Start wilms analysis

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

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+# 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

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+# Results should not be committed
+/results/*
+!/results/README.md
+
+# Ignore the scratch directory (but keep it present)
+/scratch/*
+!/scratch/.gitkeep

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

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+# pull base image
+FROM bioconductor/tidyverse:3.19
+
+
+# Change the default CRAN mirror
+RUN echo "options(repos = c(CRAN = 'https://mran.microsoft.com/snapshot/2022-02-01'), download.file.method = 'libcurl')" >> ${R_HOME}/etc/Rprofile.site
+
+
+# Set global R options
+RUN echo "options(repos = 'https://cloud.r-project.org')" > $(R --no-echo --no-save -e "cat(Sys.getenv('R_HOME'))")/etc/Rprofile.site
+ENV RETICULATE_MINICONDA_ENABLED=FALSE
+
+
+RUN R -e "devtools::install_github('enblacar/SCpubr')"
+RUN R --no-echo --no-restore --no-save -e "install.packages('remotes')"
+RUN R -e "remotes::install_github('satijalab/seurat', 'seurat5', quiet = TRUE)"  # this also install patchwork (and others)
+RUN R -e "remotes::install_github('satijalab/azimuth', quiet = TRUE)"            # this also install SingleCellExperiment, DT (and others)
+
+
+#RUN chmod -R a+rw ${R_HOME}/site-library # so that everyone can dynamically install more libraries within container
+#RUN chmod -R a+rw ${R_HOME}/library
+
+# add custom options for rstudio sessions
+# make sure sessions stay alive forever
+RUN echo "session-timeout-minutes=0" >> /etc/rstudio/rsession.conf
+# make sure we get rstudio server logs in the container
+# RUN echo $'[*]\nlog-level=warn\nlogger-type=file\n' > /etc/rstudio/logging.conf
+
+# make sure all R related binaries are in PATH in case we want to call them directly
+ENV PATH ${R_HOME}/bin:$PATH

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

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+# 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). Please before running the script, make sure that the paths are correct. 
+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
+
+In this module, we start with the processed `SingleCellExperiment` objects from the ScPCA Portal.
+Data have been downloaded locally and are found in mnt_data. the mnt_data folder has to be define in the config.yaml file or changed in the notebook accordingly. 
+
+```{r paths}
+path_to_data <- "~/mnt_data/Wilms ALSF/SCPCP000006_2024-06-25"
+```
+
+## 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|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
+
+For complete reproducibility of the results, you can build and run the docker image using the Dockerfile. This will allow you to work on RStudio (R version 4.4.1) from the based image bioconductor/tidyverse:3.19.
+
+In the config.yaml file, define your system specific parameter and paths (e.g. to the data).
+Execute the run.sh file and open RStudio in your browser (http://localhost:8080/). 
+By default, username = rstudio, password = wordpass.
+
+
+
+
+## Computational resources
+
+No need to run any analysis, just open the metadata table!

analyses/cell-type-wilms-tumor-06/config.yaml

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+project_name: maudp_ScPCA
+project_docker: cancerbits/dockr:maudp_ScPCAOpen_podman
+
+
+# SYSTEM-SPECIFIC PARAMETERS (please change to match your local setup):
+project_root_host: $CODEBASE/OpenScPCA-analysis/
+data_root_host: $DATA
+out_root_host: $OUT
+resource_root_host: $RESOURCES
+
+# PATHS AS VISIBLE WITHIN RSTUDIO (should not be changed):
+project_root: /home/rstudio
+data_root: /home/rstudio/mnt_data
+out_root: /home/rstudio/mnt_out
+resource_root: /home/rstudio/mnt_resources/
+tmp_root: /home/rstudio/mnt_tmp/
+

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

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+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

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+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,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

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

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+# Results directory instructions
+
+Files in the results directory should not be directly committed to the repository.
+
+Instead, copy results files to an S3 bucket and add a link to the S3 location in this README file.

analyses/cell-type-wilms-tumor-06/run.sh

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+#!/bin/bash
+
+
+# parse config parameters:
+source bash/parse_yaml.sh
+eval $(parse_yaml config.yaml CONF_)
+
+# ids defined in image for the rstudio user
+uid=1000
+gid=1000
+# subid ranges on host
+subuidSize=$(( $(podman info --format "{{ range .Host.IDMappings.UIDMap }}+{{.Size }}{{end }}" ) - 1 ))
+subgidSize=$(( $(podman info --format "{{ range .Host.IDMappings.GIDMap }}+{{.Size }}{{end }}" ) - 1 ))
+
+
+podman run -d --rm \
+  --name ${CONF_project_name}_${USER} \
+  -e RUNROOTLESS=false \
+  --uidmap $uid:0:1 --uidmap 0:1:$uid --uidmap $(($uid+1)):$(($uid+1)):$(($subuidSize-$uid)) \
+  --gidmap $gid:0:1 --gidmap 0:1:$gid --gidmap $(($gid+1)):$(($gid+1)):$(($subgidSize-$gid)) \
+  --group-add=keep-groups \
+  -p 8080:8787 \
+  -e PASSWORD=wordpass \
+  -e TZ=Europe/Vienna \
+  --volume=$(realpath ${CONF_project_root_host%%*##*( )}):${CONF_project_root} \
+  --volume=$(realpath ${CONF_resource_root_host%%*##*( )}):${CONF_resource_root}:ro \
+  --volume=$(realpath ${CONF_out_root_host%%*##*( )}):${CONF_out_root} \
+  --volume=$(realpath ${CONF_data_root_host%%*##*( )}):${CONF_data_root} \
+  ${CONF_project_docker}