Change supplementary material to Quarto

tutorials/containers/Dockerfile

@@ -13,8 +13,18 @@ WORKDIR /course
 ENV TZ="Europe/Stockholm"
 ENV DEBIAN_FRONTEND=noninteractive
 
-# Install package for setting timezone
-RUN apt-get update && apt-get install -y tzdata && apt-get clean
+# Install packages require for timezone and Quarto installation
+RUN apt-get update \
+    && apt-get install -y tzdata curl \
+    && apt-get clean
+
+# Install Quarto
+ARG QUARTO_VERSION="1.3.450"
+RUN mkdir -p /opt/quarto/${QUARTO_VERSION} \
+    && curl -o quarto.tar.gz -L "https://github.com/quarto-dev/quarto-cli/releases/download/v${QUARTO_VERSION}/quarto-${QUARTO_VERSION}-linux-amd64.tar.gz" \
+    && tar -zxvf quarto.tar.gz -C "/opt/quarto/${QUARTO_VERSION}" --strip-components=1 \
+    && rm quarto.tar.gz
+ENV PATH /opt/quarto/${QUARTO_VERSION}/bin:${PATH}
 
 # Configure Conda/Mamba
 RUN mamba init bash && conda config --set channel_priority strict && \
@@ -40,4 +50,4 @@ COPY code ./code/
 # Open up port 8888
 EXPOSE 8888
 
-CMD snakemake -rp -c 1 --configfile config.yml
+CMD snakemake -rp -c 1 --configfile config.yml

tutorials/containers/Snakefile

@@ -19,16 +19,16 @@ def get_sample_url(wildcards):
 rule get_SRA_by_accession:
     """
     Retrieve a single-read FASTQ file from a remote repository
-    
-    The fastq file is retrieved with wget and piped directly to the 
-    seqtk program which samples a number of reads defined by the 
-    max_reads parameter. The fastq output from seqtk is in turn piped 
+
+    The fastq file is retrieved with wget and piped directly to the
+    seqtk program which samples a number of reads defined by the
+    max_reads parameter. The fastq output from seqtk is in turn piped
     to gzip and stored as a compressed *.fastq.gz file.
 
-    The actual URL for the file is obtained from the config which 
-    requires that each sample_id is defined in the configfile as for 
+    The actual URL for the file is obtained from the config which
+    requires that each sample_id is defined in the configfile as for
     example:
-    
+
     sample_id: "https://url/to/file"
     """
     output:
@@ -206,16 +206,14 @@ rule make_supplementary:
         GSM_IDs = config["sample_ids_geo"]
     shell:
         """
-        echo 'rmarkdown::render("code/supplementary_material.Rmd", \
-                  output_file="supplementary.html", \
-                  output_dir="results/", \
-                  params=list(counts_file="{input.counts}", \
-                              multiqc_file="{input.multiqc_file}", \
-                              summary_file="{input.summary_file}", \
-                              rulegraph_file="{input.rulegraph}", \
-                              SRR_IDs="{params.SRR_IDs}", \
-                              GSM_IDs="{params.GSM_IDs}"))' \
-        | R --vanilla > {log} 2>&1
+        quarto render code/supplementary_material.qmd \
+            -P counts_file:../{input.counts} \
+            -P multiqc_file:../{input.multiqc_file} \
+            -P summary_file:../{input.summary_file} \
+            -P rulegraph_file:../{input.rulegraph} \
+            -P srr_ids:"{params.SRR_IDs}" \
+            -P gsm_ids:"{params.GSM_IDs}"
+        mv code/supplementary_material.html {output}
         """
 
 rule generate_rulegraph:
@@ -227,4 +225,4 @@ rule generate_rulegraph:
     shell:
         """
         snakemake --rulegraph --configfile config.yml | dot -Tpng > {output}
-        """
+        """

tutorials/containers/code/supplementary_material.Rmd → tutorials/containers/code/supplementary_material.qmd

@@ -1,37 +1,39 @@
 ---
 title: "Supplementary Material"
-output: html_document
+format:
+    html:
+        echo: false
+        embed_resources: true
+engine: knitr
 params:
-  counts_file: "results/tables/counts.tsv"
-  multiqc_file: "intermediate/multiqc_general_stats.txt"
-  summary_file: "results/tables/counts.tsv.summary"
-  rulegraph_file: "results/rulegraph.png"
-  SRR_IDs: "SRR935090 SRR935091 SRR935092"
-  GSM_IDs: "GSM1186459 GSM1186460 GSM1186461"
+    counts_file: "results/tables/counts.tsv"
+    multiqc_file: "intermediate/multiqc_general_stats.txt"
+    summary_file: "results/tables/counts.tsv.summary"
+    rulegraph_file: "results/rulegraph.png"
+    srr_ids: "SRR935090 SRR935091 SRR935092"
+    gsm_ids: "GSM1186459 GSM1186460 GSM1186461"
 ---
 
-```{r Setup, include = FALSE}
-knitr::opts_knit$set(root.dir = "../")
-knitr::opts_chunk$set(echo = FALSE)
-```
-
-```{r Dependencies, include = FALSE}
+```{r Dependencies}
+#| include: false
 library("ggplot2")
 library("reshape2")
 library("pheatmap")
 library("GEOquery")
 ```
 
-```{r Read parameters, include = FALSE}
+```{r Read parameters}
+#| include: false
 counts_file <- params$counts_file
 multiqc_file <- params$multiqc_file
 summary_file <- params$summary_file
 rulegraph_file <- params$rulegraph_file
-SRR_IDs <- unlist(strsplit(params$SRR_IDs, " "))
-GSM_IDs <- unlist(strsplit(params$GSM_IDs, " "))
+srr_ids <- unlist(strsplit(params$srr_ids, " "))
+gsm_ids <- unlist(strsplit(params$gsm_ids, " "))
 ```
 
-```{r Read data, include = FALSE}
+```{r Read data}
+#| include: false
 # Read counts
 counts <- read.delim(counts_file,
                      header       = TRUE,
@@ -50,13 +52,13 @@ colnames(counts_summary) <- gsub(".*(SRR[0-9]+)\\..*", "\\1",
 
 # Read metadata
 meta <- data.frame()
-for (GSM in GSM_IDs) {
+for (GSM in gsm_ids) {
     gsm <- Meta(getGEO(GSM))
     current_meta <- as.data.frame(do.call(cbind, gsm))
     meta <- rbind(meta, current_meta)
 }
 meta <- meta[c("title", "geo_accession", "source_name_ch1", "characteristics_ch1")]
-gsm2srr <- data.frame(geo_accession = GSM_IDs, SRR = SRR_IDs)
+gsm2srr <- data.frame(geo_accession = gsm_ids, SRR = srr_ids)
 meta <- merge(meta, gsm2srr, by = "geo_accession")
 
 # Read FastQC data and update column names
@@ -69,7 +71,7 @@ patterns <- c(".+percent_duplicates.*",
 subs <- c("Percent duplicates", "Percent GC", "Avg sequence length",
           "Percent fails", "Total sequences")
 for (i in 1:length(patterns)) {
-  colnames(qc) <- gsub(patterns[i], subs[i], colnames(qc))
+    colnames(qc) <- gsub(patterns[i], subs[i], colnames(qc))
 }
 meta <- merge(meta, qc, by.x = "SRR", by.y = "Sample")
 ```
@@ -106,7 +108,8 @@ ggplot(count_data, aes(x = Sample, y = Reads, fill = Feature)) +
     geom_bar(stat = "identity")
 ```
 
-```{r Gene heatmap, fig.height = 14}
+```{r Gene heatmap}
+#| fig-height: 14
 cv_cutoff <- 1.2
 max_cutoff <- 5
 heatmap_data <-
@@ -127,9 +130,9 @@ show(gg)
 The code for reproducing this analysis is available in this [GitHub repo](https://github.com/NBISweden/workshop-reproducible-research/tree/master/docker).
 The repo contains:
 
-* A Snakemake workflow for running all analysis steps
-* A Conda environment file for installing all needed dependenciesx
-* A Docker file for running the analysis in a well-defined and isolated system
+- A Snakemake workflow for running all analysis steps
+- A Conda environment file for installing all needed dependenciesx
+- A Docker file for running the analysis in a well-defined and isolated system
 
 The results in this supplementary were generated in the following R environment: