hto 1.1.7a0

A method to demultiplex hashtagged single-cell data.

HTO DND - Demultiplex Hashtag Data

hto is a Python package designed for efficient and accurate demultiplexing of hash-tagged oligonucleotides (HTOs) in single-cell data. It normalises based on observed background signal and denoises the data to remove batch effects and noise:

• Normalization: Normalize HTO data using background signal, inspired by the DSB method (see citation below).
• Denoising: Remove batch effects and noise from the data by regressing out cell by cell variation.
• Demultiplexing: Cluster and classify cells into singlets, doublets, or negatives using clustering methods like k-means or Gaussian Mixture Models (GMM).

The package supports command-line interface (CLI) usage and Python imports.

Installation

Using pip:

pip install hto

From source:

git clone https://github.com/sail-mskcc/hto_dnd.git
cd hto_dnd
pip install .

Usage

Python API

The python API is built around AnnData. It is highly recommended two work with three AnnData objects:

• adata_hto: Filtered AnnData object with HTO data, containing only actual cells.
• adata_hto_raw: Raw AnnData object with HTO data, containing actual cells and background signal.
• adata_gex: Raw AnnData object with gene expression data. This is optional and can be used to construct a more informative background signal.

import hto

# get mockdata
mockdata = hto.data.generate_hto(n_cells=1000, n_htos=3, seed=10)
adata_hto = mockdata["filtered"]
adata_hto_raw = mockdata["raw"]
adata_gex = mockdata["gex"]

# denoise, normalize, and demultiplex
adata_demux = hto.demultiplex(
  adata_hto,
  adata_hto_raw,
  adata_gex=adata_gex,
)

# see results
adata_demux.obs[["hash_id", "doublet_info"]].head()

Command-Line Interface (CLI)

The CLI provides an API for the hto demultiplex scripts. Make sure to define --adata-out to save the output.

hto demultiplex \
  --adata-hto /path/to/adata_hto.h5ad \
  --adata-hto-raw /path/to/adata_hto_raw.h5ad \
  --adata-gex /path/to/adata_gex.h5ad \
  --adata-out /path/to/output.h5ad

Data Requirements

HTO-DND requires data from cell hashing experiments where samples are labeled with hashtagged antibodies:

• HTO data (adata_hto): Filtered cell × HTO count matrix in AnnData format.
• Raw HTO data (adata_hto_raw): Unfiltered barcode × HTO count matrix including empty droplets. Required for background estimation.
• Gene expression data (adata_gex, recommended): Cell × gene count matrix for improved background estimation.