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<h2>Saving/Loading VoltRon Objects</h2> |
|
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<p>We use <strong>BPCells</strong> and <strong>ImageArray</strong> |
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packages to accelerate operations of feature matrices and images. Here |
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<strong>BPCells</strong> allows users access and operate on large |
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feature matrices or clustering/spatial analysis, while |
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<strong>ImageArray</strong> provides <a |
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href="https://en.wikipedia.org/wiki/Pyramid_(image_processing)">pyramids |
|
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images</a> to allow fast access to large microscopy images. You can |
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download these package from GitHub using <strong>devtools</strong>.</p> |
|
|
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<pre class="r watch-out"><code>devtools::install_github("bnprks/BPCells/r") |
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|
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devtools::install_github("BIMSBbioinfo/ImageArray") |
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library(BPCells) |
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library(ImageArray)</code></pre> |
|
|
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<p>We can now save the VoltRon object to disk, large matrices and images |
|
|
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will be written to either <strong>hdf5</strong> or <strong>zarr</strong> |
|
|
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files depending on the <strong>format</strong> arguement, and the rest |
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of the R object would be written to an <code>.rds</code> file, both |
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under the designated <strong>output</strong>.</p> |
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|
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<pre class="r watch-out"><code>hddata <- saveVoltRon(hddata, format = "HDF5VoltRon", output = "data/VisiumHD")</code></pre> |
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<p>If you want you can load the VoltRon object from the same path as you |
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|
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have saved.</p> |
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<pre class="r watch-out"><code>hddata <- loadVoltRon("data/VisiumHD/")</code></pre> |
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<p><br></p> |
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|
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</div> |
|
|
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<div id="cellspot-analysis" class="section level2"> |
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|
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<h2>Cell/Spot Analysis</h2> |
|
|
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<p>The <strong>BPCells</strong> package provides fast methods to achieve |
|
|
520 |
operations common to single cell analysis such as filtering, |
|
|
521 |
normalization and dimensionality reduction. Here we have an example of |
|
|
522 |
single-cell like clustering of VisiumHD bins which is efficiently |
|
|
523 |
clustered.</p> |
|
|
524 |
<pre class="r watch-out"><code>spatialpoints <- vrSpatialPoints(hddata)[as.vector(Metadata(hddata)$Count > 10)] |
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|
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hddata <- subset(hddata, spatialpoints = spatialpoints) |
|
|
526 |
hddata <- normalizeData(hddata, sizefactor = 10000) |
|
|
527 |
hddata <- getFeatures(hddata, n = 3000) |
|
|
528 |
selected_features <- getVariableFeatures(hddata) |
|
|
529 |
hddata <- getPCA(hddata, features = selected_features, dims = 30) |
|
|
530 |
hddata <- getUMAP(hddata, dims = 1:30)</code></pre> |
|
|
531 |
<p>We can now visualized genes over embedding or spatial plots.</p> |
|
|
532 |
<pre class="r watch-out"><code>vrEmbeddingFeaturePlot(hddata, features = "Nrgn", embedding = "umap") |
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vrSpatialFeaturePlot(hddata, features = "Nrgn")</code></pre> |
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<table> |
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<tbody> |
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<tr style="vertical-align: center"> |
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<td style="width:50%; vertical-align: center"> |
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<img src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/ondisk_embeddingfeature.png" class="center"> |
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</td> |
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<td style="width:55%; vertical-align: center"> |
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<img src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/ondisk_spatialfeature.png" class="center"> |
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</td> |
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</tr> |
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</tbody> |
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</table> |
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<p><br></p> |
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</div> |
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<div id="spatial-data-alignment" class="section level2"> |
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<h2>Spatial Data Alignment</h2> |
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|
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<p>The image registration workflow in the <a |
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|
551 |
href="registration.html">Spatial Data Alignment</a> tutorial can also be |
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552 |
conducted using disk-backed methods of the VoltRon package.</p> |
|
|
553 |
<pre class="r watch-out"><code>library(VoltRon) |
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Xen_R1 <- importXenium("Xenium_R1/outs", sample_name = "XeniumR1", resolution_level = 3) |
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Xen_R1_image <- importImageData("Xenium_FFPE_Human_Breast_Cancer_Rep1_he_image.tif", |
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sample_name = "XeniumR1image", |
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image_name = "H&E")</code></pre> |
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<p><br></p> |
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<p>We can save both Xenium and H&E (image) datasets to disk before |
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using the mini Shiny app for registration</p> |
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<pre class="r watch-out"><code>Xen_R1_disk <- saveVoltRon(Xen_R1, |
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format = "HDF5VoltRon", |
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output = "data/Xen_R1_h5", replace = TRUE) |
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Xen_R1_image_disk <- saveVoltRon(Xen_R1_image, |
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format = "HDF5VoltRon", |
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output = "data/Xen_R1_image_h5", replace = TRUE)</code></pre> |
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<p><br></p> |
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<p>These disk-based datasets can then be loaded from the disk |
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569 |
easily.</p> |
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<pre class="r watch-out"><code>Xen_R1_disk <- loadVoltRon("../data/OnDisk/Xen_R1_h5/") |
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Xen_R1_image_disk <- loadVoltRon("../data/OnDisk/Xen_R1_image_h5/")</code></pre> |
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<p><br></p> |
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<p>VoltRon stores large images as pyramids to increase interactive |
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visualization efficiency. This storage strategy allows shiny apps to |
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zoom in to tissue niches in a speedy fashion. VoltRon incorporates |
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<code>Image_Array</code> objects (<a |
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href="https://github.com/BIMSBbioinfo/ImageArray" |
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class="uri">https://github.com/BIMSBbioinfo/ImageArray</a>) to define |
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these pyramids.</p> |
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<pre class="r watch-out"><code>vrImages(Xen_R1_image_disk, as.raster = TRUE)</code></pre> |
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<pre><code>Image_Array Object |
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582 |
Series 1 of size (3,27587,20511) |
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Series 2 of size (3,13794,10256) |
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Series 3 of size (3,6897,5128) |
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Series 4 of size (3,3449,2564) |
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Series 5 of size (3,1725,1282) |
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Series 6 of size (3,863,641) |
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Series 7 of size (3,432,321) </code></pre> |
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<p><br></p> |
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<p>We can now visualize and align the Xenium and H&E objects.</p> |
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<pre class="r watch-out"><code># Align spatial data |
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xen_reg <- registerSpatialData(object_list = list(Xen_R1_disk, Xen_R1_image_disk))</code></pre> |
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<div> |
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<p><video width="100%" height="100%" controls autoplay> |
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<source src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/video_temp.mov" type="video/mp4"> |
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Your browser does not support the video tag. </video></p> |
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</div> |
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<p><br></p> |
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<pre class="r watch-out"><code># transfer aligned H&E to Xenium data |
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600 |
Xenium_reg <- xen_reg$registered_spat[[2]] |
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vrImages(Xen_R1_disk[["Assay1"]], name = "main", channel = "H&E") <- vrImages(Xenium_reg, name = "H&E_reg") |
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|
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# visualize |
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vrImages(Xen_R1_disk, channel = "H&E", scale.perc = 10)</code></pre> |
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605 |
<p><br></p> |
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<p><img width="92%" height="92%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/ondisk_alignedHE.png" class="center"></p> |
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</div> |
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</div> |
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