Translator: A Transfer Learning Approach to Facilitate Single-Cell ATAC-Seq Data Analysis from Reference Dataset

Xu, Siwei; Škarica, Mario; Hwang, Ahyeon; Dai, Yi; Lee, Che Yu; Girgenti, Matthew J.; Zhang, Jing

doi:10.1089/cmb.2021.0596

Cited by 6 publications

(1 citation statement)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While other transfer learning methods do exist, nearly all focus on the integration of scRNA-seq data with scATAC-seq [18][19][20]. Moreoever, methods that do focus on transfer learning for scATAC-seq data [21] leave much to be desired. First, they lack the ability to utilize publicaly available pre-trained models; second, they have yet to be used for cell-type annotation; and third, they lack a software framework to facilitate easy analysis.…”

Section: Introductionmentioning

confidence: 99%

Fast clustering and cell-type annotation of scATAC data using pre-trained embeddings

LeRoy,

Smith,

Zheng

et al. 2023

Preprint

View full text Add to dashboard Cite

MotivationData from the single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) is now widely available. One major computational challenge is dealing with high dimensionality and inherent sparsity, which is typically addressed by producing lower-dimensional representations of single cells for downstream clustering tasks. Current approaches produce such individual cell embeddings directly through a one-step learning process. Here, we propose an alternative approach by building embedding models pre-trained on reference data. We argue that this provides a more flexible analysis workflow that also has computational performance advantages through transfer learning.ResultsWe implemented our approach in scEmbed, an unsupervised machine learning framework that learns low-dimensional embeddings of genomic regulatory regions to represent and analyze scATAC-seq data. scEmbed is competitive with alternative scATAC embedding approaches in terms of clustering ability and has the advantage of learning patterns of region co-occurrence that can be transferred to other, unseen datasets. Moreover, pre-trained models on reference data can be exploited to build fast and accurate cell-type annotation systems without the need for other data modalities. scEmbed is implemented in Python and it is available to download from GitHub. We also make our pre-trained models available on huggingface for public use.AvailabilityscEmbed is open source and available athttps://github.com/databio/geniml. Pre-trained models from this work can be obtained on huggingface:https://huggingface.co/databio.

show abstract

Section: Introductionmentioning

confidence: 99%

Fast clustering and cell-type annotation of scATAC data using pre-trained embeddings

LeRoy,

Smith,

Zheng

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

Dual alignment feature embedding network for multi-omics data clustering

Xiao,

Yang,

et al. 2025

Knowledge-Based Systems

View full text Add to dashboard Cite

Effective multi-modal clustering method via skip aggregation network for parallel scRNA-seq and scATAC-seq data

Hu,

Liang,

Dong

et al. 2024

Briefings in Bioinformatics

View full text Add to dashboard Cite

In recent years, there has been a growing trend in the realm of parallel clustering analysis for single-cell RNA-seq (scRNA) and single-cell Assay of Transposase Accessible Chromatin (scATAC) data. However, prevailing methods often treat these two data modalities as equals, neglecting the fact that the scRNA mode holds significantly richer information compared to the scATAC. This disregard hinders the model benefits from the insights derived from multiple modalities, compromising the overall clustering performance. To this end, we propose an effective multi-modal clustering model scEMC for parallel scRNA and Assay of Transposase Accessible Chromatin data. Concretely, we have devised a skip aggregation network to simultaneously learn global structural information among cells and integrate data from diverse modalities. To safeguard the quality of integrated cell representation against the influence stemming from sparse scATAC data, we connect the scRNA data with the aggregated representation via skip connection. Moreover, to effectively fit the real distribution of cells, we introduced a Zero Inflated Negative Binomial-based denoising autoencoder that accommodates corrupted data containing synthetic noise, concurrently integrating a joint optimization module that employs multiple losses. Extensive experiments serve to underscore the effectiveness of our model. This work contributes significantly to the ongoing exploration of cell subpopulations and tumor microenvironments, and the code of our work will be public at https://github.com/DayuHuu/scEMC.

show abstract

Translator: A Transfer Learning Approach to Facilitate Single-Cell ATAC-Seq Data Analysis from Reference Dataset

Cited by 6 publications

References 40 publications

Fast clustering and cell-type annotation of scATAC data using pre-trained embeddings

Fast clustering and cell-type annotation of scATAC data using pre-trained embeddings

Dual alignment feature embedding network for multi-omics data clustering

Effective multi-modal clustering method via skip aggregation network for parallel scRNA-seq and scATAC-seq data

Contact Info

Product

Resources

About