Early T-cell precursor T-cell acute lymphoblastic leukemia (ETP ALL) annotation (SCPCP000003)

[UTSouthwesternDSSR]

Proposed analysis

We plan to annotate cell types for the early T-cell precursor T-cell acute lymphoblastic leukemia (ETP ALL) samples (n=30) in SCPCP000003. Our analysis involves data cleanup for low quality cells and doublets, cell type annotation, and tumor cell identification.

Scientific goals

The goal of this analysis is to curate a validated cell type annotation for ETP ALL samples in the portal. Specifically, we aim to generate the following outcomes: (i) Lists of marker genes to identify cell types in ETP ALL; (ii) Identification of tumor cells from normal cells; (iii) Refined annotation of cell types among normal cells; (iv) Annotation of sub-groups among tumor cells, if applicable.

Methods or approach

1. We will start with the processed count matrices provided by ALSF.

2. We will remove doublets if there is any in each sample, using available tools like DoubletFinder.

3. We will first perform an automated cell type annotation using SingleR with publicly available references, including datasets from literatures. Then we will run PCA, clustering, and UMAP visualization, where we expect same cell types cluster together.

4. The annotation generated in step 3 will then be complemented with manually curated marker gene lists, using tools like enrichr or cellassign. In addition, we will use the cell surface protein expression from CITE-seq for annotation confirmation.

5. We will use CNV-based tools like CopyKat to classify tumor and normal cells. Those cells with absence of inferred copy number alterations will be annotated as normal cells.

6. Finally, we will merge the whole cohort (i.e. similar sample types) together, and perform PCA, clustering, and UMAP visualization. We expect normal cell type to cluster together, and tumor cells to be separated by inter-sample heterogeneity. We will fine-tune the cell type annotation, if any cell groups with other cell types.

Existing modules

This module is based on the existing annotation workflow of Ewing sarcoma samples in #292, with some modification. We expect to follow the same workflow as outlined in #628, whenever applicable.

Input data

This analysis will use processed count matrices in SingleCellExperiment object from SCPCP000003.

Scientific literature

The following datasets might be useful on curating marker gene lists and/or cell type references, based on our initial literature study:
https://ashpublications.org/blood/article/137/18/2463/474247/Single-cell-RNA-seq-reveals-developmental
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10635362/
https://www.nature.com/articles/s41598-023-39152-z
https://www.nature.com/articles/s41375-018-0127-8

Other details

This analysis will be performed on our local machine and HPC, and will be conducted in R. We plan to share the annotation within two months.