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Cell annotation#

Motivation#

Cell annotation is a procedure that assigns identities to cells based on their transcriptional profiles. This process can be programmatically executed using two distinct approaches: gene marker/signature-based and reference-based. In our pipeline, we leverage signature-based methods to assign cell identity

Important

In the current version, we have tested the annotation only at the major cell level. However, users can execute it at multiple levels, such as T-cell subtypes on HPC version.

Step-by-step#

To ensure reproducibility, we consolidated markers from various publications into a single database. Altogether, the cell annotation database encompasses 390 gene markers across 13 distinct cell types.

1. Running pipeline#

1.1. On the HPC#

HPC

  • workflow_level = Annotation
  • input_cell_markers_db = ./assets/cell_markers_database.csv
  • input_annotation_level = Major cells
nextflow run main.nf --workflow_level Annotation --project_name Training --sample_csv sample_table.csv --meta_data meta_data.csv --cancer_type Ovarian -resume -profile seadragon

1.2. On Cirro#

Alternatively, we execute this task on Cirro.

Cirro

  • Defining the pipeline entrypoint = Annotation
  • Input cell markers = Default
  • Annotation level = Major cells

On Cirro, users should (Do not run):

  • Navigate to the Pipelines tab and enter "BTC scRNA Pipeline" in the search engine.
  • Change the Dataset to BTC Training dataset and the Copy Parameters From option to Run_01.
  • Double-check the aforementioned parameters and click Run.

Be aware that the input_cell_markers_db parameter permits users to replace the cell annotation database. Alternatively, users can also append additional markers to the CSV file. Kindly refer to the Advanced config section for a deeper understanding of this process.

2. Inspecting report#

For convenience the figures can be located in the Test_annotation_report.html report within the Run_02 dataset.

Info

The database for cell markers is stored in the pipeline repository. You can access it here.

2.1. Cell annotation#

The nonMalignant cells were categorized into six distinct populations: B/Plasma Cells, Endothelial Cells, Fibroblasts, Myeloid Cells, T-Cells, and NK Cells.

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2.2. Cell signatures#

Furthermore, the pipeline offers a FeaturePlot to examine module score values associated with each cell type across clusters.

Image caption

3. Exercise: Conducting an in-depth immune cell annotation#

Question

What would occur if we include Dendritic Cell Subsets in the 'Annotation Level' parameter? A: Run_Dendritic

Please note: When configuring the pipeline on Cirro, ensure that the Dataset is set to BTC Training dataset and select Run_02 for the Copy Parameters From option. Additionally, configure the Entrypoint parameter to Annotation.

References#

  1. scTyper: a comprehensive pipeline for the cell typing analysis of single-cell RNA-seq data