SSRE: Cell Type Detection Based on Sparse Subspace Representation and Similarity Enhancement

AbstractAccurate identification of cell types from single-cell RNA sequencing (scRNA-seq) data plays a critical role in a variety of scRNA-seq analysis studies. It corresponds to solving an unsupervised clustering problem, in which the similarity measurement between cells in a high dimensional space affects the result significantly. Although many approaches have been proposed recently, the accuracy of cell type identification still needs to be improved. In this study, we proposed a novel single-cell clustering framework based on similarity learning, called SSRE. In SSRE, we model the relationships between cells based on subspace assumption and generate a sparse representation of the cell-to-cell similarity, which retains the most similar neighbors for each cell. Besides, we adopt classical pairwise similarities incorporated with a gene selection and enhancement strategy to further improve the effectiveness of SSRE. For performance evaluation, we applied SSRE in clustering, visualization, and other exploratory data analysis processes on various scRNA-seq datasets. Experimental results show that SSRE achieves superior performance in most cases compared to several state-of-the-art methods.

Download Full-text

Bayesian Inference for Single-cell Clustering and Imputing

Genomics and Computational Biology ◽

10.18547/gcb.2017.vol3.iss1.e46 ◽

2017 ◽

Vol 3 (1) ◽

pp. 46 ◽

Cited By ~ 25

Author(s):

Elham Azizi ◽

Sandhya Prabhakaran ◽

Ambrose Carr ◽

Dana Pe'er

Keyword(s):

Single Cell ◽

Cell Types ◽

Superior Performance ◽

Underlying Structure ◽

Specific Information ◽

Cell Type ◽

Cell Clustering ◽

Bayesian Probabilistic Model ◽

Cell Type Specific ◽

Cell Data

Single-cell RNA-seq gives access to gene expression measurements for thousands of cells, allowing discovery and characterization of cell types. However, the data is noise-prone due to experimental errors and cell type-specific biases. Current computational approaches for analyzing single-cell data involve a global normalization step which introduces incorrect biases and spurious noise and does not resolve missing data (dropouts). This can lead to misleading conclusions in downstream analyses. Moreover, a single normalization removes important cell type-specific information. We propose a data-driven model, BISCUIT, that iteratively normalizes and clusters cells, thereby separating noise from interesting biological signals. BISCUIT is a Bayesian probabilistic model that learns cell-specific parameters to intelligently drive normalization. This approach displays superior performance to global normalization followed by clustering in both synthetic and real single-cell data compared with previous methods, and allows easy interpretation and recovery of the underlying structure and cell types.

Download Full-text

Unsupervised cell functional annotation for single-cell RNA-Seq

10.1101/2021.11.20.469410 ◽

2021 ◽

Author(s):

Dongshunyi Li ◽

Jun Ding ◽

Ziv Bar-Joseph

Keyword(s):

Single Cell ◽

Dimensional Space ◽

Cell Types ◽

Specific Gene ◽

Rna Seq ◽

Cell Type ◽

Sequencing Data ◽

Gene Sets ◽

Supervised Methods ◽

Low Dimensional

One of the first steps in the analysis of single cell RNA-Sequencing data (scRNA-Seq) is the assignment of cell types. While a number of supervised methods have been developed for this, in most cases such assignment is performed by first clustering cells in low-dimensional space and then assigning cell types to different clusters. To overcome noise and to improve cell type assignments we developed UNIFAN, a neural network method that simultaneously clusters and annotates cells using known gene sets. UNIFAN combines both, low dimension representation for all genes and cell specific gene set activity scores to determine the clustering. We applied UNIFAN to human and mouse scRNA-Seq datasets from several different organs. As we show, by using knowledge on gene sets, UNIFAN greatly outperforms prior methods developed for clustering scRNA-Seq data. The gene sets assigned by UNIFAN to different clusters provide strong evidence for the cell type that is represented by this cluster making annotations easier.

Download Full-text

CellMixS: quantifying and visualizing batch effects in single cell RNA-seq data

10.1101/2020.12.11.420885 ◽

2020 ◽

Author(s):

Almut Luetge ◽

Joanna Zyprych-Walczak ◽

Urszula Brykczynska Kunzmann ◽

Helena L Crowell ◽

Daniela Calini ◽

...

Keyword(s):

Single Cell ◽

Dimensional Space ◽

Synthetic Data ◽

Real Data ◽

Cell Types ◽

Batch Effects ◽

Cell Type ◽

Cell Type Specificity ◽

Distance Distributions ◽

A Cell

A key challenge in single cell RNA-sequencing (scRNA-seq) data analysis are dataset- and batch-specific differences that can obscure the biological signal of interest. While there are various tools and methods to perform data integration and correct for batch effects, their performance can vary between datasets and according to the nature of the bias. Therefore, it is important to understand how batch effects manifest in order to adjust for them in a reliable way. Here, we systematically explore batch effects in a variety of scRNA-seq datasets according to magnitude, cell type specificity and complexity. We developed a cell-specific mixing score (cms) that quantifies how well cells from multiple batches are mixed. By considering distance distributions (in a lower dimensional space), the score is able to detect local batch bias and differentiate between unbalanced batches (i.e., when one cell type is more abundant in a batch) and systematic differences between cells of the same cell type. We implemented cms and related metrics to detect batch effects or measure structure preservation in the CellMixS R/Bioconductor package. We systematically compare different metrics that have been proposed to quantify batch effects or bias in scRNA-seq data using real datasets with known batch effects and synthetic data that mimic various real data scenarios. While these metrics target the same question and are used interchangeably, we find differences in inter- and intra-dataset scalability, sensitivity and in a metric's ability to handle batch effects with differentially abundant cell types. We find that cell-specific metrics outperform cell type-specific and global metrics and recommend them for both method benchmarks and batch exploration.

Download Full-text

Optimal marker gene selection for cell type discrimination in single cell analyses

Nature Communications ◽

10.1038/s41467-021-21453-4 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Bianca Dumitrascu ◽

Soledad Villar ◽

Dustin G. Mixon ◽

Barbara E. Engelhardt

Keyword(s):

Single Cell ◽

Gene Selection ◽

Marker Gene ◽

Cell Types ◽

Specific Cell ◽

Cell Type ◽

Computationally Efficient ◽

Data Set ◽

Gene Markers

AbstractSingle-cell technologies characterize complex cell populations across multiple data modalities at unprecedented scale and resolution. Multi-omic data for single cell gene expression, in situ hybridization, or single cell chromatin states are increasingly available across diverse tissue types. When isolating specific cell types from a sample of disassociated cells or performing in situ sequencing in collections of heterogeneous cells, one challenging task is to select a small set of informative markers that robustly enable the identification and discrimination of specific cell types or cell states as precisely as possible. Given single cell RNA-seq data and a set of cellular labels to discriminate, scGeneFit selects gene markers that jointly optimize cell label recovery using label-aware compressive classification methods. This results in a substantially more robust and less redundant set of markers than existing methods, most of which identify markers that separate each cell label from the rest. When applied to a data set given a hierarchy of cell types as labels, the markers found by our method improves the recovery of the cell type hierarchy with fewer markers than existing methods using a computationally efficient and principled optimization.

Download Full-text

Straightforward clustering of single-cell RNA-Seq data with t-SNE and DBSCAN

10.1101/770388 ◽

2019 ◽

Cited By ~ 3

Author(s):

Florian Wagner

Keyword(s):

Single Cell ◽

Mononuclear Cells ◽

Gene Selection ◽

Real Data ◽

Cell Types ◽

Fine Tuning ◽

Rna Seq ◽

Clustering Methods ◽

Cell Type ◽

Peripheral Blood Mononuclear

AbstractClustering of cells by cell type is arguably the most common and repetitive task encountered during the analysis of single-cell RNA-Seq data. However, as popular clustering methods operate largely independently of visualization techniques, the fine-tuning of clustering parameters can be unintuitive and time-consuming. Here, I propose Galapagos, a simple and effective clustering workflow based on t-SNE and DBSCAN that does not require a gene selection step. In practice, Galapagos only involves the fine-tuning of two parameters, which is straightforward, as clustering is performed directly on the t-SNE visualization results. Using peripheral blood mononuclear cells as a model tissue, I validate the effectiveness of Galapagos in different ways. First, I show that Galapagos generates clusters corresponding to all main cell types present. Then, I demonstrate that the t-SNE results are robust to parameter choices and initialization points. Next, I employ a simulation approach to show that clustering with Galapagos is accurate and robust to the high levels of technical noise present. Finally, to demonstrate Galapagos’ accuracy on real data, I compare clustering results to true cell type identities established using CITE-Seq data. In this context, I also provide an example of the primary limitation of Galapagos, namely the difficulty to resolve related cell types in cases where t-SNE fails to clearly separate the cells. Galapagos helps to make clustering scRNA-Seq data more intuitive and reproducible, and can be implemented in most programming languages with only a few lines of code.

Download Full-text

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

10.1101/2021.02.09.430550 ◽

2021 ◽

Author(s):

Dongyuan Song ◽

Kexin Aileen Li ◽

Zachary Hemminger ◽

Roy Wollman ◽

Jingyi Jessica Li

Keyword(s):

Single Cell ◽

Gene Selection ◽

Spatial Information ◽

Dimensional Space ◽

Single Cells ◽

High Sensitivity ◽

Cell Types ◽

Gene Profiling ◽

Selection Methods ◽

Low Dimensional

AbstractSingle-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. 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 cell-type annotation on targeted gene profiling data.

Download Full-text

AutoGeneS: Automatic gene selection using multi-objective optimization for RNA-seq deconvolution

10.1101/2020.02.21.940650 ◽

2020 ◽

Cited By ~ 5

Author(s):

Hananeh Aliee ◽

Fabian Theis

Keyword(s):

Single Cell ◽

Prior Knowledge ◽

Gene Selection ◽

Ground Truth ◽

Cell Types ◽

Cellular Heterogeneity ◽

Marker Genes ◽

Rna Seq ◽

Cell Type ◽

The Impact

AbstractTissues are complex systems of interacting cell types. Knowing cell-type proportions in a tissue is very important to identify which cells or cell types are targeted by a disease or perturbation. When measuring such responses using RNA-seq, bulk RNA-seq masks cellular heterogeneity. Hence, several computational methods have been proposed to infer cell-type proportions from bulk RNA samples. Their performance with noisy reference profiles highly depends on the set of genes undergoing deconvolution. These genes are often selected based on prior knowledge or a single-criterion test that might not be useful to dissect closely correlated cell types. In this work, we introduce AutoGeneS, a tool that automatically extracts informative genes and reveals the cellular heterogeneity of bulk RNA samples. AutoGeneS requires no prior knowledge about marker genes and selects genes by simultaneously optimizing multiple criteria: minimizing the correlation and maximizing the distance between cell types. It can be applied to reference profiles from various sources like single-cell experiments or sorted cell populations. Results from human samples of peripheral blood illustrate that AutoGeneS outperforms other methods. Our results also highlight the impact of our approach on analyzing bulk RNA samples with noisy single-cell reference profiles and closely correlated cell types. Ground truth cell proportions analyzed by flow cytometry confirmed the accuracy of the predictions of AutoGeneS in identifying cell-type proportions. AutoGeneS is available for use via a standalone Python package (https://github.com/theislab/AutoGeneS).

Download Full-text

Optimal marker gene selection for cell type discrimination in single cell analyses

10.1101/599654 ◽

2019 ◽

Cited By ~ 3

Author(s):

Bianca Dumitrascu ◽

Soledad Villar ◽

Dustin G. Mixon ◽

Barbara E. Engelhardt

Keyword(s):

Single Cell ◽

Gene Selection ◽

Marker Gene ◽

Cell Types ◽

Specific Cell ◽

Gene Transcript ◽

Cell Type ◽

Computationally Efficient ◽

Data Set

Single-cell technologies characterize complex cell populations across multiple data modalities at un-precedented scale and resolution. Multi-omic data for single cell gene expression,in situhybridization, or single cell chromatin states are increasingly available across diverse tissue types. When isolating specific cell types from a sample of disassociated cells or performingin situsequencing in collections of heterogeneous cells, one challenging task is to select a small set of informative markers to identify and differentiate specific cell types or cell states as precisely as possible. Given single cell RNA-seq data and a set of cellular labels to discriminate, scGene-Fit selects gene transcript markers that jointly optimize cell label recovery using label-aware compressive classification methods, resulting in a substantially more robust and less redundant set of markers than existing methods. When applied to a data set given a hierarchy of cell type labels, the markers found by our method enable the recovery of the label hierarchy through a computationally efficient and principled optimization.

Download Full-text

Genomic Architecture of Cells in Tissues (GeACT): Study of Human Mid-gestation Fetus

10.1101/2020.04.12.038000 ◽

2020 ◽

Author(s):

Feng Tian ◽

Fan Zhou ◽

Xiang Li ◽

Wenping Ma ◽

Honggui Wu ◽

...

Keyword(s):

Transcription Factors ◽

Single Cell ◽

Human Cell ◽

Expression Profiles ◽

Single Cells ◽

Cell Types ◽

List Type ◽

Cell Type ◽

Genomic Architecture ◽

Gene Modules

SummaryBy circumventing cellular heterogeneity, single cell omics have now been widely utilized for cell typing in human tissues, culminating with the undertaking of human cell atlas aimed at characterizing all human cell types. However, more important are the probing of gene regulatory networks, underlying chromatin architecture and critical transcription factors for each cell type. Here we report the Genomic Architecture of Cells in Tissues (GeACT), a comprehensive genomic data base that collectively address the above needs with the goal of understanding the functional genome in action. GeACT was made possible by our novel single-cell RNA-seq (MALBAC-DT) and ATAC-seq (METATAC) methods of high detectability and precision. We exemplified GeACT by first studying representative organs in human mid-gestation fetus. In particular, correlated gene modules (CGMs) are observed and found to be cell-type-dependent. We linked gene expression profiles to the underlying chromatin states, and found the key transcription factors for representative CGMs.HighlightsGenomic Architecture of Cells in Tissues (GeACT) data for human mid-gestation fetusDetermining correlated gene modules (CGMs) in different cell types by MALBAC-DTMeasuring chromatin open regions in single cells with high detectability by METATACIntegrating transcriptomics and chromatin accessibility to reveal key TFs for a CGM

Download Full-text

Mapping single-cell atlases throughout Metazoa unravels cell type evolution

eLife ◽

10.7554/elife.66747 ◽

2021 ◽

Vol 10 ◽

Author(s):

Alexander J Tarashansky ◽

Jacob M Musser ◽

Margarita Khariton ◽

Pengyang Li ◽

Detlev Arendt ◽

...

Keyword(s):

Stem Cell ◽

Single Cell ◽

Cell Types ◽

The Self ◽

Cell Type ◽

Germ Layers ◽

Animal Evolution ◽

Self Assembling ◽

Animal Phyla ◽

Cell Data

Comparing single-cell transcriptomic atlases from diverse organisms can elucidate the origins of cellular diversity and assist the annotation of new cell atlases. Yet, comparison between distant relatives is hindered by complex gene histories and diversifications in expression programs. Previously, we introduced the self-assembling manifold (SAM) algorithm to robustly reconstruct manifolds from single-cell data (Tarashansky et al., 2019). Here, we build on SAM to map cell atlas manifolds across species. This new method, SAMap, identifies homologous cell types with shared expression programs across distant species within phyla, even in complex examples where homologous tissues emerge from distinct germ layers. SAMap also finds many genes with more similar expression to their paralogs than their orthologs, suggesting paralog substitution may be more common in evolution than previously appreciated. Lastly, comparing species across animal phyla, spanning mouse to sponge, reveals ancient contractile and stem cell families, which may have arisen early in animal evolution.

Download Full-text