WeietalCRC2024

The scripts for Wei et. al paper on cancer cell calling in colorectal cancer

Processed data is available from zenodo and should be unpacked into a directory structure like so (named ./data/):

. ├── cellbender │   ├── p007n_cellbender_counts.h5 │   ├── p007t_cellbender_counts.h5 │   ├── p008n_cellbender_counts.h5 │   └── ... ├── cellranger │   ├── p007n_raw_feature_bc_matrix.h5 │   ├── p007t_raw_feature_bc_matrix.h5 │   ├── p008n_raw_feature_bc_matrix.h5 │   └── ... ├── cellSNP │   ├── downsampled │   │   ├── p007t_05 │   │   │   ├── cellSNP.base.vcf │   │   │   ├── cellSNP.samples.tsv │   │   │   ├── cellSNP.tag.AD.mtx │   │   │   ├── cellSNP.tag.DP.mtx │   │   │   └── cellSNP.tag.OTH.mtx │   │   ├── ... │   ├── p007n │   │   ├── cellSNP.base.vcf │   │   ├── cellSNP.cells.vcf │   │   ├── cellSNP.samples.tsv │   │   ├── cellSNP.tag.AD.mtx │   │   ├── cellSNP.tag.DP.mtx │   │   └── cellSNP.tag.OTH.mtx │   ├── ... ├── numbat │   ├── p007_clone_post_2.tsv │   ├── p008_clone_post_2.tsv │   └── ... └── WGS ├── p007_filtered.vcf.gz ├── p008_filtered.vcf.gz └── ...

Analysis pipeline

Ambient RNA background removal (Snakefile)

• Ambient RNA background removal by CellBender
  • input: {sample}rawfeaturebcmatrix.h5
  • output: {sample}cellbendercounts.h5

Preprocessing

Integrate all samples from CellBender output and filter for QC parameters

Cell type annotation (coarse, epithelial cells vs immune cells vs stromal cells)

• run 1_preprocessing_h5.ipynb
  • input: all cellbender_counts.h5 object
  • output:
    • CBallcells.h5 and CBepicells.h5 that contains high-quality all/epithelial cells with calculated PCA, UMAP, diffusion map, and louvain embeddings, as well as coarse cell type annotation
    • anno/{sample}{celltype}.txt cell barcode list by sample and cell type

Identification of cancer cells

inferCNV: infer copy number alteration status from gene expression

• run inferCNV/inferCNV.ipynb to execute inferCNV

  • input: CBallcells.h5
  • output: allcellCBcounts.rds, allepicellCBcounts.rds, allcellanno.txt, allepicellanno.txt, inferCNVexpressiondata.rds and other inferCNV intermediate objects

• run inferCNV/inferCNV_result.ipynb to collect inferCNV result

  • collect inferCNV result from the previous run
  • output: infercnv_clone_scores.tsv

Numbat: infer copy number alterations from phased gene expression profiles

• run script/Snakefile for all the samples except p009
• run script//Numbat/combine_p009t1t2_and_run_numbat.ipynb since p009 have two normal samples and two tumour samples, they were run separately with modified scripts
• run script/Numbat/collect_numbat_result.ipynb to collect Numbat result of all the samples
  • output: numbat_all_output_clone_post_combined9.csv

CCISM: identify cancer cells using SNVs

• rub script/run_CCISM.sh to run CCISM on all the sample

iCMS: annotates cancer cell phenotypes (iCMS2/iCMS3)

• input
  • download the h5 object from Joanito et al.:
  • CB_epi_cells.h5
• run iCMS/preprocessing_Joanito.ipynb to filter the count matrix with the same criteria as our h5 objects
  • output adata_concat_with_joanito.h5
• run iCMS/run_scvi_snakemake.sh for scVI model training
• run iCMS/scvi_model_result_iCMS.ipynb to inspect scVI modeling result, train and inspect scANVI models
  • output `CBepicells_iCMS.h5

Integrate results from inferCNV, Numbat, CCISM, and iCMS

• run 2_integrate_tools_result.ipynb
• input
  • CB_epi_cells_iCMS.h5 which contains iCMS result
  • CCISM result
  • Numbat: numbat_all_output_clone_post_combined9.csv
  • inferCNV: infercnv_clone_scores.tsv
• output: CB_epi_Numbat_CCISM_inferCNV_iCMS.h5

Cell type annotation at finer resolution

• run 3_cell_type_annotation_finer.ipynb
  • output adata_all_full_cell_type_annotation.h5

Consensus cancer calls

• run 4_consensus_calls.ipynb
  • output CB_epi_Numbat_CCISM_inferCNV_icms_Uhlitz_resolved_identity.h5

Pseudotime and cell type enrichment

• run 5_cellrank.ipynb

Ligand and receptor expression

• run 6a_DEG_epi.ipynb for expression and differential expression analysis of epithelial cells; 6b_DEG_imm_str.ipynb for immune and stromal cells

CRC pathways and signatures expression levels

• run 7_CRC_pathway_signature.ipynb

Cell-cell interaction

• run 8_cellchat.ipynb to infer cell-cell interactions in the normal versus tumour samples

Figures in the publication (Figures.ipynb)

• run script/Figures.ipynb
  • input: data/data_consolidated.h5ad
  • output: figures/*
• Figure 2 contains data simulation result, run script/Fig2_simulations.Rmd
  • output Fig2_simulation_results.csv