From bfb1655e4c61af8d708cb39bfb411ffb329feb20 Mon Sep 17 00:00:00 2001 From: fame2827 Date: Mon, 13 Nov 2023 15:58:11 +1100 Subject: [PATCH] updated readme --- DESCRIPTION | 24 +- README.html | 491 ++++++++++++++++++++++++++++++++++++ README.md | 66 ++--- _pkgdown.yml | 6 +- vignettes/images/banner.png | Bin 0 -> 2484767 bytes 5 files changed, 530 insertions(+), 57 deletions(-) create mode 100644 README.html create mode 100644 vignettes/images/banner.png diff --git a/DESCRIPTION b/DESCRIPTION index 94de142..b292601 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,16 +1,12 @@ -Package: BuildABiocWorkshop -Title: Bioconductor or R Workshop Template and Github Actions +Package: ScdneySpatial +Title: Unlocking single cell spatial omics analyses with scdney Version: 2.0.0 Authors@R: - person(given = "Sean", - family = "Davis", - role = c("aut", "cre"), - email = "seandavi@gmail.com", - comment = c(ORCID = "0000-0002-8991-6458")) -Description: This package serves as a template for using github actions to - prepare a Bioconductor Workshop package for use. In particular, - a pkgdown website and docker image are generated based on workflow - settings. + c(person(given = "Farhan", family = "Ameen", role = c("aut", "cre"), email = "farhan.ameen@sydney.edu.au"), + person(given = "Ellis", family = "Patrick", role = c("aut"), email = "ellis.patrick@sydney.edu.au"), + person(given = "Shila", family = "Ghazanfar", role = c("aut"), email = "shila.ghazanfar@sydney.edu.au")) +Description: This workshop introduces an analysitical framwork for analysisng data from high dimensional spatial omics technologies, + using functionality from packages developed by the Sydney Precision Data Science Center. License: MIT + file LICENSE Encoding: UTF-8 LazyData: true @@ -22,7 +18,7 @@ Suggests: knitr, rmarkdown, pkgdown -URL: https://seandavi.github.io/BuildABiocWorkshop/ -BugReports: https://github.com/seandavi/BuildABiocWorkshop/issues/new/choose +URL: https://sydneybiox.github.io/ScdneySpatial/ +BugReports: https://github.com/SydneyBioX/ScdneySpatial/issues/new/choose VignetteBuilder: knitr -DockerImage: ghcr.io/seandavi/buildabiocworkshop:latest +DockerImage: ghcr.io/sydneybiox/scdneyspatial:latest \ No newline at end of file diff --git a/README.html b/README.html new file mode 100644 index 0000000..2c56d14 --- /dev/null +++ b/README.html @@ -0,0 +1,491 @@ + + + + + + + + + + + + + +README + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+

+
+

Overview

+

Understanding the interplay between different types of cells and +their immediate environment is critical for understanding the mechanisms +of cells themselves and their function in the context of human diseases. +Recent advances in high dimensional in situ cytometry technologies have +fundamentally revolutionized our ability to observe these complex +cellular relationships providing an unprecedented characterisation of +cellular heterogeneity in a tissue environment.

+
+
+

Description

+

In this tutorial we will introduce an analytical framework for +analysing data from high dimensional spatial omics technologies such as, +CODEX, CycIF, IMC and High Definition Spatial Transcriptomics. This +framework makes use of functionality from our Bioconductor packages +simpleSeg, scHot, FuseSOM, spicyR, listClust, statial, scFeatures and +ClassifyR. By the end of this tutorial attendees will be able to +implement and assess some of the key steps of a spatial analysis +pipeline including cell segmentation, feature normalisation, cell type +identification, microenvironment and cell-state characterisation, +spatial hypothesis testing and patient classification. Understanding +these key steps will provide attendees with the core skills needed to +interrogate the comprehensive spatial information generated by these +exciting new technologies.

+
+

Pre-requisites

+

It is expected that students will have:

+
    +
  • basic knowledge of R syntax,
  • +
  • familiarity with SingleCellExperiment and/or SpatialExperiment +objects, and
  • +
  • this workshop will not provide an in-depth description of +cell-resolution spatial omics technologies.
  • +
+
+
+

Time outline

+

The expected timing of the workshop:

+ + + + + + + + + + + + + + + + + + + + + + + + + +
ActivityTime
Data visualisation & cell segmentation1h 30m
Cell type clustering & classification1h 30m
Spatial analysis & feature generation2h
Patient Classification1h 30m
+
+
+

Learning objectives

+
    +
  • Understand and visualise spatial omics datasets.
  • +
  • Identify key biological questions that can be addressed with these +technologies and spatial analysis.
  • +
  • Understand the key analytical steps involved in spatial omics +analysis, and perform these steps using R.
  • +
  • Evaluate the performance of data normalisation and cell +segmentation.
    +
  • +
  • Understand and generate individual feature representations from +spatial omics data.
  • +
  • Develop appreciation on how to assess performance of classification +models.
  • +
  • Perform disease outcome prediction using the feature representation +and robust classification framework.
  • +
+
+
+
+ + + + +
+ + + + + + + + + + + + + + + diff --git a/README.md b/README.md index afb8cc4..f1a4080 100644 --- a/README.md +++ b/README.md @@ -1,52 +1,38 @@ -# BuildABiocWorkshop +# ![](vignettes/images/banner.png) -This package is a template for building a Bioconductor workshop. The package -includes Github actions to: +## Overview -1. Set up bioconductor/bioconductor_docker:devel on Github resources -2. Install package dependencies for your package (based on the `DESCRIPTION` file) -3. Run `rcmdcheck::rcmdcheck` -4. Build a pkgdown website and push it to github pages -5. Build a docker image with the installed package and dependencies and deploy to [the Github Container Repository](https://docs.github.com/en/packages/working-with-a-github-packages-registry/working-with-the-container-registry#pulling-container-images) at the name `ghcr.io/gihub_user/repo_name`, all lowercase. +Understanding the interplay between different types of cells and their immediate environment is critical for understanding the mechanisms of cells themselves and their function in the context of human diseases. Recent advances in high dimensional in situ cytometry technologies have fundamentally revolutionized our ability to observe these complex cellular relationships providing an unprecedented characterisation of cellular heterogeneity in a tissue environment. -## Responsibilities +## Description -Package authors are primarily responsible for: +In this tutorial we will introduce an analytical framework for analysing data from high dimensional spatial omics technologies such as, CODEX, CycIF, IMC and High Definition Spatial Transcriptomics. This framework makes use of functionality from our Bioconductor packages simpleSeg, scHot, FuseSOM, spicyR, listClust, statial, scFeatures and ClassifyR. By the end of this tutorial attendees will be able to implement and assess some of the key steps of a spatial analysis pipeline including cell segmentation, feature normalisation, cell type identification, microenvironment and cell-state characterisation, spatial hypothesis testing and patient classification. Understanding these key steps will provide attendees with the core skills needed to interrogate the comprehensive spatial information generated by these exciting new technologies. -1. Creating a landing site of their choosing for their workshops (a website). This website should be listed in the `DESCRIPTION` file as the `URL`. -2. Creating a docker image that will contain workshop materials and the installed packages necessary to run those materials. The name of the resulting docker image, including "tag" if desired, should be listed in a non-standard tag, `DockerImage:` in the `DESCRIPTION` file. +### Pre-requisites -Both of those tasks can be accomplished using the Github actions included in this template package. The vignette accompanying this package describes how to accomplish both of these tasks. +It is expected that students will have: -## Details +- basic knowledge of R syntax, +- familiarity with SingleCellExperiment and/or SpatialExperiment objects, and +- this workshop will not provide an in-depth description of cell-resolution spatial omics technologies. -For detailed instructions, see the `How to build a workshop` article/vignette. +### Time outline -## Results of successful deployment +The expected timing of the workshop: -- A working docker image that contains the installed package and dependencies. -- An up-to-date `pkgdown` website at https://YOURUSERNAME.github.io/YOURREPOSITORYNAME/ -- Docker image will be tagged with `latest`, `sha-XXXXXX` where `XXXXXX` is the hash of the current `master` commit, and `master`. +| Activity | Time | +|----------------------------------------|--------| +| Data visualisation & cell segmentation | 1h 30m | +| Cell type clustering & classification | 1h 30m | +| Spatial analysis & feature generation | 2h | +| Patient Classification | 1h 30m | -## To use the resulting image: +### Learning objectives -```sh -docker run -e PASSWORD= -p 8787:8787 YOURDOCKERIMAGENAME -``` -Once running, navigate to http://localhost:8787/ and then login with `rstudio`:`yourchosenpassword`. - -To try with **this** repository docker image: - -```sh -docker run -e PASSWORD=abc -p 8787:8787 ghcr.io/bioconductor/buildabiocworkshop -``` - -*NOTE*: Running docker that uses the password in plain text like above exposes the password to others -in a multi-user system (like a shared workstation or compute node). In practice, consider using an environment -variable instead of plain text to pass along passwords and other secrets in docker command lines. - - -## Whatcha get - -- https://bioconductor.github.io/BuildABiocWorkshop -- A Docker image that you can run locally, in the cloud, or (usually) even as a singularity container on HPC systems. +- Understand and visualise spatial omics datasets. +- Identify key biological questions that can be addressed with these technologies and spatial analysis. +- Understand the key analytical steps involved in spatial omics analysis, and perform these steps using R. +- Evaluate the performance of data normalisation and cell segmentation.\ +- Understand and generate individual feature representations from spatial omics data. +- Develop appreciation on how to assess performance of classification models. +- Perform disease outcome prediction using the feature representation and robust classification framework. diff --git a/_pkgdown.yml b/_pkgdown.yml index 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