Merge pull request #681 from maud-p/start-wilms-analysis

Start wilms analysis

.github/workflows/docker_all-modules.yml

@@ -38,6 +38,7 @@ jobs:
           - simulate-sce
           - cell-type-ewings
           - doublet-detection
+          - cell-type-wilms-tumor-06
     uses: ./.github/workflows/build-push-docker-module.yml
     if: github.repository_owner == 'AlexsLemonade'
     with:

.github/workflows/docker_cell-type-wilms-tumor-06.yml

@@ -0,0 +1,55 @@
+# This is a workflow to build the docker image for the cell-type-wilms-tumor-06 module
+#
+# Docker modules are run on pull requests when code for files that affect the Docker image have changed.
+# If other files are used during the Docker build, they should be added to `paths`
+#
+# At module initialization, this workflow is inactive, and needs to be activated manually
+
+name: Build docker image for cell-type-wilms-tumor-06
+
+concurrency:
+  # only one run per branch at a time
+  group: "docker_cell-type-wilms-tumor-06_${{ github.ref }}"
+  cancel-in-progress: true
+
+on:
+  pull_request:
+    branches:
+      - main
+    paths:
+      - "analyses/cell-type-wilms-tumor-06/Dockerfile"
+      - "analyses/cell-type-wilms-tumor-06/.dockerignore"
+      - "analyses/cell-type-wilms-tumor-06/renv.lock"
+      - "analyses/cell-type-wilms-tumor-06/conda-lock.yml"
+  workflow_dispatch:
+    inputs:
+      push-ecr:
+        description: "Push to AWS ECR"
+        type: boolean
+        required: true
+
+jobs:
+  test-build:
+    name: Test Build Docker Image
+    if: github.event_name == 'pull_request' || (contains(github.event_name, 'workflow_') && !inputs.push-ecr)
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v3
+
+      - name: Build image
+        uses: docker/build-push-action@v5
+        with:
+          context: "{{defaultContext}}:analyses/cell-type-wilms-tumor-06"
+          push: false
+          cache-from: type=gha
+          cache-to: type=gha,mode=max
+
+  build-push:
+    name: Build and Push Docker Image
+    if: github.repository_owner == 'AlexsLemonade' && (github.event_name == 'push' || inputs.push-ecr)
+    uses: ./.github/workflows/build-push-docker-module.yml
+    with:
+      module: "cell-type-wilms-tumor-06"
+      push-ecr: true

.github/workflows/run_cell-type-wilms-tumor-06.yml

@@ -0,0 +1,59 @@
+# This is a workflow to run the cell-type-wilms-tumor-06 module
+#
+# Analysis modules are run based on three triggers:
+# - Manual trigger
+# - On pull requests where code in the module has changed
+# - As a reusable workflow called from a separate workflow which periodically runs all modules
+#
+# At initialization, only the manual trigger is active
+
+name: Run cell-type-wilms-tumor-06 analysis module
+env:
+  MODULE_PATH: analyses/cell-type-wilms-tumor-06
+  AWS_DEFAULT_REGION: us-east-2
+
+concurrency:
+  # only one run per branch at a time
+  group: "run_cell-type-wilms-tumor-06_${{ github.ref }}"
+  cancel-in-progress: true
+
+on:
+  workflow_dispatch:
+  # workflow_call:
+  # pull_request:
+  #   branches:
+  #     - main
+  #   paths:
+  #     - "analyses/cell-type-wilms-tumor-06/**"
+
+jobs:
+  run-module:
+    if: github.repository_owner == 'AlexsLemonade'
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout repo
+        uses: actions/checkout@v4
+
+      - name: Set up R
+        uses: r-lib/actions/setup-r@v2
+        with:
+          r-version: 4.4.0
+          use-public-rspm: true
+
+      - name: Set up pandoc
+        uses: r-lib/actions/setup-pandoc@v2
+
+      - name: Set up renv
+        uses: r-lib/actions/setup-renv@v2
+        with:
+          working-directory: ${{ env.MODULE_PATH }}
+
+      # Update this step as needed to download the desired data
+      - name: Download test data
+        run: ./download-data.py --test-data --format SCE
+
+      - name: Run analysis module
+        run: |
+          cd ${MODULE_PATH}
+          # run module script(s) here

analyses/cell-type-wilms-tumor-06/.Rprofile

@@ -0,0 +1,4 @@
+# Don't activate renv in an OpenScPCA docker image
+if(Sys.getenv('OPENSCPCA_DOCKER') != 'TRUE'){
+  source('renv/activate.R')
+}

analyses/cell-type-wilms-tumor-06/.dockerignore

@@ -0,0 +1,8 @@
+# Ignore everything by default
+*
+
+# Include specific files in the docker environment
+!/renv.lock
+!/requirements.txt
+!/environment.yml
+!/conda-lock.yml

analyses/cell-type-wilms-tumor-06/.gitignore

@@ -0,0 +1,11 @@
+# Results should not be committed
+/results/*
+!/results/README.md
+
+# Ignore the scratch directory (but keep it present)
+/scratch/*
+!/scratch/.gitkeep
+
+# Ignore Docker-related files specific to module author's system
+config.yaml
+run.sh

analyses/cell-type-wilms-tumor-06/Dockerfile

@@ -0,0 +1,28 @@
+# Pull base image
+# This image has RStudio Server on it
+FROM bioconductor/bioconductor_docker:3.19
+
+# Labels following the Open Containers Initiative (OCI) recommendations
+# For more information, see https://specs.opencontainers.org/image-spec/annotations/?v=v1.0.1
+LABEL org.opencontainers.image.title="openscpca/cell-type-wilms-tumor-06"
+LABEL org.opencontainers.image.description="Docker image for the OpenScPCA analysis module 'cell-type-wilms-tumor-06'"
+LABEL org.opencontainers.image.authors="OpenScPCA [email protected]"
+LABEL org.opencontainers.image.source="https://github.com/AlexsLemonade/OpenScPCA-analysis/tree/main/analyses/cell-type-wilms-tumor-06"
+
+# Set an environment variable to allow checking if we are in an OpenScPCA container
+ENV OPENSCPCA_DOCKER=TRUE
+
+# Disable the renv cache to install packages directly into the R library
+ENV RENV_CONFIG_CACHE_ENABLED=FALSE
+
+# Install renv
+RUN R --no-echo --no-restore --no-save -e "install.packages('renv')"
+
+# Copy the renv.lock file from the host environment to the image
+COPY renv.lock renv.lock
+
+# restore from renv.lock file and clean up to reduce image size
+RUN Rscript -e 'renv::restore()' && \
+  rm -rf ~/.cache/R/renv && \
+  rm -rf /tmp/downloaded_packages && \
+  rm -rf /tmp/Rtmp*

analyses/cell-type-wilms-tumor-06/README.md

@@ -0,0 +1,180 @@
+# Wilms Tumor Dataset Annotation (SCPCP000006)
+
+Wilms tumor (WT) is the most common pediatric kidney cancer characterized by an exacerbated intra- and inter- tumor heterogeneity.
+The genetic landscape of WT is very diverse in each of the histological contingents.
+The COG classifies WT patients into two groups: the favorable histology and diffuse anaplasia.
+Each of these groups is composed of the blastemal, epithelial, and stromal populations of cancer cells in different proportions, as well as cells from the normal kidney, mostly kidney epithelial cells, endothelial cells, immune cells and normal stromal cells (fibroblast).
+
+## Description
+
+Here, we first aim to annotate the Wilms Tumor snRNA-seq samples in the SCPCP000006 (n=40) dataset. To do so we will:
+
+• Provide annotations of normal cells composing the kidney, including normal kidney epithelium, endothelium, stroma and immune cells
+• Provide annotations of tumor cell populations that may be present in the WT samples, including blastemal, epithelial, and stromal populations of cancer cells
+Based on the provided annotation, we would like to additionally provide a reference of marker genes for the three cancer cell populations, which is so far lacking for the WT community.
+
+The analysis is/will be divided as the following:
+
+- [x] Metadata file: compilation of a metadata file of marker genes for expected cell types that will be used for validation at a later step
+- [ ] Script: clustering of cells across a set of parameters for few samples
+- [ ] Script: label transfer from the fetal kidney atlas reference using runAzimuth
+- [ ] Script: run InferCNV
+- [ ] Notebook: explore results from steps 2 to 4 for about 5 to 10 samples
+- [ ] Script: compile scripts 2 to 4 in a RMardown file with required adjustements and render it across all samples
+- [ ] Notebook: explore results from step 6, integrate all samples together and annotate the dataset using (i) metadatafile, (ii) CNV information, (iii) label transfer information
+
+## Usage
+From Rstudio, run the Rmd reports or render the R scripts (see below R studio session set up).
+You can also simply have a look at the html reports in the notebook folder.
+Here, no need to run anything, we try to guide you through the analysis. Have a look at the code using the unhide code button on the top right of each chunk!
+
+## Input files
+
+### single nuclei data
+
+We work with the _processed.rds SingleCellExperiment objects.
+From the module directory, make sure that the conda environment is set-up:
+
+```shell
+conda activate openscpca
+```
+
+log into AWS CLI:
+```shell
+# replace `openscpca` with your AWS CLI profile name if it differs
+export AWS_PROFILE=openscpca
+aws sso login
+```
+
+use download-data.py to download the data as the following:
+```shell
+../../download-data.py --projects SCPCP000006
+```
+This is saving the data in OpenScPCA-analysis/data/current/SCPCP000006
+
+Of note, this requires AWS CLI setup to run as intended: https://openscpca.readthedocs.io/en/latest/technical-setup/environment-setup/configure-aws-cli/
+
+### sample metadata
+
+The OpenScPCA-analysis/data/current/SCPCP000006/single_cell_metadata.tsv file contains clinical information related to the samples in the dataset.
+Some information can be helpful for annotation and validation:
+
+- treatment: Some of the samples have been pre-treated with chemotherapy and some are upfront resection.
+We expect few changes between the 2 conditions, including a higher immune infiltration and more DNA damages pathways in treated samples.
+
+- histology: the COG classifies Wilms tumor as either (i) Favorable or (ii) Anaplastic.
+Some differenices are expected, some marker genes or pathways are associated with anaplasia (see sets of marker gene).
+
+## Output files
+
+## Marker sets
+
+This folder is a resource for later validation of the annotated cell types.
+
+### The table CellType_metadata.csv contains the following column and information:
+- "gene_symbol" contains the symbol of the described gene, using the HUGO Gene Nomenclature
+- ENSEMBL_ID contains the stable identifier from the ENSEMBL database
+- cell_class is either "malignant" for marker genes specific to malignant population, or "non-malignant" for markers genes specific to non-malignant tissue or "both" for marker genes that can be found in malignant as well as non-malignant tissue but are still informative in respect to the cell type.
+- cell_type contains the list of the cell types that are attributed to the marker gene
+- DOI contains the list of main publication identifiers supporting the choice of the marker gene
+- comment can be empty or contains any additional information
+
+  |gene_symbol|ENSEMBL_ID|cell_class|cell_type|DOI|comment|
+  |---|---|---|---|---|---|
+  |WT1|ENSG00000184937|malignant|cancer_cell|10.1242/dev.153163|Tumor_suppressor_WT1_is_lost_in_some_WT_cells|
+  |IGF2|ENSG00000167244|malignant|cancer_cell|10.1038/ng1293-408|NA|
+  |TP53|ENSG00000141510|malignant|anaplastic|10.1158/1078-0432.CCR-16-0985|Might_also_be_in_small_non_anaplastic_subset|
+  |MYCN|ENSG00000134323|malignant|anaplastic|10.18632/oncotarget.3377|Also_in_non_anaplastic_poor_outcome|
+  |MAX|ENSG00000125952|malignant|anaplastic|10.1016/j.ccell.2015.01.002|Also_in_non_anaplastic_poor_outcome|
+  |SIX1|ENSG00000126778|malignant|blastema|10.1016/j.ccell.2015.01.002|NA|
+  |SIX2|ENSG00000170577|malignant|blastema|10.1016/j.ccell.2015.01.002|NA|
+  |CITED1|ENSG00000125931|malignant|blastema|10.1593/neo.07358|Also_in_embryonic_kidney|
+  |PTPRC|ENSG00000081237|immune|NA|10.1101/gr.273300.120|NA|
+  |CD68|ENSG00000129226|immune|myeloid|10.1186/1746-1596-7-12|NA|
+  |CD163|ENSG00000177575|immune|macrophage|10.1186/1746-1596-7-12|NA|
+  |VWF|ENSG00000110799|endothelium|endothelium|10.1134/S1990747819030140|NA|
+  |CD3E|ENSG00000198851|immune|T_cell|10.1101/gr.273300.120|NA|
+  |MS4A1|ENSG00000156738|immune|B_cell|10.1101/gr.273300.120|NA|
+  |FOXP3|ENSG00000049768|immune|T_cell|10.1101/gr.273300.120|Treg|
+  |CD4|ENSG00000010610|immune|T_cell|10.1101/gr.273300.120|NA|
+  |CD8A|ENSG00000153563|immune|T_cell|10.1101/gr.273300.120|NA|
+  |EPCAM|ENSG00000119888|NA|epithelial|10.1016/j.stemcr.2014.05.013|epithelial_malignant_and_non_malignant|
+  |NCAM1|ENSG00000149294|malignant|blastema|10.1016/j.stemcr.2014.05.013|might_also_be_expressed_in_non_malignant|
+  |PODXL|ENSG00000128567|non-malignant|podocyte|10.1016/j.stem.2019.06.009|NA|
+  |COL6A3|ENSG00000163359|malignant|mesenchymal|10.2147/OTT.S256654|might_also_be_expressed_in_non_malignant_stroma|
+  |THY1|ENSG00000154096|malignant|mesenchymal|10.1093/hmg/ddq042|might_also_be_expressed_in_non_malignant_stroma|
+
+
+### The table GeneticAlterations_metadata.csv contains the following column and information:
+- alteration contains the number and portion of the affected chromosome
+- gain_loss contains the information regarding the gain or loss of the corresponding genetic alteration
+- cell_class is "malignant"
+- cell_type contains the list of the malignant cell types that are attributed to the marker gene, either blastemal, stromal, epithelial or NA if none of the three histology is more prone to the described genetic alteration
+- DOI contains the list of main publication identifiers supporting the choice of the genetic alteration
+- comment can be empty or contains any additional information
+
+|alteration|gain_loss|cell_class|cell_type|DOI|PMID|comment
+|---|---|---|---|---|---|---|
+|11p13|loss|malignant|NA|10.1242/dev.153163|NA|NA|
+|11p15|loss|malignant|NA|10.1128/mcb.9.4.1799-1803.1989|NA|NA|
+|16q|loss|malignant|NA|NA|1317258|Associated_with_relapse|
+|1p|loss|malignant|NA|NA|8162576|Associated_with_relapse|
+|1q|gain|malignant|NA|10.1016/S0002-9440(10)63982-X|NA|Associated_with_relapse|
+
+
+
+## Software requirements
+
+To perform the analysis, run the RMarkdown script in R (version 4.4.1).
+The main packages used are:
+- Seurat version 5
+- Azimuth version 5
+- inferCNV
+- SCpubr for visualization
+- DT for table visualization
+- DElegate for differential expression analysis
+
+### Docker
+
+To build the Docker image, run the following from this directory:
+
+```shell
+docker buildx build . -t openscpca/cell-type-wilms-tumor-06
+```
+
+The image will also be available from ECR: <https://gallery.ecr.aws/openscpca/cell-type-wilms-tumor-06>
+
+To run the container and develop in RStudio Server, run the following **from the root of the repository**, Replacing `{PASSWORD}`, including the curly braces, with a password of your choosing:
+
+```shell
+docker run \
+  --mount type=bind,target=/home/rstudio/OpenScPCA-analysis,source=$PWD \
+  -e PASSWORD={PASSWORD} \
+  -p 8787:8787 \
+  public.ecr.aws/openscpca/cell-type-wilms-tumor-06:latest
+```
+
+This will pull the latest version of the image from ECR if you do not yet have a copy locally.
+
+Navigate to <http://localhost:8787/> and log in with the username `rstudio` and the password you set.
+
+Within RStudio Server, `OpenScPCA-analysis` will point to your local copy of the repository.
+
+#### A note on Apple Silicon
+
+If you are on a Mac with an M series chip, you will not be able to use RStudio Server if you are using a `linux/amd64` or `linux/x86_84` (like the ones available from ECR).
+You must build an ARM image locally to be able to use RStudio Server within the container.
+
+#### A note for Halbritter lab internal development
+This work has been developed on a system that uses podman instead of docker. The steps to run the docker/podman images are slightly different and we saved in run-podman-internal.sh our internal approach to run the container. Please, refer to the Docker section to build and run the container instead. 
+
+### renv
+
+This module uses `renv`.
+If you are using RStudio Server within the container, the `renv` project will not be activated by default.
+You can install packages within the container and use `renv::snapshot()` to update the lockfile without activating the project without a problem in our testing.
+The `renv` lockfile is used to install R packages in the Docker image.
+
+## Computational resources
+
+

analyses/cell-type-wilms-tumor-06/components/dependencies.R

@@ -0,0 +1,8 @@
+# Tidyverse
+library(tidyverse)
+
+# Single-cell packages
+library(Seurat)  # remotes::install_github("satijalab/[email protected]")
+library(presto)  # remotes::install_github("immunogenomics/presto")
+library(Azimuth)  # remotes::install_github("satijalab/azimuth")
+library(SCpubr)

analyses/cell-type-wilms-tumor-06/marker-sets/CellType_metadata.csv

@@ -0,0 +1,23 @@
+gene_symbol,ENSEMBL_ID,cell_class,cell_type,DOI,comment
+WT1,ENSG00000184937,malignant,cancer_cell,10.1242/dev.153163,Tumor_suppressor_WT1_is_lost_in_some_WT_cells
+IGF2,ENSG00000167244,malignant,cancer_cell,10.1038/ng1293-408,NA
+TP53,ENSG00000141510,malignant,anaplastic,10.1158/1078-0432.CCR-16-0985,Might_also_be_in_small_non_anaplastic_subset
+MYCN,ENSG00000134323,malignant,anaplastic,10.18632/oncotarget.3377,Also_in_non_anaplastic_poor_outcome
+MAX,ENSG00000125952,malignant,anaplastic,10.1016/j.ccell.2015.01.002,Also_in_non_anaplastic_poor_outcome
+SIX1,ENSG00000126778,malignant,blastema,10.1016/j.ccell.2015.01.002,NA
+SIX2,ENSG00000170577,malignant,blastema,10.1016/j.ccell.2015.01.002,NA
+CITED1,ENSG00000125931,malignant,blastema,10.1593/neo.07358,Also_in_embryonic_kidney
+PTPRC,ENSG00000081237,immune,NA,10.1101/gr.273300.120,NA
+CD68,ENSG00000129226,immune,myeloid,10.1186/1746-1596-7-12,NA
+CD163,ENSG00000177575,immune,macrophage,10.1186/1746-1596-7-12,NA
+VWF,ENSG00000110799,endothelium,endothelium,10.1134/S1990747819030140,NA
+CD3E,ENSG00000198851,immune,T_cell,10.1101/gr.273300.120,NA
+MS4A1,ENSG00000156738,immune,B_cell,10.1101/gr.273300.120,NA
+FOXP3,ENSG00000049768,immune,T_cell,10.1101/gr.273300.120,Treg
+CD4,ENSG00000010610,immune,T_cell,10.1101/gr.273300.120,NA
+CD8A,ENSG00000153563,immune,T_cell,10.1101/gr.273300.120,NA
+EPCAM,ENSG00000119888,NA,epithelial,10.1016/j.stemcr.2014.05.013,epithelial_malignant_and_non_malignant
+NCAM1,ENSG00000149294,malignant,blastema,10.1016/j.stemcr.2014.05.013,might_also_be_expressed_in_non_malignant
+PODXL,ENSG00000128567,non-malignant,podocyte,10.1016/j.stem.2019.06.009,NA
+COL6A3,ENSG00000163359,malignant,mesenchymal,10.2147/OTT.S256654,might_also_be_expressed_in_non_malignant_stroma
+THY1,ENSG00000154096,malignant,mesenchymal,10.1093/hmg/ddq042,might_also_be_expressed_in_non_malignant_stroma

analyses/cell-type-wilms-tumor-06/marker-sets/GeneticAlterations_metadata.csv

@@ -0,0 +1,6 @@
+alteration,gain_loss,cell_class,cell_type,DOI,PMID,comment
+11p13,loss,malignant,NA,10.1242/dev.153163,NA,NA
+11p15,loss,malignant,NA,10.1128/mcb.9.4.1799-1803.1989,NA,NA
+16q,loss,malignant,NA,NA,1317258,Associated_with_relapse
+1p,loss,malignant,NA,NA,8162576,Associated_with_relapse
+1q,gain,malignant,NA,10.1016/S0002-9440(10)63982-X,NA,Associated_with_relapse