Cell type prioritization in single-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.

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Axes of inter-sample variability among transcriptional neighborhoods reveal disease associated cell states in single-cell data

10.1101/2021.04.19.440534 ◽

2021 ◽

Author(s):

Yakir A Reshef ◽

Laurie Rumker ◽

Joyce B Kang ◽

Aparna Nathan ◽

Megan B Murray ◽

...

Keyword(s):

Rheumatoid Arthritis ◽

Single Cell ◽

Active Tuberculosis ◽

Cell Populations ◽

Cell Type ◽

Accurate Identification ◽

Shared Function ◽

Statistical Approaches ◽

Cell Data ◽

Notch Activation

As single-cell datasets grow in sample size, there is a critical need to characterize cell states that vary across samples and associate with sample attributes like clinical phenotypes. Current statistical approaches typically map cells to cell-type clusters and examine sample differences through that lens alone. Here we present covarying neighborhood analysis (CNA), an unbiased method to identify cell populations of interest with greater flexibility and granularity. CNA characterizes dominant axes of variation across samples by identifying groups of very small regions in transcriptional space, termed neighborhoods, that covary in abundance across samples, suggesting shared function or regulation. CNA can then rigorously test for associations between any sample-level attribute and the abundances of these covarying neighborhood groups. We show in simulation that CNA enables more powerful and accurate identification of disease-associated cell states than a cluster-based approach. When applied to published datasets, CNA captures a Notch activation signature in rheumatoid arthritis, redefines monocyte populations expanded in sepsis, and identifies a previously undiscovered T-cell population associated with progression to active tuberculosis.

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ESCO: single cell expression simulation incorporating gene co-expression

10.1101/2020.10.20.347211 ◽

2020 ◽

Author(s):

Jinjin Tian ◽

Jiebiao Wang ◽

Kathryn Roeder

Keyword(s):

Single Cell ◽

R Package ◽

Brain Cell ◽

Gene Interactions ◽

Cell Type ◽

Imputation Methods ◽

Biological Interest ◽

A Cell ◽

Cell Expression ◽

Cell Data

AbstractMotivationGene-gene co-expression networks (GCN) are of biological interest for the useful information they provide for understanding gene-gene interactions. The advent of single cell RNA-sequencing allows us to examine more subtle gene co-expression occurring within a cell type. Many imputation and denoising methods have been developed to deal with the technical challenges observed in single cell data; meanwhile, several simulators have been developed for benchmarking and assessing these methods. Most of these simulators, however, either do not incorporate gene co-expression or generate co-expression in an inconvenient manner.ResultsTherefore, with the focus on gene co-expression, we propose a new simulator, ESCO, which adopts the idea of the copula to impose gene co-expression, while preserving the highlights of available simulators, which perform well for simulation of gene expression marginally. Using ESCO, we assess the performance of imputation methods on GCN recovery and find that imputation generally helps GCN recovery when the data are not too sparse, and the ensemble imputation method works best among leading methods. In contrast, imputation fails to help in the presence of an excessive fraction of zero counts, where simple data aggregating methods are a better choice. These findings are further verified with mouse and human brain cell data.AvailabilityThe ESCO implementation is available as R package SplatterESCO (https://github.com/JINJINT/SplatterESCO)[email protected]

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Ensemble learning for classifying single-cell data and projection across reference atlases

Bioinformatics ◽

10.1093/bioinformatics/btaa137 ◽

2020 ◽

Vol 36 (11) ◽

pp. 3585-3587

Author(s):

Lin Wang ◽

Francisca Catalan ◽

Karin Shamardani ◽

Husam Babikir ◽

Aaron Diaz

Keyword(s):

Single Cell ◽

Cell Types ◽

Status Quo ◽

Supplementary Information ◽

Published Data ◽

Supplementary Data ◽

Cell Type ◽

Low Sensitivity ◽

Project Data ◽

Cell Data

Abstract Summary Single-cell data are being generated at an accelerating pace. How best to project data across single-cell atlases is an open problem. We developed a boosted learner that overcomes the greatest challenge with status quo classifiers: low sensitivity, especially when dealing with rare cell types. By comparing novel and published data from distinct scRNA-seq modalities that were acquired from the same tissues, we show that this approach preserves cell-type labels when mapping across diverse platforms. Availability and implementation https://github.com/diazlab/ELSA Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

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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.

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Implication of specific retinal cell-type involvement and gene expression changes in AMD progression using integrative analysis of single-cell and bulk RNA-seq profiling

Scientific Reports ◽

10.1038/s41598-021-95122-3 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Yafei Lyu ◽

Randy Zauhar ◽

Nicholas Dana ◽

Christianne E. Strang ◽

Jian Hu ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Rna Sequencing ◽

Age Related Macular Degeneration ◽

Specific Gene ◽

Cell Type ◽

Adult Human ◽

Single Cell Rna Sequencing ◽

Cell Type Specific ◽

Cell Data

AbstractAge‐related macular degeneration (AMD) is a blinding eye disease with no unifying theme for its etiology. We used single-cell RNA sequencing to analyze the transcriptomes of ~ 93,000 cells from the macula and peripheral retina from two adult human donors and bulk RNA sequencing from fifteen adult human donors with and without AMD. Analysis of our single-cell data identified 267 cell-type-specific genes. Comparison of macula and peripheral retinal regions found no cell-type differences but did identify 50 differentially expressed genes (DEGs) with about 1/3 expressed in cones. Integration of our single-cell data with bulk RNA sequencing data from normal and AMD donors showed compositional changes more pronounced in macula in rods, microglia, endothelium, Müller glia, and astrocytes in the transition from normal to advanced AMD. KEGG pathway analysis of our normal vs. advanced AMD eyes identified enrichment in complement and coagulation pathways, antigen presentation, tissue remodeling, and signaling pathways including PI3K-Akt, NOD-like, Toll-like, and Rap1. These results showcase the use of single-cell RNA sequencing to infer cell-type compositional and cell-type-specific gene expression changes in intact bulk tissue and provide a foundation for investigating molecular mechanisms of retinal disease that lead to new therapeutic targets.

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propeller: testing for differences in cell type proportions in single cell data

10.1101/2021.11.28.470236 ◽

2021 ◽

Author(s):

Belinda Phipson ◽

Choon Boon Sim ◽

Enzo R. Porrello ◽

Alex W Hewitt ◽

Joseph Powell ◽

...

Keyword(s):

Single Cell ◽

Single Cells ◽

R Package ◽

Cell Type ◽

Experimental Conditions ◽

Cell Type Composition ◽

Type Composition ◽

Biological Replication ◽

Cell Data ◽

Different Sources

Single cell RNA Sequencing (scRNA-seq) has rapidly gained popularity over the last few years for profiling the transcriptomes of thousands to millions of single cells. To date, there are more than a thousand software packages that have been developed to analyse scRNA-seq data. These focus predominantly on visualization, dimensionality reduction and cell type identification. Single cell technology is now being used to analyse experiments with complex designs including biological replication. One question that can be asked from single cell experiments which has not been possible to address with bulk RNA-seq data is whether the cell type proportions are different between two or more experimental conditions. As well as gene expression changes, the relative depletion or enrichment of a particular cell type can be the functional consequence of disease or treatment. However, cell type proportions estimates from scRNA-seq data are variable and statistical methods that can correctly account for different sources of variability are needed to confidently identify statistically significant shifts in cell type composition between experimental conditions. We present propeller, a robust and flexible method that leverages biological replication to find statistically significant differences in cell type proportions between groups. The propeller method is publicly available in the open source speckle R package (https://github.com/Oshlack/speckle).

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Automated population identification and sorting algorithms for high-dimensional single-cell data

10.1101/046664 ◽

2016 ◽

Cited By ~ 1

Author(s):

Benedict Anchang ◽

Sylvia K. Plevritis

Keyword(s):

Single Cell ◽

Cell Sorting ◽

Intracellular Signaling ◽

Expert Knowledge ◽

Single Cells ◽

Cell Type ◽

Experimental Conditions ◽

Cell Type Specific ◽

Cell Subpopulations ◽

Cell Data

AbstractCell sorting or gating homogenous subpopulations from single-cell data enables cell-type specific characterization, such as cell-type genomic profiling as well as the study of tumor progression. This highlight summarizes recently developed automated gating algorithms that are optimized for both population identification and sorting homogeneous single cells in heterogeneous single-cell data. Data-driven gating strategies identify and/or sort homogeneous subpopulations from a heterogeneous population without relying on expert knowledge thereby removing human bias and variability. We further describe an optimized cell sorting strategy called CCAST based on Clustering, Classification and Sorting Trees which identifies the relevant gating markers, gating hierarchy and partitions that define underlying cell subpopulations. CCAST identifies more homogeneous subpopulations in several applications compared to prior sorting strategies and reveals simultaneous intracellular signaling across different lineage subtypes under different experimental conditions.

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genesorteR: Feature Ranking in Clustered Single Cell Data

10.1101/676379 ◽

2019 ◽

Cited By ~ 5

Author(s):

Mahmoud M Ibrahim ◽

Rafael Kramann

Keyword(s):

Single Cell ◽

Differential Expression ◽

Expression Analysis ◽

Differential Expression Analysis ◽

Large Cell ◽

R Package ◽

Marker Genes ◽

Data Sets ◽

Cell Type ◽

Cell Data

ABSTRACTMarker genes identified in single cell experiments are expected to be highly specific to a certain cell type and highly expressed in that cell type. Detecting a gene by differential expression analysis does not necessarily satisfy those two conditions and is typically computationally expensive for large cell numbers.Here we present genesorteR, an R package that ranks features in single cell data in a manner consistent with the expected definition of marker genes in experimental biology research. We benchmark genesorteR using various data sets and show that it is distinctly more accurate in large single cell data sets compared to other methods. genesorteR is orders of magnitude faster than current implementations of differential expression analysis methods, can operate on data containing millions of cells and is applicable to both single cell RNA-Seq and single cell ATAC-Seq data.genesorteR is available at https://github.com/mahmoudibrahim/genesorteR.

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Single-Cell Sequencing Reveals Lineage-Specific Dynamic Genetic Regulation of Gene Expression During Human Cardiomyocyte Differentiation

10.1101/2021.06.03.446970 ◽

2021 ◽

Author(s):

Reem Elorbany ◽

Joshua M Popp ◽

Katherine Rhodes ◽

Benjamin J Strober ◽

Kenneth Barr ◽

...

Keyword(s):

Single Cell ◽

Cell Lines ◽

Specific Gene ◽

Specific Cell ◽

Cardiomyocyte Differentiation ◽

Cell Type ◽

Dynamic Effects ◽

Regulatory Changes ◽

Gene Regulatory ◽

Cell Data

Dynamic and temporally specific gene regulatory changes may underlie unexplained genetic associations with complex disease. During a dynamic process such as cellular differentiation, the overall cell type composition of a tissue (or an in vitro culture) and the gene regulatory profile of each cell can both experience significant changes over time. To identify these dynamic effects in high resolution, we collected single-cell RNA-sequencing data over a differentiation time course from induced pluripotent stem cells to cardiomyocytes, sampled at 7 unique time points in 19 human cell lines. We employed a flexible approach to map dynamic eQTLs whose effects vary significantly over the course of bifurcating differentiation trajectories, including many whose effects are specific to one of these two lineages. Our study design allowed us to distinguish true dynamic eQTLs affecting a specific cell lineage from expression changes driven by potentially non-genetic differences between cell lines such as cell composition. Additionally, we used the cell type profiles learned from single-cell data to deconvolve and re-analyze data from matched bulk RNA-seq samples. Using this approach, we were able to identify a large number of novel dynamic eQTLs in single cell data while also attributing dynamic effects in bulk to a particular lineage. Overall, we found that using single cell data to uncover dynamic eQTLs can provide new insight into the gene regulatory changes that occur among heterogeneous cell types during cardiomyocyte differentiation.

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