Visualizing and interpreting single-cell gene expression datasets with Similarity Weighted Nonnegative Embedding

SummaryHigh throughput single-cell gene expression profiling has enabled the characterization of novel cell types and developmental trajectories. Visualizing these datasets is crucial to biological interpretation, and the most popular method is t-Stochastic Neighbor embedding (t-SNE), which visualizes local patterns better than other methods, but often distorts global structure, such as distances between clusters. We developed Similarity Weighted Nonnegative Embedding (SWNE), which enhances interpretation of datasets by embedding the genes and factors that separate cell states alongside the cells on the visualization, captures local structure better than t-SNE and existing methods, and maintains fidelity when visualizing global structure. SWNE uses nonnegative matrix factorization to decompose the gene expression matrix into biologically relevant factors, embeds the cells, genes and factors in a 2D visualization, and uses a similarity matrix to smooth the embeddings. We demonstrate SWNE on single cell RNA-seq data from hematopoietic progenitors and human brain cells.

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GiniClust: detecting rare cell types from single-cell gene expression data with Gini index

Genome Biology ◽

10.1186/s13059-016-1010-4 ◽

2016 ◽

Vol 17 (1) ◽

Cited By ~ 126

Author(s):

Lan Jiang ◽

Huidong Chen ◽

Luca Pinello ◽

Guo-Cheng Yuan

Keyword(s):

Gene Expression ◽

Single Cell ◽

Gene Expression Data ◽

Gini Index ◽

Cell Types ◽

Expression Data ◽

Cell Gene Expression ◽

Cell Gene

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Optimal-Transport Analysis of Single-Cell Gene Expression Identifies Developmental Trajectories in Reprogramming

Cell ◽

10.1016/j.cell.2019.01.006 ◽

2019 ◽

Vol 176 (4) ◽

pp. 928-943.e22 ◽

Cited By ~ 91

Author(s):

Geoffrey Schiebinger ◽

Jian Shu ◽

Marcin Tabaka ◽

Brian Cleary ◽

Vidya Subramanian ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Optimal Transport ◽

Developmental Trajectories ◽

Cell Gene Expression ◽

Transport Analysis ◽

Cell Gene

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Inferring Relevant Cell Types for Complex Traits by Using Single-Cell Gene Expression

The American Journal of Human Genetics ◽

10.1016/j.ajhg.2017.09.009 ◽

2017 ◽

Vol 101 (5) ◽

pp. 686-699 ◽

Cited By ~ 45

Author(s):

Diego Calderon ◽

Anand Bhaskar ◽

David A. Knowles ◽

David Golan ◽

Towfique Raj ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Complex Traits ◽

Cell Types ◽

Cell Gene Expression ◽

Cell Gene

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In situelectro-sequencing in three-dimensional tissues

10.1101/2021.04.22.440941 ◽

2021 ◽

Author(s):

Qiang Li ◽

Zuwan Lin ◽

Ren Liu ◽

Xin Tang ◽

Jiahao Huang ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Three Dimensional ◽

Cell Types ◽

Developmental Trajectory ◽

Single Cell Level ◽

Cell Level ◽

Cell Gene Expression ◽

Cell Gene

AbstractPairwise mapping of single-cell gene expression and electrophysiology in intact three-dimensional (3D) tissues is crucial for studying electrogenic organs (e.g., brain and heart)1–5. Here, we introducein situelectro-sequencing (electro-seq), combining soft bioelectronics within situRNA sequencing to stably map millisecond-timescale cellular electrophysiology and simultaneously profile a large number of genes at single-cell level across 3D tissues. We appliedin situelectro-seq to 3D human induced pluripotent stem cell-derived cardiomyocyte (hiPSC-CM) patches, precisely registering the CM gene expression with electrophysiology at single-cell level, enabling multimodalin situanalysis. Such multimodal data integration substantially improved the dissection of cell types and the reconstruction of developmental trajectory from spatially heterogeneous tissues. Using machine learning (ML)-based cross-modal analysis,in situelectro-seq identified the gene-to-electrophysiology relationship over the time course of cardiac maturation. Further leveraging such a relationship to train a coupled autoencoder, we demonstrated the prediction of single-cell gene expression profile evolution using long-term electrical measurement from the same cardiac patch or 3D millimeter-scale cardiac organoids. As exemplified by cardiac tissue maturation,in situelectro-seq will be broadly applicable to create spatiotemporal multimodal maps and predictive models in electrogenic organs, allowing discovery of cell types and gene programs responsible for electrophysiological function and dysfunction.

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Microfluidic single-cell transcriptomics: moving towards multimodal and spatiotemporal omics

Lab on a Chip ◽

10.1039/d1lc00607j ◽

2021 ◽

Author(s):

Shichao Lin ◽

Yilong Liu ◽

Mingxia Zhang ◽

Xing Xu ◽

Yingwen Chen ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Cell Types ◽

Cellular Heterogeneity ◽

Cell Gene Expression ◽

Cell Gene

Cells are the basic units of life with vast heterogeneity. Single-cell transcriptomics unveils cell-to-cell gene expression variabilities, discovers novel cell types, and uncovers the critical roles of cellular heterogeneity in...

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scGMAI: a Gaussian mixture model for clustering single-cell RNA-Seq data based on deep autoencoder

Briefings in Bioinformatics ◽

10.1093/bib/bbaa316 ◽

2020 ◽

Author(s):

Bin Yu ◽

Chen Chen ◽

Ren Qi ◽

Ruiqing Zheng ◽

Patrick J Skillman-Lawrence ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Rapid Development ◽

Cell Types ◽

Gaussian Mixture ◽

Computational Techniques ◽

Rna Seq ◽

Fast Independent Component Analysis ◽

Cell Gene Expression ◽

Cell Gene

Abstract The rapid development of single-cell RNA sequencing (scRNA-Seq) technology provides strong technical support for accurate and efficient analyzing single-cell gene expression data. However, the analysis of scRNA-Seq is accompanied by many obstacles, including dropout events and the curse of dimensionality. Here, we propose the scGMAI, which is a new single-cell Gaussian mixture clustering method based on autoencoder networks and the fast independent component analysis (FastICA). Specifically, scGMAI utilizes autoencoder networks to reconstruct gene expression values from scRNA-Seq data and FastICA is used to reduce the dimensions of reconstructed data. The integration of these computational techniques in scGMAI leads to outperforming results compared to existing tools, including Seurat, in clustering cells from 17 public scRNA-Seq datasets. In summary, scGMAI is an effective tool for accurately clustering and identifying cell types from scRNA-Seq data and shows the great potential of its applicative power in scRNA-Seq data analysis. The source code is available at https://github.com/QUST-AIBBDRC/scGMAI/.

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Prediction of single-cell gene expression for transcription factor analysis

GigaScience ◽

10.1093/gigascience/giaa113 ◽

2020 ◽

Vol 9 (11) ◽

Cited By ~ 1

Author(s):

Fatemeh Behjati Ardakani ◽

Kathrin Kattler ◽

Tobias Heinen ◽

Florian Schmidt ◽

David Feuerborn ◽

...

Keyword(s):

Gene Expression ◽

Transcription Factor ◽

Single Cell ◽

Cell Types ◽

Single Cell Level ◽

Cell Level ◽

Novel Approach ◽

Cell Gene Expression ◽

Specific Regulation ◽

Cell Gene

Abstract Background Single-cell RNA sequencing is a powerful technology to discover new cell types and study biological processes in complex biological samples. A current challenge is to predict transcription factor (TF) regulation from single-cell RNA data. Results Here, we propose a novel approach for predicting gene expression at the single-cell level using cis-regulatory motifs, as well as epigenetic features. We designed a tree-guided multi-task learning framework that considers each cell as a task. Through this framework we were able to explain the single-cell gene expression values using either TF binding affinities or TF ChIP-seq data measured at specific genomic regions. TFs identified using these models could be validated by the literature. Conclusion Our proposed method allows us to identify distinct TFs that show cell type–specific regulation. This approach is not limited to TFs but can use any type of data that can potentially be used in explaining gene expression at the single-cell level to study factors that drive differentiation or show abnormal regulation in disease. The implementation of our workflow can be accessed under an MIT license via https://github.com/SchulzLab/Triangulate.

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Single-cell analysis delineates a trajectory toward the human early otic lineage

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1605537113 ◽

2016 ◽

Vol 113 (30) ◽

pp. 8508-8513 ◽

Cited By ~ 21

Author(s):

Megan Ealy ◽

Daniel C. Ellwanger ◽

Nina Kosaric ◽

Andres P. Stapper ◽

Stefan Heller

Keyword(s):

Gene Expression ◽

Stem Cell ◽

Single Cell ◽

Single Cell Analysis ◽

Cell Types ◽

Marker Genes ◽

Cell Analysis ◽

Cell Gene Expression ◽

Neural Ectoderm ◽

Cell Gene

Efficient pluripotent stem cell guidance protocols for the production of human posterior cranial placodes such as the otic placode that gives rise to the inner ear do not exist. Here we use a systematic approach including defined monolayer culture, signaling modulation, and single-cell gene expression analysis to delineate a developmental trajectory for human otic lineage specification in vitro. We found that modulation of bone morphogenetic protein (BMP) and WNT signaling combined with FGF and retinoic acid treatments over the course of 18 days generates cell populations that develop chronological expression of marker genes of non-neural ectoderm, preplacodal ectoderm, and early otic lineage. Gene expression along this differentiation path is distinct from other lineages such as endoderm, mesendoderm, and neural ectoderm. Single-cell analysis exposed the heterogeneity of differentiating cells and allowed discrimination of non-neural ectoderm and otic lineage cells from off-target populations. Pseudotemporal ordering of human embryonic stem cell and induced pluripotent stem cell-derived single-cell gene expression profiles revealed an initially synchronous guidance toward non-neural ectoderm, followed by comparatively asynchronous occurrences of preplacodal and otic marker genes. Positive correlation of marker gene expression between both cell lines and resemblance to mouse embryonic day 10.5 otocyst cells implied reasonable robustness of the guidance protocol. Single-cell trajectory analysis further revealed that otic progenitor cell types are induced in monolayer cultures, but further development appears impeded, likely because of lack of a lineage-stabilizing microenvironment. Our results provide a framework for future exploration of stabilizing microenvironments for efficient differentiation of stem cell-generated human otic cell types.

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