myeloma_imc

Repo for reproducing the spatial analysis on the myeloma cohort from NTNU (Standal Group) for the manuscript: [TBD]

Raw data

Raw data deposited to Zenodo: 10.5281/zenodo.17093203

Raw data includes:

• Images, segmentation masks, bone masks, steinbock raw outputs, patient metadata
• Intermediary files for phenotyping (e.g. scimap gates, scimap outputs, manually annotated and aggregated batch-corrected intensities CSVs, XGB models including performances and parameters, holdout test data for xgboost) and other models like cellcharter AutoK and trVAE models
• Processed single-cell table as .csv and .h5ad (this can be used to reproduce the Figures from the manuscript)

Processing workflow

Note: All paths need to be adjusted to the github repo and locally stored files

Image processing with Steinbock

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Steinbock has been used with standard parameters:

steinbock preprocess imc panel

steinbock preprocess imc images --hpf 50

steinbock segment deepcell --minmax --verbosity INFO

steinbock measure intensities

steinbock measure regionprops

steinbock export anndata --intensities intensities --data regionprops -o cells.h5ad

Further preprocessing

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Artifact removal with subsequent cleaning of quantification tables

Bone labeling from geojson using this script and adjusting quantification files accordingly

Phenotyping

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For detailed information on the different steps taken see our manuscript at: [TBD]

Ground Truth

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Ground truth was generated with visual manual annotation using scimap's prior knowledge approach. Note: Scimap version 2.0.5 was used, many functions for hierarchical prior knowlege driven annotation have been updated in more recent versions.

• Single images were annotated. Intermediary output files can be found here with one annotated anndata object per image and the stored gates. Aggregating single outputs into csv can be performed here. Adjusting existing files to a new hierarchical assignment table can be found in the first part of this script

Phenotyping has been performed with scanorama batch corrected (scanorama_corrected) data, however uncorrected data (uncorrected) is also present, therefore data paths in scripts have to be set accordingly. As Scanorama corrected data yielded superior performance in our model, annotations soley rely on scanorama-corrected data.

• Batch correction has been perforned with subsequent transfer to csvs. If uncorrected values should be used, these can be found in the data repository (not recommended)
  • For adjusting the per-image anndata annotations from scimap with batch corrected values, refer to the second part of this script

Training of XGBoost model

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• Ground Truth aggregated data was used to train an xgboost model. The script integrates optuna for hyperparameter tuning.
• Classifier was applied on all data. Models are uploaded to the data repository (see above)
• Running the model on holdout data was performed here. Holdout data annotations can be downloaded form the data repository

Downstream analysis

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Preprocessing

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• Transfering the classifier annotations (csv format) to the quantification tables from Steinbock output and creating an updated anndata object with comprehensive information
• Further preprocessing steps as removing/relabeling patients can be found here
• Phenotyping refinemed by:
  • subclustering unknown cells to find celltypes missed by xgboost
  • refinement (last part) of macrophages annotations in tumor aggregates. Find the thresholding data needed for this in the public data repository and here
• Spatial neighbor graph and Marker normalization can be found here. The script further includes some renamings for celltypes and neighborhoods to fit to the mansucript

CellCharter

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• To build the trVAE model and run autoK-Clustering to find the best cluster sizes for cellcharter neighborhoods, refer to this. The cellcharter pipeline to extract neighborhood labels can be found here. The trVAE model is uploaded in the same directory, autoK models are uploaded to the data repository

COZI

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Running Cozi to infer NEP scores and saving results can be found here

Preprocessing of clinical data and associated spatial scores

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• Raw Metadata is available as excel sheet on the data repository, to transform into python format and preprocess, use this script
• To get neighborhod enrichment scores per patients and saving them, refer to this script
• To connect COZI scores to patients, refer to this script

Downstream analysis of the manuscript

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All figures can be recreated directly without running the processing workflowe using the anndata object uploaded to the data repository and the interaction scores saved as csv files here All scripts for creating the figures can be found here