Proposed Analysis for SCPCAB0024: Cell Type Annotation of Pediatric Osteosarcoma #1226
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Hi @khoidnutsw. I'm Jen, the Scientific Community Manager at the Data Lab. Thank you for sharing your proposed analysis! Our team will be reviewing your submission and will follow up with next steps soon. You can expect to hear from us by next Wednesday. We will also be setting up an AWS account for you. Once we do, you should receive an email to finish setting up your account. Here are instructions for setting up AWS. I'll reach out again when you should be expecting to see this. In the meantime, please let me know if you have any questions about OpenScPCA. We look forward to working together! |
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Hi @khoidnutsw, I'm Stephanie, one of the Data Scientists with the Childhood Cancer Data Lab here to help you get started on your analysis! To begin, I'd like to discuss some aspects of your proposed analysis to both give you some tips on working on your module, and to learn more about your planned approach. Specific questions about the proposalFirst, I would like to ask some follow-up questions about your proposal. Overall, it seems like you have laid out a very strong concept for a general cell type annotation pipeline, but it is not very specific to this data and/or the exact tasks you plan to carry out here. It would be great to learn more about what you're thinking for these particular samples :) For example, here are some of the specific questions I have, to give you a sense of what level of detail we're looking for before you get started.
Please feel free to take some time to think about the specific analysis steps you would like to take here, since this will both help you to structure your analysis, and it will help us to review your code and support you along the way! As an example of this, one important part of OpenScPCA-analysis contributing is scoping pull requests to ensure manageable units of work as you build up the module. Having a detailed scientific plan in place will help you to plan your work and scope your PRs to have a much smoother experience overall. Let's continue the discussion here as you refine your analysis plan, happy to chat anytime! :) General information about contributingIn addition, I wanted to offer some general information about contributing to OpenScPCA as it relates to some items in your proposal:
Thanks for your interest in OpenScPCA, and looking forward to chatting more about your proposal! Let me know if I can clarify anything so far too :) |
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Hi Stephanie, Thanks so much for the detailed feedback and for helping me get started! I appreciate the guidance on streamlining the analysis and leveraging the existing OpenScPCA resources. Regarding your questions about the proposal, I clarify my analysis intention as below:
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Proposed analysis
This analysis proposes to perform comprehensive cell type annotation for the single-cell RNA sequencing dataset associated with Group ID SCPCAB0024 (Project ID SCPCP000017), which focuses on pediatric osteosarcoma. The goal is to identify and characterize the various cell populations present within the tumor microenvironment and circulating tumor cells, providing a detailed cellular atlas, with a specific focus on refining cell-level annotation and distinguishing cancer cells from normal cells.
Scientific goals
The scientific goals of this analysis are directly aligned with the project's objectives as outlined in its abstract:
Describe the tumor and immune microenvironment: Accurately identify and quantify the diverse cell types (e.g., malignant osteosarcoma cells, various immune cell subsets, stromal cells) within the primary and metastatic osteosarcoma samples.
Analyze individual circulating tumor cells (CTCs): Characterize the phenotypic and potential phylogenetic relationship of CTCs to primary and metastatic disease by assigning their cell types and comparing their profiles to those of tumor cells from solid tissues.
Correlate gene expression patterns with clinical behavior: Lay the groundwork for future analyses that correlate the identified cell types and their gene expression patterns with clinical variables such as the presence of metastasis and response to therapy, ultimately aiming to discover biomarkers predictive of clinical behavior.
Distinguish cancer from normal cells at the single-cell level: Develop and apply methods to accurately identify malignant osteosarcoma cells and differentiate them from non-malignant cells within the tumor microenvironment and circulation.
Methods or approach
The cell type annotation will follow a standard single-cell RNA sequencing analysis workflow, adapted for robust and accurate cell type identification, with additional steps for cancer/normal cell detection:
Data Preprocessing: Quality control, normalization, and dimensionality reduction (e.g., PCA, UMAP) of the raw gene expression data.
Clustering: Unsupervised clustering algorithms (e.g., Leiden or Louvain clustering) will be applied to group cells with similar gene expression profiles.
Marker Gene Identification: Differential gene expression analysis will be performed for each cluster to identify unique marker genes.
Cell Type Annotation:
Manual Annotation: Clusters will be manually annotated based on established cell type-specific marker genes from scientific literature and publicly available databases (e.g., CellMarker, Human Cell Atlas).
Reference-based Annotation (if applicable): Utilize computational tools (e.g., Seurat's FindTransferAnchors and TransferData, SingleR, Azimuth) to integrate and annotate cells using well-curated reference datasets of known cell types, particularly for immune cell populations.
Doublet Detection and Removal: Implement methods to identify and remove potential doublet cells to ensure accurate cell type assignments.
Cancer/Normal Cell Detection:
Copy Number Variation (CNV) Inference: Infer large-scale chromosomal CNVs from single-cell RNA-seq data (e.g., using inferCNV, CopyKAT) to identify aneuploid cells characteristic of cancer.
Differential Expression Analysis: Compare gene expression profiles between putative tumor clusters and normal cell clusters (e.g., fibroblasts, epithelial cells from normal tissue if available) to identify cancer-specific gene signatures.
Integration with Clinical/Genomic Data: If available, integrate information on known genomic alterations in osteosarcoma to support the identification of malignant cells.
Pathway Analysis: Perform pathway enrichment analysis on identified cancer cell clusters to understand underlying biological processes.
Refined Cell-Level Annotation: Based on the combined results of marker gene analysis, reference-based annotation, and cancer/normal cell detection, refine the cell type assignments to a granular level, distinguishing between malignant and non-malignant cell types.
Visualization: Generate high-quality visualizations (e.g., UMAP/t-SNE plots colored by cell type and cancer/normal status, heatmaps of marker genes, CNV profiles) to represent the identified cell populations.
Report Generation: A comprehensive report detailing the methods, results, and visualizations will be generated, suitable for contribution to the OpenScPCA-analysis repository.
Existing modules
This analysis will consume the processed single-cell RNA-seq data from the SCPCP000017 project. It is anticipated that the output of this analysis (cell type assignments, including cancer/normal status) will serve as input for downstream analyses, such as differential expression analysis between disease states or trajectory inference, potentially relating to existing or future modules focused on biological interpretation.
Input data
The primary input data will be the single-cell RNA-seq data (gene expression matrices and associated metadata) for the 28 osteosarcoma samples within Project ID SCPCP000017 (Group ID SCPCAB0024). This includes both primary tumor and circulating tumor cell data, as described in the project details.
Scientific literature
Relevant scientific literature on osteosarcoma biology, single-cell RNA sequencing analysis, and cell type annotation methodologies will be consulted.
Other details
No response
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