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Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use <code>wflow_publish</code> or <code>wflow_git_commit</code>). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:
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@@ -816,7 +855,31 @@ Updating materials for workshop in July 2021
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<divid="introduction"class="section level2">
<h2>Introduction</h2>
<p>In this workshop, we will introduce the data infrastructure for scRNA-seq analysis in R and practice a workflow of scRNAseq analysis; from pre-processing, quality control to dimensionality reduction and clustering. We will then demonstrate the usage of marker genes for cell type annotation and an automatic approach for matching query cells to a reference atlas with labels.</p>
<p>In this workshop, we will introduce the data infrastructure for scRNA-seq analysis in R and practice a workflow of scRNAseq analysis; from pre-processing, quality control to dimensionality reduction and clustering, marker gene detection and cell type annotation - with plenty more along the way!</p>
<p>We use the Bioconductor single-cell ecosystem for this workshop. Thus, participants will need a recent version of R (version 4.0+) and a set of specific packages that we use.</p>
<p>The code snippet below will install the necessary packages for you in R (i.e. run the following code at the R prompt in an R or RStudio session). The first line installs the <code>BiocManager</code> package, which is the preferred package for then installing Bioconductor packages. The next (long) line then installs the necessary Bioconductor packages (and any dependencies).</p>
