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Fix wilms-06 typos #885

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Nov 18, 2024
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Fix wilms-06 typos #885

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0008441
fix typos in README file
sjspielman Nov 15, 2024
ead8c47
fix typos in 00b and 02b, and re-render 00b
sjspielman Nov 15, 2024
b1e44cf
a few more 02b typos
sjspielman Nov 15, 2024
9df127d
fix 04 notebook typos and re-render
sjspielman Nov 15, 2024
89a5393
fix typos in the other READMEs
sjspielman Nov 15, 2024
a6b76db
fix typos on 06 notebook, but do not regenerate
sjspielman Nov 15, 2024
f856718
fix typos on 05 notebook, but do not regenerate
sjspielman Nov 15, 2024
c3ae878
caught a few more 03 typos
sjspielman Nov 15, 2024
3cb42aa
fix 07 typos and table formatting, and regenerate notebook
sjspielman Nov 15, 2024
fb6b488
regenerate these notebooks from command line, not knit, since that af…
sjspielman Nov 15, 2024
557c3ea
Apply suggestions from code review
sjspielman Nov 15, 2024
2e69987
Spearman needs to be in caps
sjspielman Nov 15, 2024
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12 changes: 12 additions & 0 deletions .github/components/dictionary.txt
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Original file line number Diff line number Diff line change
Expand Up @@ -29,6 +29,7 @@ Cao
Carpentries
CellAssign
CELLxGENE
chemotherapies
chondrocytes
chr
CLI
Expand All @@ -50,6 +51,7 @@ demultiplexing
dendogram
derangements
designee
diploidy
discoverable
DM
DNT
Expand Down Expand Up @@ -107,6 +109,7 @@ immunities
impactful
indicia
InferCNV
intra
Jaccard
Jitter
JSON
Expand Down Expand Up @@ -160,7 +163,10 @@ overclustered
Panglao
PanglaoDB
PDX
peritubular
pluripotent
PMID
PNG
podman
podocyte
Posit
Expand All @@ -175,6 +181,7 @@ README
redistribution
redistributions
renv
repartition
repo
reproducibility
reproducibly
Expand All @@ -190,26 +197,31 @@ SCE
ScPCA
SCPCP
scRNA
scRNAseq
scrublet
SEACells
SemVar
seq
SingleR
snRNA
socio
Spearman
SSO
stemness
stroma
stromal
Stumptown
subdiagnosis
sublicensable
subtypes
subunits
symlink
symlinked
synched
TBD
Tirode
trainings
transcriptional
transferrable
transphobic
Treg
Expand Down
111 changes: 56 additions & 55 deletions analyses/cell-type-wilms-tumor-06/README.md
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Expand Up @@ -9,20 +9,20 @@ Each of these groups is composed of the blastemal, epithelial, and stromal popul

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 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
- 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
- [x] Script: clustering of cells across a set of parameters for few samples
- [x] Script: label transfer from the fetal kidney atlas reference using runAzimuth
- [x] Script: run copykat and inferCNV
- [x] Script: label transfer from the fetal kidney atlas reference using an Azimuth-adapted approach
- [x] Script: run `copyKat` and `inferCNV`
- [x] Notebook: explore results from steps 2 to 4 for about 5 to 10 samples
- [x] Script: Run inferCNV for all samples
- [x] Notebook: explore results from step 6, integrate all samples together and annotate the dataset using (i) metadatafile, (ii) CNV information, (iii) label transfer information
- [x] Script: Run `inferCNV` for all samples
- [x] Notebook: explore results from step 6 and annotate the dataset using (i) metadata file, (ii) CNV information, (iii) label transfer information

## Usage

Expand All @@ -45,7 +45,7 @@ Of note, this requires AWS CLI setup to run as intended: https://openscpca.readt
```shell
../../download-data.py --projects SCPCP000006
```
This is saving the data in OpenScPCA-analysis/data/current/SCPCP000006
This is saving the data in `OpenScPCA-analysis/data/current/SCPCP000006`

4. Run the module:
```shell
Expand All @@ -61,25 +61,25 @@ Some information can be helpful for annotation and validation:
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).
Some differences are expected, some marker genes or pathways are associated with anaplasia (see sets of marker gene).

## Output files

for each of the steps, we have two types of `output`:

- the `notebook` saved in the `notebook` directory, with a subfolder for each sample.
- the `notebook` saved in the `notebook` directory, with a folder for each sample.

- the created objects saved in `results` directory, with a subfolder for each sample.
- the created objects saved in `results` directory, with a folder for each sample.


# Analysis

## Marker sets

We first build a resource for later validation of the annotated cell types.
We gather from the litterature marker genes and specific genomic alterations that could help us characterizing the Wilms tumor ecosystem, including cancer and non-cancer cells.
We gather from the literature marker genes and specific genomic alterations that could help us characterizing the Wilms tumor ecosystem, including cancer and non-cancer cells.

### The table CellType_metadata.csv contains the following column and information:
### 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
Expand All @@ -90,31 +90,31 @@ We gather from the litterature marker genes and specific genomic alterations tha

|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:
|`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
Expand All @@ -125,11 +125,11 @@ We gather from the litterature marker genes and specific genomic alterations tha

|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|
|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|
|1q|gain|malignant|NA|`10.1016/S0002-9440(10)63982-X`|NA|Associated_with_relapse|


## workflow description
Expand All @@ -152,22 +152,23 @@ The `00_run_workflow.sh` contains the following steps:

- Exploration of clustering, label transfers, marker genes and pathways: `03_clustering_exploration.Rmd` in `notebook_template`

- CNV inference using [`infercnv`](https://github.com/broadinstitute/inferCNV/wiki) with endothelial and immune cells as reference from either the same patient or a pool of upfront resection Wilms tumor samples: `06_infercnv.R` in `script`
- CNV inference using [`inferCNV`](https://github.com/broadinstitute/inferCNV/wiki) with endothelial and immune cells as reference from either the same patient or a pool of upfront resection Wilms tumor samples: `06_inferCNV.R` in `script`


While we only selected the `infercnv` method with endothelium and immune cells as normal reference for the main workflow across samples, our analysis includes an exploration of cnv inference methods based on `copykat` and `infercnv` on a subselection of samples:
the `script` `explore-cnv-methods.R` calls the independent scripts `05_copyKAT.R` and `06_infercnv.R` for the samples
- "SCPCS000179",
- "SCPCS000184",
- "SCPCS000194",
- "SCPCS000205",
- "SCPCS000208".
While we only selected the `inferCNV` method with endothelium and immune cells as normal reference for the main workflow across samples, our analysis includes an exploration of CNV inference methods based on `copyKAT` and `inferCNV` on a subset of samples.
The script `explore-cnv-methods.R` calls the independent scripts `05_copyKAT.R` and `06_inferCNV.R` for these samples:

- `SCPCS000179`
- `SCPCS000184`
- `SCPCS000194`
- `SCPCS000205`
- `SCPCS000208`

In addition, we explored the results for all samples in one notebook twice during the analysis:

- the notebook `04_annotation_Across_Samples_exploration.Rmd` explored the annotations obtained by label transfer in all samples

- the notebook `07_annotation_Across_Samples_exploration.Rmd` explored the potential of combining label transfer and cnv to finalize the annotation of the Wilms tumor dataset.
- the notebook `07_annotation_Across_Samples_exploration.Rmd` explored the potential of combining label transfer and CNV to finalize the annotation of the Wilms tumor dataset.


For each sample and each of the step, an html report is generated and accessible in the directory `notebook`.
Expand Down Expand Up @@ -200,20 +201,20 @@ Here we will use an `Azimuth`-adapted approach to transfer labels from the refer
### Input and outputs

We start with the `_process.Rds` data to run `01_seurat-processing.Rmd`.
The output of `01_seurat-processing.Rmd` is saved in `results` in a subfolder for each sample and is the input of the second step `02a_label-transfer_fetal_full_reference_Cao.Rmd`.
The output of `01_seurat-processing.Rmd` is saved in `results` in a folder for each sample and is the input of the second step `02a_label-transfer_fetal_full_reference_Cao.Rmd`.
The output of `02a_label-transfer_fetal_full_reference_Cao.Rmd` is then the input of `02b_label-transfer_fetal_kidney_reference_Stewart.Rmd`.
Following the same approach, the output of `02b_label-transfer_fetal_kidney_reference_Stewart.Rmd` is the input of `03_clustering_exploration.Rmd` and `06_infercnv.R`.
The outputs of `06_infercnv.R` `06_infercnv_HMM-i3_{sample_id}_{reference-type}.rds` is finally the input of `07_combined_annotation_across_samples_exploration.Rmd`, which produces a TSV with annotations in `results/SCPCP000006-annotations.tsv `.
Following the same approach, the output of `02b_label-transfer_fetal_kidney_reference_Stewart.Rmd` is the input of `03_clustering_exploration.Rmd` and `06_inferCNV.R`.
The outputs of `06_inferCNV.R` `06_inferCNV_HMM-i3_{sample_id}_{reference-type}.rds` is finally the input of `07_combined_annotation_across_samples_exploration.Rmd`, which produces a TSV with annotations in `results/SCPCP000006-annotations.tsv `.


All inputs/outputs generated and used in the main workflow are saved in the `results/{sample_id}` folder.
Results in subfolders such as `results/{sample_id}/05_copyKAT` or `results/{sample_id}/06_infercnv` have been obtained for a subselection of samples in the exploratory analysis, and are thus kept separated from the results of the main workflow.
Results in folders such as `results/{sample_id}/05_copyKAT` or `results/{sample_id}/06_inferCNV` have been obtained for a subset of samples in the exploratory analysis, and are thus kept separated from the results of the main workflow.

At the end of the workflow, we have a `Seurat`object that contains:
At the end of the workflow, we have a `Seurat` object that contains:
- normalization and clustering, dimensional reductions
- label transfer from the fetal full reference
- label transfer from the fetal kidney reference
- cnv predictions using `infercnv`
- CNV predictions using `inferCNV`

## Software requirements

Expand Down
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