More Like this

1. scPNMF: sparse gene encoding of single cells to facilitate gene selection for targeted gene profiling

   https://doi.org/10.1093/bioinformatics/btab273  

   Song, Dongyuan ; Li, Kexin ; Hemminger, Zachary ; Wollman, Roy ; Li, Jingyi Jessica ( July 2021 , Bioinformatics)

   ABSTRACT: Motivation Single-cell RNA sequencing (scRNA-seq) captures whole transcriptome information of individual cells. While scRNA-seq measures thousands of genes, researchers are often interested in only dozens to hundreds of genes for a closer study. Then, a question is how to select those informative genes from scRNA-seq data. Moreover, single-cell targeted gene profiling technologies are gaining popularity for their low costs, high sensitivity and extra (e.g. spatial) information; however, they typically can only measure up to a few hundred genes. Then another challenging question is how to select genes for targeted gene profiling based on existing scRNA-seq data. Results Here, we develop the single-cell Projective Non-negative Matrix Factorization (scPNMF) method to select informative genes from scRNA-seq data in an unsupervised way. Compared with existing gene selection methods, scPNMF has two advantages. First, its selected informative genes can better distinguish cell types. Second, it enables the alignment of new targeted gene profiling data with reference data in a low-dimensional space to facilitate the prediction of cell types in the new data. Technically, scPNMF modifies the PNMF algorithm for gene selection by changing the initialization and adding a basis selection step, which selects informative bases to distinguish cell types. We demonstrate that scPNMF outperforms the state-of-the-art gene selection methods on diverse scRNA-seq datasets. Moreover, we show that scPNMF can guide the design of targeted gene profiling experiments and the cell-type annotation on targeted gene profiling data. Availability and implementation The R package is open-access and available at https://github.com/JSB-UCLA/scPNMF. The data used in this work are available at Zenodo: https://doi.org/10.5281/zenodo.4797997. Supplementary information Supplementary data are available at Bioinformatics online. 

   more » « less
2. A model-based constrained deep learning clustering approach for spatially resolved single-cell data

   https://doi.org/10.1101/gr.276477.121  

   Lin, Xiang ; Gao, Le ; Whitener, Nathan ; Ahmed, Ashley ; Wei, Zhi ( October 2022 , Genome Research)

   Spatially resolved scRNA-seq (sp-scRNA-seq) technologies provide the potential to comprehensively profile gene expression patterns in tissue context. However, the development of computational methods lags behind the advances in these technologies, which limits the fulfillment of their potential. In this study, we develop a deep learning approach for clustering sp-scRNA-seq data, named Deep Spatially constrained Single-cell Clustering (DSSC). In this model, we integrate the spatial information of cells into the clustering process in two steps: (1) the spatial information is encoded by using a graphical neural network model, and (2) cell-to-cell constraints are built based on the spatial expression pattern of the marker genes and added in the model to guide the clustering process. Then, a deep embedding clustering is performed on the bottleneck layer of autoencoder by Kullback–Leibler (KL) divergence along with the learning of feature representation. DSSC is the first model that can use information from both spatial coordinates and marker genes to guide cell/spot clustering. Extensive experiments on both simulated and real data sets show that DSSC boosts clustering performance significantly compared with the state-of-the-art methods. It has robust performance across different data sets with various cell type/tissue organization and/or cell type/tissue spatial dependency. We conclude that DSSC is a promising tool for clustering sp-scRNA-seq data. 

   more » « less
3. Joint Grid Discretization for Biological Pattern Discovery

   https://doi.org/10.1145/3388440.3412415  

   Wang, Jiandong ; Kumar, Sajal ; Song, Mingzhou ( September 2020 , Proceedings. The 11th ACM Int'l Conf on Bioinform, Comput Biol and Health Inform)

   null (Ed.)

   The complexity, dynamics, and scale of data acquired by modern biotechnology increasingly favor model-free computational methods that make minimal assumptions about underlying biological mechanisms. For example, single-cell transcriptome and proteome data have a throughput several orders more than bulk methods. Many model-free statistical methods for pattern discovery such as mutual information and chi-squared tests, however, require discrete data. Most discretization methods minimize squared errors for each variable independently, not necessarily retaining joint patterns. To address this issue, we present a joint grid discretization algorithm that preserves clusters in the original data. We evaluated this algorithm on simulated data to show its advantage over other methods in maintaining clusters as measured by the adjusted Rand index. We also show it promotes global functional patterns over independent patterns. On single-cell proteome and transcriptome of leukemia and healthy blood, joint grid discretization captured known protein-to-RNA regulatory relationships, while revealing previously unknown interactions. As such, the joint grid discretization is applicable as a data transformation step in associative, functional, and causal inference of molecular interactions fundamental to systems biology. The developed software is publicly available at https://cran.r-project.org/package=GridOnClusters 

   more » « less
4. Deconvolution analysis of cell‐type expression from bulk tissues by integrating with single‐cell expression reference

   https://doi.org/10.1002/gepi.22494  

   Luo, Yutong ; Fan, Ruzong ( July 2022 , Genetic Epidemiology)

   To understand phenotypic variations and key factors which affect disease susceptibility of complex traits, it is important to decipher cell‐type tissue compositions. To study cellular compositions of bulk tissue samples, one can evaluate cellular abundances and cell‐type‐specific gene expression patterns from the tissue transcriptome profiles. We develop both fixed and mixed models to reconstruct cellular expression fractions for bulk‐profiled samples by using reference single‐cell (sc) RNA‐sequencing (RNA‐seq) reference data. In benchmark evaluations of estimating cellular expression fractions, the mixed‐effect models provide similar results as an elegant machine learning algorithm named cell‐type identification by estimating relative subsets of RNA transcripts (CIBERSORTx), which is a well‐known and reliable procedure to reconstruct cell‐type abundances and cell‐type‐specific gene expression profiles. In real data analysis, the mixed‐effect models outperform or perform similarly as CIBERSORTx. The mixed models perform better than the fixed models in both benchmark evaluations and data analysis. In simulation studies, we show that if the heterogeneity exists in scRNA‐seq data, it is better to use mixed models with heterogeneous mean and variance–covariance. As a byproduct, the mixed models provide fractions of covariance between subject‐specific gene expression and cell types to measure their correlations. The proposed mixed models provide a complementary tool to dissect bulk tissues using scRNA‐seq data.

    

   more » « less
5. ACTIVA : realistic single-cell RNA-seq generation with automatic cell-type identification using introspective variational autoencoders

   https://doi.org/10.1093/bioinformatics/btac095  

   Heydari, A. Ali ; Davalos, Oscar A. ; Zhao, Lihong ; Hoyer, Katrina K. ; Sindi, Suzanne S. ; Mathelier, ed., Anthony ( February 2022 , Bioinformatics)

   Single-cell RNA sequencing (scRNAseq) technologies allow for measurements of gene expression at a single-cell resolution. This provides researchers with a tremendous advantage for detecting heterogeneity, delineating cellular maps or identifying rare subpopulations. However, a critical complication remains: the low number of single-cell observations due to limitations by rarity of subpopulation, tissue degradation or cost. This absence of sufficient data may cause inaccuracy or irreproducibility of downstream analysis. In this work, we present Automated Cell-Type-informed Introspective Variational Autoencoder (ACTIVA): a novel framework for generating realistic synthetic data using a single-stream adversarial variational autoencoder conditioned with cell-type information. Within a single framework, ACTIVA can enlarge existing datasets and generate specific subpopulations on demand, as opposed to two separate models [such as single-cell GAN (scGAN) and conditional scGAN (cscGAN)]. Data generation and augmentation with ACTIVA can enhance scRNAseq pipelines and analysis, such as benchmarking new algorithms, studying the accuracy of classifiers and detecting marker genes. ACTIVA will facilitate analysis of smaller datasets, potentially reducing the number of patients and animals necessary in initial studies.

   We train and evaluate models on multiple public scRNAseq datasets. In comparison to GAN-based models (scGAN and cscGAN), we demonstrate that ACTIVA generates cells that are more realistic and harder for classifiers to identify as synthetic which also have better pair-wise correlation between genes. Data augmentation with ACTIVA significantly improves classification of rare subtypes (more than 45% improvement compared with not augmenting and 4% better than cscGAN) all while reducing run-time by an order of magnitude in comparison to both models.

   The codes and datasets are hosted on Zenodo (https://doi.org/10.5281/zenodo.5879639). Tutorials are available at https://github.com/SindiLab/ACTIVA.

   Supplementary data are available at Bioinformatics online.

    

   more » « less