hide-deconv 0.2.0

HIDE-Deconv - Hierarchical Cell Type Deconvolution

HIDE-deconv

Interactive command line tool and python package for hierarchical deconvolution and analysis of bulk RNA-seq data.

Features

• Designed for AnnData single cell datasets
• Open Source package, that can be run on safe servers
• Hierarchical cell type deconvolution for any number of cell type annotation layers
• Includes methods for post-deconvolution analysis
• Usable via command line interface and Python API
• Provides a guided workflow that allows users without programming experience to perform deconvolution

For a detailed overview of all features and usage, please read the Userguide

Installation

# Create and activate a new virtual environment (recommended)
python3 -m venv .venv
source .venv/bin/activate

# Install HIDE-deconv
pip install hide-deconv

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Necessary Data

• Single-cell data: Annotated AnnData (.h5ad) file with gene names in adata.var_names and cell type annotations for each desired layer in adata.obs (at least one layer of cell type annotations is necessary).
• Bulk RNA-seq data: CSV file, genes as row index, samples as columns. Gene IDs must match single-cell data.
• Sample sheet (optional): CSV with sample meta-information (e.g., cohort, survival time, event).
• Data Normalization: We recommend to use raw counts for all datasets.

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Command Line Workflow

Deconvolution (standard workflow):

hide-deconv run --path <project_dir>

This initializes the project, preprocesses data, trains the model, and runs deconvolution.

Difference in composition & survival analysis:

hide-deconv analyze diff --path <project_dir>
hide-deconv analyze survival --path <project_dir>

• For difference analysis, the sample sheet must contain columns for sample ID and cohort.
• For survival analysis, the sample sheet must contain columns for sample ID, survival time and event. Values in the event column must be either "1": event occured or "0": no event.

Command overview:

hide-deconv help

This displays a short introduction to the command line interface and gives an overview of all available commands.

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Tutorials

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API Example

import anndata as ad
import pandas as pd
import numpy as np
from hide_deconv.preprocessing import (
	train_test_split_adata,
	create_reference,
	create_hierarchy,
	create_bulks,
)
from hide_deconv.models import HIDE
from hide_deconv.statistic import run_mann_whitney_u

# 1. Load AnnData
adata = ad.read_h5ad("single_cells.h5ad")

# 2. Split into training and test set
adata_train, adata_test = train_test_split_adata(adata, celltype_col="cell_type", train_frac=0.7)

# 3. Create reference profiles and hierarchy (single layer example)
X_sub = create_reference(adata_train, celltype_col="cell_type")
A_l = [pd.DataFrame(np.eye(X_sub.shape[1]), index=X_sub.columns, columns=X_sub.columns)]
X_l = [X_sub]

# 4. Simulate training bulks
Y_train, C_train = create_bulks(adata_train, n_bulks=1000, n_cells_per_bulk=100, celltype_col="cell_type")

# 5. Simulate test bulks
Y_test, C_test = create_bulks(adata_test, n_bulks=100, n_cells_per_bulk=100, celltype_col="cell_type")

# 6. Initialize and train model
hide = HIDE(X_l, A_l)
hide.train(Y_train, C_train, iter=1000)

# 7. Deconvolution on test data
results = hide.predict(Y_test, norm=True)["prediction"]

# 8. Optional: Difference in composition analysis
# (requires a sample sheet with columns 'SampleID' and 'Cohort')

# sample_sheet = read_csv("sample_sheet.csv")
# diff = run_mann_whitney_u(results[0], sample_sheet, sample_id_col="SampleID", cohort_col="Cohort")

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Citation

HIDE-deconv's deconvolution algorithm is based on HIDE: Hierarchical Cell Type Deconvolution. If you use HIDE-deconv, please cite the following article.

Dennis Völkl, Malte Mensching-Buhr, Thomas Sterr, Sarah Bolz, Andreas Schäfer, Nicole Seifert, Jana Tauschke, Austin Rayford, Oddbjørn Straume, Helena U Zacharias, Sushma Nagaraja Grellscheid, Tim Beissbarth, Michael Altenbuchinger, Franziska Görtler, HIDE: hierarchical cell-type deconvolution, Bioinformatics, Volume 41, Issue Supplement_1, July 2025, Pages i207–i216, https://doi.org/10.1093/bioinformatics/btaf179

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License

This project is licensed under the MIT License.

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Contact

For questions, support or scientific collaboration:

• Dennis Voelkl: dennis.k.voelkl(at)uib.no
• Franziska Goertler: Franziska.Gortler(at)uib.no

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