From 74821d2d52b96287b1f0358fd184ee1fe3466116 Mon Sep 17 00:00:00 2001 From: fuerzhou Date: Tue, 21 Nov 2023 15:11:54 +1100 Subject: [PATCH] Built site for gh-pages --- .nojekyll | 2 +- index.html | 18 +++++++----------- search.json | 11 ++--------- sitemap.xml | 6 +++--- 4 files changed, 13 insertions(+), 24 deletions(-) diff --git a/.nojekyll b/.nojekyll index d72f830..f3e30cb 100644 --- a/.nojekyll +++ b/.nojekyll @@ -1 +1 @@ -9966aebb \ No newline at end of file +66fa5cf9 \ No newline at end of file diff --git a/index.html b/index.html index 202304c..74a978c 100644 --- a/index.html +++ b/index.html @@ -117,7 +117,6 @@

On this page

  • Learning objectives
  • Workshop organisers
  • Schedule
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  • Additional materials
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    Welcome

    In this workshop we will introduce two R packages we developed for the analysis and simulation of spatial proteomics data (multiplex immunohistochemistry, CODEX, MIBI, IMC, etc) published this year in Nature Communications (Feng et al. 2023). SPIAT (SPatial Image Analysis of Tissues) is a spatial-platform agnostic toolkit with a suite of spatial analysis algorithms, and spaSim (spatial Simulator) is a novel simulator of tissue spatial patterns.

    The workshop is designed for those who would like to get started with or improve their capacity to perform spatial data analyses. We will cover how to handle spatial proteomics data, from reading, formatting, and extensive spatial analysis through SPIAT’s six analysis modules. These range from quality control and visualisation to more advanced topics such as cell colocalisation, spatial heterogeneity, tumour structure characterisation, and neighbourhood identification.

    During the workshop, you will get an overview of the analysis workflow and understand the rationale behind specific functions. You will get your hands dirty by designing an analysis workflow using a real example dataset, so you’ll learn by doing.

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    Please have a read of our recent paper (Nature Communications) if you are interested! https://www.nature.com/articles/s41467-023-37822-0

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    Links to the two packages:

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    These two R packages were developed in the Anna Trigos Lab.

    Time and Location

    @@ -255,16 +261,6 @@

    Schedule

    -
    -
    -

    Additional materials

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    Please have a read of our recent paper if you are interested!

    -

    Links to the two packages:

    - -

    These two R packages were developed in the Anna Trigos Lab.

    diff --git a/search.json b/search.json index 802e789..8337fb7 100644 --- a/search.json +++ b/search.json @@ -11,14 +11,14 @@ "href": "index.html", "title": "Overview of the workshop", "section": "", - "text": "In this workshop we will introduce two R packages we developed for the analysis and simulation of spatial proteomics data (multiplex immunohistochemistry, CODEX, MIBI, IMC, etc) published this year in Nature Communications (Feng et al. 2023). SPIAT (SPatial Image Analysis of Tissues) is a spatial-platform agnostic toolkit with a suite of spatial analysis algorithms, and spaSim (spatial Simulator) is a novel simulator of tissue spatial patterns.\nThe workshop is designed for those who would like to get started with or improve their capacity to perform spatial data analyses. We will cover how to handle spatial proteomics data, from reading, formatting, and extensive spatial analysis through SPIAT’s six analysis modules. These range from quality control and visualisation to more advanced topics such as cell colocalisation, spatial heterogeneity, tumour structure characterisation, and neighbourhood identification.\nDuring the workshop, you will get an overview of the analysis workflow and understand the rationale behind specific functions. You will get your hands dirty by designing an analysis workflow using a real example dataset, so you’ll learn by doing." + "text": "In this workshop we will introduce two R packages we developed for the analysis and simulation of spatial proteomics data (multiplex immunohistochemistry, CODEX, MIBI, IMC, etc) published this year in Nature Communications (Feng et al. 2023). SPIAT (SPatial Image Analysis of Tissues) is a spatial-platform agnostic toolkit with a suite of spatial analysis algorithms, and spaSim (spatial Simulator) is a novel simulator of tissue spatial patterns.\nThe workshop is designed for those who would like to get started with or improve their capacity to perform spatial data analyses. We will cover how to handle spatial proteomics data, from reading, formatting, and extensive spatial analysis through SPIAT’s six analysis modules. These range from quality control and visualisation to more advanced topics such as cell colocalisation, spatial heterogeneity, tumour structure characterisation, and neighbourhood identification.\nDuring the workshop, you will get an overview of the analysis workflow and understand the rationale behind specific functions. You will get your hands dirty by designing an analysis workflow using a real example dataset, so you’ll learn by doing.\nPlease have a read of our recent paper (Nature Communications) if you are interested! https://www.nature.com/articles/s41467-023-37822-0\nLinks to the two packages:\n\nSPIAT Github and SPIAT Bioconductor\nspaSim Github and spaSim Bioconductor\n\nThese two R packages were developed in the Anna Trigos Lab." }, { "objectID": "index.html#welcome", "href": "index.html#welcome", "title": "Overview of the workshop", "section": "", - "text": "In this workshop we will introduce two R packages we developed for the analysis and simulation of spatial proteomics data (multiplex immunohistochemistry, CODEX, MIBI, IMC, etc) published this year in Nature Communications (Feng et al. 2023). SPIAT (SPatial Image Analysis of Tissues) is a spatial-platform agnostic toolkit with a suite of spatial analysis algorithms, and spaSim (spatial Simulator) is a novel simulator of tissue spatial patterns.\nThe workshop is designed for those who would like to get started with or improve their capacity to perform spatial data analyses. We will cover how to handle spatial proteomics data, from reading, formatting, and extensive spatial analysis through SPIAT’s six analysis modules. These range from quality control and visualisation to more advanced topics such as cell colocalisation, spatial heterogeneity, tumour structure characterisation, and neighbourhood identification.\nDuring the workshop, you will get an overview of the analysis workflow and understand the rationale behind specific functions. You will get your hands dirty by designing an analysis workflow using a real example dataset, so you’ll learn by doing." + "text": "In this workshop we will introduce two R packages we developed for the analysis and simulation of spatial proteomics data (multiplex immunohistochemistry, CODEX, MIBI, IMC, etc) published this year in Nature Communications (Feng et al. 2023). SPIAT (SPatial Image Analysis of Tissues) is a spatial-platform agnostic toolkit with a suite of spatial analysis algorithms, and spaSim (spatial Simulator) is a novel simulator of tissue spatial patterns.\nThe workshop is designed for those who would like to get started with or improve their capacity to perform spatial data analyses. We will cover how to handle spatial proteomics data, from reading, formatting, and extensive spatial analysis through SPIAT’s six analysis modules. 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