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- - -Content not found. Please use links in the navbar. - -
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YEAR: 2023
-COPYRIGHT HOLDER: stCCA authors
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Copyright (c) 2023 stCCA authors

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Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

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All vignettes

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  • -

    Revanth Krishna. Maintainer. -

    -
  • -
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Citation

- Source: DESCRIPTION -
-
- - -

Guo N, Vargas J, Fritz D, Krishna R, Zhang F (2023). -STew: STew. -R package version 2.0, https://github.com/fanzhanglab/STew. -

-
@Manual{,
-  title = {STew: STew},
-  author = {Nanxi Guo and Juan Vargas and Douglas Fritz and Revanth Krishna and Fan Zhang},
-  year = {2023},
-  note = {R package version 2.0},
-  url = {https://github.com/fanzhanglab/STew},
-}
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- // loop to fetch from lvl0, lvl1, etc. - for (var idx in hierarchy) { - words = words.concat(hierarchy[idx].matchedWords); - } - - var content = hit._highlightResult.content; - if (content) { - words = words.concat(content.matchedWords); - } - - // return unique words - var words_uniq = [...new Set(words)]; - return words_uniq; -} - -function updateHitURL(hit) { - - var words = matchedWords(hit); - var url = ""; - - if (hit.anchor) { - url = hit.url_without_anchor + '?q=' + escape(words.join(" ")) + '#' + hit.anchor; - } else { - url = hit.url + '?q=' + escape(words.join(" ")); - } - - return url; -} diff --git a/docs/index.html b/docs/index.html deleted file mode 100644 index 2ca40ce..0000000 --- a/docs/index.html +++ /dev/null @@ -1,245 +0,0 @@ - - - - - - - -STew • STew - - - - - - - - - - - - -
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    R-CMD-check

    - - -

    -We introduce stCCA, a multi-view representation learning method for spatial transcriptomic data, to jointly characterize the gene expression variation and spatial information in the shared low-dimenion space in a scalable manner. stCCA will output distinct spatially informed cell gradients, robust clusters, and statistical goodness of model fit to reveal significant genes that reflect subtle spatial niches in complex tissues. -

    -

    -

    -
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    Installation -

    -

    You can install the stCCA Package from GitHub using the devtools as follows:

    -
    -# install.packages("devtools")
    -devtools::install_github("fanzhanglab/stCCA")
    -

    (OR)

    -
    -remotes::install_github("fanzhanglab/stCCA")
    -


    -
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    Dependencies / Other required packages -

    -
      -
    • R (>= 4.2)
    • -
    • MERINGUE (>= 1.0)
    • -
    • loe (>= 1.1)
    • -
    • Matrix (>= 1.5.4)
    • -
    • ggplot2 (>= 3.4.2)
    • -
    • ggpubr (>= 0.6.0)
    • -
    • Seurat (>= 4.3.0)
    • -
    • future.apply (>= 1.10.0)
    • -
    • RANN (>= 2.6.1)
    • -
    • sctransform
    • -
    • tibble
    • -
    -


    -
    -
    -
    -

    Tutorials -

    -

    Step-by-step notebook of applying stCCA on identifying spatially informed low-dimensional embeddings and spatially aware clusters on the 10X Visium Human Brain Data (DLPFC):

    - -


    -
    -

    Below are several major steps of running stCCA: -

    -
    -# Create a new stCCA object for the loaded spatial transcriptomic data
    -stCCA = stCCA_Obj(count = dlpfc$count_exp,
    -                  spatial = dlpfc$spatial)
    -

    … (skip several preprocessing steps) …

    -
    -# permute optimal penalty parameters
    -stCCA <- parallel_cca_permute(x = stCCA$exp_adj_matrix, z = stCCA$adj_matrix, obj = stCCA, nperms=50, niter=3)
    -
    -# Perform sparse CCA based on the optimal penalty parameters
    -stCCA <- cca_main(x = stCCA$exp_adj_matrix, z = stCCA$adj_matrix, obj = stCCA, K=20, penaltyx=stCCA$bestpenaltyx, penaltyz=stCCA$bestpenaltyz, v=stcc$v.init)
    -
    -gradient_plot <- spatial_gradient(stCCA)
    -gradient_plot[1:5]
    -

    -


    -
    -cluster_plot <- plot_cluster(coordis = spatial, label = cluster$res_0.30, colors = colors, t="Cell clusters based on stCCA")
    -cluster_plot
    -

    -


    -
    -# Save the main results into the stCCA object
    -saveRDS(stCCA, file="stCCA_10x_human_dlpfc_no.rds")
    -


    -
    -
    -

    Benchmarcking stCCA with other algorithms: -

    -

    -


    -
    -
    -
    -

    Citations -

    -

    Guo, N., Vargas, J., Fritz, D., Krishna, R., Zhang, F. Uncover spatially informed shared variations underlying single-cell spatial transcriptomics with stCCA, bioRxiv, 2023

    -


    -
    -
    -

    Help, Suggestion and Contribution -

    -

    Using github issues section, if you have any question, comments, suggestions, or to report coding related issues of stCCA is highly encouranged than sending emails.

    -
      -
    • Please check the GitHub issues for similar issues that has been reported and resolved. This helps the team to focus on adding new features and working on cool projects instead of resolving the same issues!
    • -
    • -Examples are required when filing a GitHub issue. In certain cases, please share your stCCA object and related codes to understand the issues.
    • -
    -


    -
    -
    -

    Contact -

    -

    Please contact fanzhanglab@gmail.com for further questions or protential collaborative opportunities!

    -
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    -
    - - -
    - - -
    - -
    -

    -

    Site built with pkgdown 2.0.7.

    -
    - -
    -
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- border-top: 1px solid #e5e5e5; - color: #666; - display: flex; - flex-shrink: 0; -} -footer p { - margin-bottom: 0; -} -footer div { - flex: 1; -} -footer .pkgdown { - text-align: right; -} -footer p { - margin-bottom: 0; -} - -img.icon { - float: right; -} - -/* Ensure in-page images don't run outside their container */ -.contents img { - max-width: 100%; - height: auto; -} - -/* Fix bug in bootstrap (only seen in firefox) */ -summary { - display: list-item; -} - -/* Typographic tweaking ---------------------------------*/ - -.contents .page-header { - margin-top: calc(-60px + 1em); -} - -dd { - margin-left: 3em; -} - -/* Section anchors ---------------------------------*/ - -a.anchor { - display: none; - margin-left: 5px; - width: 20px; - height: 20px; - - background-image: url(./link.svg); - background-repeat: no-repeat; - background-size: 20px 20px; - background-position: center center; -} - -h1:hover .anchor, -h2:hover .anchor, -h3:hover .anchor, -h4:hover .anchor, -h5:hover .anchor, -h6:hover .anchor { - display: inline-block; -} - -/* Fixes for fixed navbar --------------------------*/ - -.contents h1, .contents h2, .contents h3, .contents h4 { - padding-top: 60px; - margin-top: -40px; -} - -/* Navbar submenu --------------------------*/ - -.dropdown-submenu { - position: relative; -} - -.dropdown-submenu>.dropdown-menu { - top: 0; - left: 100%; - margin-top: -6px; - margin-left: -1px; - border-radius: 0 6px 6px 6px; -} - -.dropdown-submenu:hover>.dropdown-menu { - display: block; -} - -.dropdown-submenu>a:after { - display: block; - content: " "; - float: right; - width: 0; - height: 0; - border-color: transparent; - border-style: solid; - border-width: 5px 0 5px 5px; - border-left-color: #cccccc; - margin-top: 5px; - margin-right: -10px; -} - -.dropdown-submenu:hover>a:after { - border-left-color: #ffffff; -} - -.dropdown-submenu.pull-left { - float: none; -} - -.dropdown-submenu.pull-left>.dropdown-menu { - left: -100%; - margin-left: 10px; - border-radius: 6px 0 6px 6px; -} - -/* Sidebar --------------------------*/ - -#pkgdown-sidebar { - margin-top: 30px; - position: -webkit-sticky; - position: sticky; - top: 70px; -} - -#pkgdown-sidebar h2 { - font-size: 1.5em; - margin-top: 1em; -} - -#pkgdown-sidebar h2:first-child { - margin-top: 0; -} - -#pkgdown-sidebar .list-unstyled li { - margin-bottom: 0.5em; -} - -/* bootstrap-toc tweaks ------------------------------------------------------*/ - -/* All levels of nav */ - -nav[data-toggle='toc'] .nav > li > a { - padding: 4px 20px 4px 6px; - font-size: 1.5rem; - font-weight: 400; - color: inherit; -} - -nav[data-toggle='toc'] .nav > li > a:hover, -nav[data-toggle='toc'] .nav > li > a:focus { - padding-left: 5px; - color: inherit; - border-left: 1px solid #878787; -} - -nav[data-toggle='toc'] .nav > .active > a, -nav[data-toggle='toc'] .nav > .active:hover > a, -nav[data-toggle='toc'] .nav > .active:focus > a { - padding-left: 5px; - font-size: 1.5rem; - font-weight: 400; - color: inherit; - border-left: 2px solid #878787; -} - -/* Nav: second level (shown on .active) */ - -nav[data-toggle='toc'] .nav .nav { - display: none; /* Hide by default, but at >768px, show it */ - padding-bottom: 10px; -} - -nav[data-toggle='toc'] .nav .nav > li > a { - padding-left: 16px; - font-size: 1.35rem; -} - -nav[data-toggle='toc'] .nav .nav > li > a:hover, -nav[data-toggle='toc'] .nav .nav > li > a:focus { - padding-left: 15px; -} - -nav[data-toggle='toc'] .nav .nav > .active > a, -nav[data-toggle='toc'] .nav .nav > .active:hover > a, -nav[data-toggle='toc'] .nav .nav > .active:focus > a { - padding-left: 15px; - font-weight: 500; - font-size: 1.35rem; -} - -/* orcid ------------------------------------------------------------------- */ - -.orcid { - font-size: 16px; - color: #A6CE39; - /* margins are required by official ORCID trademark and display guidelines */ - margin-left:4px; - margin-right:4px; - vertical-align: middle; -} - -/* Reference index & topics ----------------------------------------------- */ - -.ref-index th {font-weight: normal;} - -.ref-index td {vertical-align: top; min-width: 100px} -.ref-index .icon {width: 40px;} -.ref-index .alias {width: 40%;} -.ref-index-icons .alias {width: calc(40% - 40px);} -.ref-index .title {width: 60%;} - -.ref-arguments th {text-align: right; padding-right: 10px;} -.ref-arguments th, .ref-arguments td {vertical-align: top; min-width: 100px} -.ref-arguments .name {width: 20%;} -.ref-arguments .desc {width: 80%;} - -/* Nice scrolling for wide elements --------------------------------------- */ - -table { - display: block; - overflow: auto; -} - -/* Syntax highlighting ---------------------------------------------------- */ - -pre, code, pre code { - background-color: #f8f8f8; - color: #333; -} -pre, pre code { - white-space: pre-wrap; - word-break: break-all; - overflow-wrap: break-word; -} - -pre { - border: 1px solid #eee; -} - -pre .img, pre .r-plt { - margin: 5px 0; -} - -pre .img img, pre .r-plt img { - background-color: #fff; -} - -code a, pre a { - color: #375f84; -} - -a.sourceLine:hover { - text-decoration: none; -} - -.fl {color: #1514b5;} -.fu {color: #000000;} /* function */ -.ch,.st {color: #036a07;} /* string */ -.kw {color: #264D66;} /* keyword */ -.co {color: #888888;} /* comment */ - -.error {font-weight: bolder;} -.warning {font-weight: bolder;} - -/* Clipboard --------------------------*/ - -.hasCopyButton { - position: relative; -} - -.btn-copy-ex { - position: absolute; - right: 0; - top: 0; - visibility: hidden; -} - -.hasCopyButton:hover button.btn-copy-ex { - visibility: visible; -} - -/* headroom.js ------------------------ */ - -.headroom { - will-change: transform; - transition: transform 200ms linear; -} -.headroom--pinned { - transform: translateY(0%); -} -.headroom--unpinned { - transform: translateY(-100%); -} - -/* mark.js ----------------------------*/ - -mark { - background-color: rgba(255, 255, 51, 0.5); - border-bottom: 2px solid rgba(255, 153, 51, 0.3); - padding: 1px; -} - -/* vertical spacing after htmlwidgets */ -.html-widget { - margin-bottom: 10px; -} - -/* fontawesome ------------------------ */ - -.fab { - font-family: "Font Awesome 5 Brands" !important; -} - -/* don't display links in code chunks when printing */ -/* source: https://stackoverflow.com/a/10781533 */ -@media print { - code a:link:after, code a:visited:after { - content: ""; - } -} - -/* Section anchors --------------------------------- - Added in pandoc 2.11: https://github.com/jgm/pandoc-templates/commit/9904bf71 -*/ - -div.csl-bib-body { } -div.csl-entry { - clear: both; -} -.hanging-indent div.csl-entry { - margin-left:2em; - text-indent:-2em; -} -div.csl-left-margin { - min-width:2em; - float:left; -} -div.csl-right-inline { - margin-left:2em; - padding-left:1em; -} -div.csl-indent { - margin-left: 2em; -} diff --git a/docs/pkgdown.js b/docs/pkgdown.js deleted file mode 100644 index 6f0eee4..0000000 --- a/docs/pkgdown.js +++ /dev/null @@ -1,108 +0,0 @@ -/* http://gregfranko.com/blog/jquery-best-practices/ */ -(function($) { - $(function() { - - $('.navbar-fixed-top').headroom(); - - $('body').css('padding-top', $('.navbar').height() + 10); - $(window).resize(function(){ - $('body').css('padding-top', $('.navbar').height() + 10); - }); - - $('[data-toggle="tooltip"]').tooltip(); - - var cur_path = paths(location.pathname); - var links = $("#navbar ul li a"); - var max_length = -1; - var pos = -1; - for (var i = 0; i < links.length; i++) { - if (links[i].getAttribute("href") === "#") - continue; - // Ignore external links - if (links[i].host !== location.host) - continue; - - var nav_path = paths(links[i].pathname); - - var length = prefix_length(nav_path, cur_path); - if (length > max_length) { - max_length = length; - pos = i; - } - } - - // Add class to parent
  • , and enclosing
  • if in dropdown - if (pos >= 0) { - var menu_anchor = $(links[pos]); - menu_anchor.parent().addClass("active"); - menu_anchor.closest("li.dropdown").addClass("active"); - } - }); - - function paths(pathname) { - var pieces = pathname.split("/"); - pieces.shift(); // always starts with / - - var end = pieces[pieces.length - 1]; - if (end === "index.html" || end === "") - pieces.pop(); - return(pieces); - } - - // Returns -1 if not found - function prefix_length(needle, haystack) { - if (needle.length > haystack.length) - return(-1); - - // Special case for length-0 haystack, since for loop won't run - if (haystack.length === 0) { - return(needle.length === 0 ? 0 : -1); - } - - for (var i = 0; i < haystack.length; i++) { - if (needle[i] != haystack[i]) - return(i); - } - - return(haystack.length); - } - - /* Clipboard --------------------------*/ - - function changeTooltipMessage(element, msg) { - var tooltipOriginalTitle=element.getAttribute('data-original-title'); - element.setAttribute('data-original-title', msg); - $(element).tooltip('show'); - element.setAttribute('data-original-title', tooltipOriginalTitle); - } - - if(ClipboardJS.isSupported()) { - $(document).ready(function() { - var copyButton = ""; - - $("div.sourceCode").addClass("hasCopyButton"); - - // Insert copy buttons: - $(copyButton).prependTo(".hasCopyButton"); - - // Initialize tooltips: - $('.btn-copy-ex').tooltip({container: 'body'}); - - // Initialize clipboard: - var clipboardBtnCopies = new ClipboardJS('[data-clipboard-copy]', { - text: function(trigger) { - return trigger.parentNode.textContent.replace(/\n#>[^\n]*/g, ""); - } - }); - - clipboardBtnCopies.on('success', function(e) { - changeTooltipMessage(e.trigger, 'Copied!'); - e.clearSelection(); - }); - - clipboardBtnCopies.on('error', function() { - changeTooltipMessage(e.trigger,'Press Ctrl+C or Command+C to copy'); - }); - }); - } -})(window.jQuery || window.$) diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml deleted file mode 100644 index 3480256..0000000 --- a/docs/pkgdown.yml +++ /dev/null @@ -1,7 +0,0 @@ -pandoc: 3.1.1 -pkgdown: 2.0.7 -pkgdown_sha: ~ -articles: - Intro_to_STew: Intro_to_STew.html -last_built: 2023-09-08T20:20Z - diff --git a/docs/reference/BuildSNNSeurat.html b/docs/reference/BuildSNNSeurat.html deleted file mode 100644 index 6441530..0000000 --- a/docs/reference/BuildSNNSeurat.html +++ /dev/null @@ -1,112 +0,0 @@ - -BuildSNNSeurat() — BuildSNNSeurat • STew - - -
    -
    - - - -
    -
    - - -
    -

    Build SNN matrix by inputting canonical variates of spatial-driven canonical loading

    -
    - -
    -
    BuildSNNSeurat(obj, k.param = 30, prune.SNN = 1/15, nn.eps = 0, cols = NULL)
    -
    - -
    -

    Arguments

    -
    obj
    -

    stCCA S3 Object

    - - -
    k.param
    -

    Maximum number of nearest neighbors to compute. Default - 30.

    - - -
    prune.SNN
    -

    Sets the cutoff for acceptable Jaccard index when computing the neighborhood overlap for the SNN construction. Any edges with values less than or equal to this will be set to 0 and removed from the SNN graph. Essentially sets the stringency of pruning (0 --- no pruning, 1 --- prune everything). Default - 1/15.

    - - -
    nn.eps
    -

    Error bound: default of 0.0 implies exact nearest neighbor search

    - - -
    cols
    -

    columns selected

    - -
    - -
    - -
    - - -
    - -
    -

    Site built with pkgdown 2.0.7.

    -
    - -
    - - - - - - - - diff --git a/docs/reference/FindClusters.html b/docs/reference/FindClusters.html deleted file mode 100644 index 5c61580..0000000 --- a/docs/reference/FindClusters.html +++ /dev/null @@ -1,100 +0,0 @@ - -FindClusters() — FindClusters • STew - - -
    -
    - - - -
    -
    - - -
    -

    Perform cell clustering using different resolutions

    -
    - -
    -
    FindClusters(resolution_list, obj)
    -
    - -
    -

    Arguments

    -
    resolution_list
    -

    List of resolutions to use for clustering

    - - -
    obj
    -

    stCCA S3 Object

    - -
    - -
    - -
    - - -
    - -
    -

    Site built with pkgdown 2.0.7.

    -
    - -
    - - - - - - - - diff --git a/docs/reference/autocorrelated_genes.html b/docs/reference/autocorrelated_genes.html deleted file mode 100644 index 1238ca0..0000000 --- a/docs/reference/autocorrelated_genes.html +++ /dev/null @@ -1,129 +0,0 @@ - -autocorrelated_genes() — autocorrelated_genes • STew - - -
    -
    - - - -
    -
    - - -
    -

    Filtering gene expression matrix by spatially auto-correlated genes and correction for multiple-testing

    -
    - -
    -
    autocorrelated_genes(
    -  obj,
    -  alternative = "greater",
    -  adjustPv = TRUE,
    -  alpha = 0.05,
    -  minPercentCells = 0.05,
    -  verbose = TRUE
    -)
    -
    - -
    -

    Arguments

    -
    obj
    -

    stCCA S3 Object

    - - -
    alternative
    -

    a character string specifying the alternative hypothesis, chosen from one of "greater", "two-sided" or "less". Default is "greater"

    - - -
    adjustPv
    -

    Adjusted P value

    - - -
    alpha
    -

    P-value threshold for LISA score to be considered significant

    - - -
    minPercentCells
    -

    Minimum percent of cells that must be driving spatial pattern

    - - -
    verbose
    -

    A logical parameter that controls the amount of output or information displayed during the execution of the function. When verbose = TRUE, the function will provide more detailed and informative output during its execution and when verbose = FALSE (default setting), the function will produce minimal output.

    - -
    -
    -

    Value

    - - -

    Subset regularized gene expression matrix by filtered genes

    -
    - -
    - -
    - - -
    - -
    -

    Site built with pkgdown 2.0.7.

    -
    - -
    - - - - - - - - diff --git a/docs/reference/cca_main.html b/docs/reference/cca_main.html deleted file mode 100644 index e5f02c9..0000000 --- a/docs/reference/cca_main.html +++ /dev/null @@ -1,188 +0,0 @@ - -cca_main() — cca_main • STew - - -
    -
    - - - -
    -
    - - -
    -

    Perform Sparse CCA using the optimal tuning parameters

    -
    - -
    -
    cca_main(
    -  x,
    -  z,
    -  obj = NULL,
    -  penaltyx = NULL,
    -  penaltyz = NULL,
    -  K = 1,
    -  niter = 15,
    -  v = NULL,
    -  trace = TRUE,
    -  standardize = TRUE,
    -  xnames = colnames(x),
    -  znames = colnames(z),
    -  upos = FALSE,
    -  uneg = FALSE,
    -  vpos = FALSE,
    -  vneg = FALSE,
    -  outcome = NULL,
    -  y = NULL,
    -  cens = NULL
    -)
    -
    - -
    -

    Arguments

    -
    x
    -

    Expression-driven adjacency matrix

    - - -
    z
    -

    Spatial-driven adjacency matrix

    - - -
    obj
    -

    stCCA S3 Object

    - - -
    penaltyx
    -

    The penalty to be applied to the matrix x, i.e. the penalty that results in the canonical vector u. If typex is "standard" then the L1 bound on u is penaltyx*sqrt(ncol(x)). In this case penaltyx must be between 0 and 1 (larger L1 bound corresponds to less penalization).

    - - -
    penaltyz
    -

    The penalty to be applied to the matrix z, i.e. the penalty that results in the canonical vector v. If typez is "standard" then the L1 bound on v is penaltyz*sqrt(ncol(z)). In this case penaltyz must be between 0 and 1 (larger L1 bound corresponds to less penalization).

    - - -
    K
    -

    The number of u's and v's desired; that is, the number of canonical vectors to be obtained.

    - - -
    niter
    -

    The number of iterations to be performed. Default - 20.

    - - -
    v
    -

    -desc

    - - -
    trace
    -

    -desc

    - - -
    standardize
    -

    -desc

    - - -
    xnames
    -

    -desc

    - - -
    znames
    -

    -desc

    - - -
    upos
    -

    -desc

    - - -
    uneg
    -

    -desc

    - - -
    vpos
    -

    -desc

    - - -
    vneg
    -

    -desc

    - - -
    outcome
    -

    -desc

    - - -
    y
    -

    -desc

    - - -
    cens
    -

    -desc

    - -
    - -
    - -
    - - -
    - -
    -

    Site built with pkgdown 2.0.7.

    -
    - -
    - - - - - - - - diff --git a/docs/reference/create_avg_plots.html b/docs/reference/create_avg_plots.html deleted file mode 100644 index 4043fb6..0000000 --- a/docs/reference/create_avg_plots.html +++ /dev/null @@ -1,118 +0,0 @@ - -create_avg_plots() — create_avg_plots • STew - - -
    -
    - - - -
    -
    - - -
    -

    Create Average Plots

    -
    - -
    -
    create_avg_plots(mean_var, obs_mean_var, models, log_transform = FALSE)
    -
    - -
    -

    Arguments

    -
    mean_var
    -

    A data frame containing the mean and variance data.

    - - -
    obs_mean_var
    -

    A data frame containing the observed mean and variance data.

    - - -
    models
    -

    A character vector specifying the models.

    - - -
    log_transform
    -

    A logical value indicating whether to perform log transformation on the data.

    - -
    -
    -

    Value

    - - -

    A list of average plots.

    -
    -
    -

    Details

    -

    This function creates average plots based on the mean and variance data.

    -
    - -
    - -
    - - -
    - -
    -

    Site built with pkgdown 2.0.7.

    -
    - -
    - - - - - - - - diff --git a/docs/reference/dlpfc.html b/docs/reference/dlpfc.html deleted file mode 100644 index 5a71d4d..0000000 --- a/docs/reference/dlpfc.html +++ /dev/null @@ -1,110 +0,0 @@ - -dlpfc — dlpfc • STew - - -
    -
    - - - -
    -
    - - -
    -

    The data provided is a list of Data set containing LIBD human dorsolateral pre-frontal cortex (DLPFC) -and spatial transcriptomics data (sample id 151673) generated with the 10x Genomics Visium platform.

    -
    - -
    -
    dlpfc
    -
    - -
    -

    Format

    -

    A dataframe with cell spatail coordinates and expression of highly variable genes

    Spatial
    -

    Spatial coordinates of cells

    - -
    count_exp
    -

    Gene expression of highly variable genes - a count matrix in which genes as rows with rownames and cells as columns with colnames

    - - -
    - -
    -

    Details

    -

    This data is provided as a sample data to understand the package and not a mandatory requirement to run the functions.

    -
    - -
    - -
    - - -
    - -
    -

    Site built with pkgdown 2.0.7.

    -
    - -
    - - - - - - - - diff --git a/docs/reference/expression_adjacency_matrix.html b/docs/reference/expression_adjacency_matrix.html deleted file mode 100644 index eb6dd87..0000000 --- a/docs/reference/expression_adjacency_matrix.html +++ /dev/null @@ -1,123 +0,0 @@ - -To calculate cell-to-cell similarity matrix on gene (spatially auto-coorelated genes) expression based on Euclidean distance; to build up a K-nearest neighbors (KNN) graph based on the cell-to-cell similarity matrix; then to convert the KNN neighbor relation matrix into an adjacency matrix — expression_adjacency_matrix • STew - - -
    -
    - - - -
    -
    - - -
    -

    To calculate cell-to-cell similarity matrix on gene (spatially auto-coorelated genes) expression based on Euclidean distance; to build up a K-nearest neighbors (KNN) graph based on the cell-to-cell similarity matrix; then to convert the KNN neighbor relation matrix into an adjacency matrix

    -
    - -
    -
    expression_adjacency_matrix(
    -  obj,
    -  nk = 200,
    -  symm = FALSE,
    -  weight = TRUE,
    -  mode = "undirected",
    -  weighted = TRUE
    -)
    -
    - -
    -

    Arguments

    -
    obj
    -

    stCCA S3 Object

    - - -
    nk
    -

    Number of neighbors

    - - -
    symm
    -

    If TRUE, the connectivity matrix is made symmetrical. Default - FALSE

    - - -
    weight
    -

    If TRUE, the weighted KNN graph is created. Default - FALSE.

    - - -
    mode
    -

    A character string chosen from c(""directed", "undirected", "max", "min", "upper", "lower", "plus")

    - - -
    weighted
    -

    To specify whether to create a weighted graph from an adjacency matrix. If NULL, an unweighted graph is created. If TRUE, a weighted graph is created.

    - -
    - -
    - -
    - - -
    - -
    -

    Site built with pkgdown 2.0.7.

    -
    - -
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