Single-cell Expression Data Research Articles

Predicting cell-to-cell communication networks using NATMI

Development of high throughput single-cell sequencing technologies has made it cost-effective to profile thousands of cells from diverse samples containing multiple cell types. To study how these different cell types work together, here we develop NATMI (Network Analysis Toolkit for Multicellular Interactions). NATMI uses connectomeDB2020 (a database of 2293 manually curated ligand-receptor pairs with literature support) to predict and visualise cell-to-cell communication networks from single-cell (or bulk) expression data. Using multiple published single-cell datasets we demonstrate how NATMI can be used to identify (i) the cell-type pairs that are communicating the most (or most specifically) within a network, (ii) the most active (or specific) ligand-receptor pairs active within a network, (iii) putative highly-communicating cellular communities and (iv) differences in intercellular communication when profiling given cell types under different conditions. Furthermore, analysis of the Tabula Muris (organism-wide) atlas confirms our previous prediction that autocrine signalling is a major feature of cell-to-cell communication networks, while also revealing that hundreds of ligands and their cognate receptors are co-expressed in individual cells suggesting a substantial potential for self-signalling.

Predicting gene regulatory networks from cell atlases.

Recent single-cell RNA-sequencing atlases have surveyed and identified major cell types across different mouse tissues. Here, we computationally reconstruct gene regulatory networks from three major mouse cell atlases to capture functional regulators critical for cell identity, while accounting for a variety of technical differences, including sampled tissues, sequencing depth, and author assigned cell type labels. Extracting the regulatory crosstalk from mouse atlases, we identify and distinguish global regulons active in multiple cell types from specialised cell type-specific regulons. We demonstrate that regulon activities accurately distinguish individual cell types, despite differences between individual atlases. We generate an integrated network that further uncovers regulon modules with coordinated activities critical for cell types, and validate modules using available experimental data. Inferring regulatory networks during myeloid differentiation from wild-type and Irf8 KO cells, we uncover functional contribution of Irf8 regulon activity and composition towards monocyte lineage. Our analysis provides an avenue to further extract and integrate the regulatory crosstalk from single-cell expression data.

Convergent molecular, cellular, and cortical neuroimaging signatures of major depressive disorder

Major depressive disorder emerges from the complex interactions of biological systems that span genes and molecules through cells, networks, and behavior. Establishing how neurobiological processes coalesce to contribute to depression requires a multiscale approach, encompassing measures of brain structure and function as well as genetic and cell-specific transcriptional data. Here, we examine anatomical (cortical thickness) and functional (functional variability, global brain connectivity) correlates of depression and negative affect across three population-imaging datasets: UK Biobank, Brain Genomics Superstruct Project, and Enhancing NeuroImaging through Meta Analysis (ENIGMA; combined n ≥ 23,723). Integrative analyses incorporate measures of cortical gene expression, postmortem patient transcriptional data, depression genome-wide association study (GWAS), and single-cell gene transcription. Neuroimaging correlates of depression and negative affect were consistent across three independent datasets. Linking ex vivo gene down-regulation with in vivo neuroimaging, we find that transcriptional correlates of depression imaging phenotypes track gene down-regulation in postmortem cortical samples of patients with depression. Integrated analysis of single-cell and Allen Human Brain Atlas expression data reveal somatostatin interneurons and astrocytes to be consistent cell associates of depression, through both in vivo imaging and ex vivo cortical gene dysregulation. Providing converging evidence for these observations, GWAS-derived polygenic risk for depression was enriched for genes expressed in interneurons, but not glia. Underscoring the translational potential of multiscale approaches, the transcriptional correlates of depression-linked brain function and structure were enriched for disorder-relevant molecular pathways. These findings bridge levels to connect specific genes, cell classes, and biological pathways to in vivo imaging correlates of depression.

Spectacle: An interactive resource for ocular single-cell RNA sequencing data analysis

Single-cell RNA sequencing has revolutionized ocular gene expression studies. This technology has enabled researchers to identify expression signatures for rare cell types and characterize how gene expression changes across biological conditions, such as topographic region or disease status. However, sharing single-cell RNA sequencing results remains a major obstacle, particular for individuals without a computational background. To address these limitations, we developed Spectacle, an interactive web-based resource for exploring previously published single-cell RNA sequencing data from ocular studies. Spectacle is powered by a locally developed R package, cellcuratoR, which utilizes the Shiny framework in R to generate interactive visualizations for single-cell expression data. Spectacle contains five pre-processed ocular single-cell RNA sequencing data sets and is accessible via the web at OcularGeneExpression.org/singlecell. With Spectacle, users can interactively identify which cell types express a gene of interest, detect transcriptomic subpopulations within a cell type, and perform highly flexible differential expression analyses. The freely-available Spectacle system reduces the bioinformatic barrier for interacting with rich single-cell RNA sequencing studies from ocular tissues, making it easy to quickly identify cell types that express a gene of interest.

Abstract 2156: IL-18 pathway agonism expands intratumoral PD1+ Tcf1+ stem-like CD8+ cells and their polyfunctional effector progeny to promote anti-tumor immunity

Abstract Recombinant cytokines were the first modern immunotherapies to produce durable cures in metastatic cancer, but their application has been hampered by only modest efficacy and limited tolerability. A major limitation to cytokine therapies is their lack of selectivity for the small fraction of lymphocytes that specifically recognize tumors. To identify cytokine pathways with greater selectively for anti-tumor lymphocytes, we analyzed single-cell RNA-seq expression data and flow cytometric profiles of tumor-infiltrating lymphocytes (TILs). Among detectable cytokine pathways, Interleukin-18 (IL-18) and its receptors IL-18R1 and IL-18RAP were markedly upregulated in activated and dysfunctional TILs, suggesting the potential of IL-18R agonism to promote anti-tumor immunity. However, recombinant IL-18 therapy has consistently failed to demonstrate anti-tumor efficacy in clinical trials. We hypothesized this may be due to the upregulation of an ultra-high affinity inhibitory decoy receptor of IL-18, IL-18BP. Indeed, IL-18BP is highly expressed in the tumor microenvironment, which we found by immunohistochemical staining and qPCR analysis of IL-18BP levels in several human cancers and syngeneic murine tumor models. Using directed evolution with yeast-surface display, we engineered “decoy-resistant” IL-18 (DR-18), a variant of IL-18 that maintains signaling potential but is impervious to binding and inhibition by IL-18BP. In multiple syngeneic tumor models including YUMMER1.7, MC38, and CT26, we found that recombinant WT IL-18 was ineffective by itself or in combination with anti-PD-1 treatment, similar to the clinical experience of rIL-18 in patients. However, in the same models, DR-18 showed robust monoagent activity that was commensurate or superior to anti-PD-1 antibodies, and it produced added therapeutic synergism when combined with anti-PD-1. Single-cell RNA-seq profiling and flow cytometry analysis of DR-18 treated tumors indicated that DR-18 treatment drives the development of poly-functional effector CD8+ T cells and NK cells. Importantly, DR-18 expands the pool of intratumoral Tcf1+ memory precursor PD1+ CD8+ T cells and decreases the prevalence of exhausted CD8+ T cells that express TOX. Mechanistically, we demonstrate that tumor antigen specific CD8+ T cells were sufficient to mediate tumor regression using LCMV epitope GP33 specific P14 CD8+ T cells in a B16-GP33 model. Together, these results highlight DR-18 is able to remodel the tumor-immune landscape to bias immune cells towards potent anti-tumor effector cells and away from the T exhausted lineage. Therefore, we conclude that the IL-18 pathway is a powerful target for immunotherapeutic intervention and implicate the secreted checkpoint IL-18BP as an obstacle to effective IL-18 immunotherapy. Citation Format: Ting Zhou, Orr-El Weizman, William Damsky, Meaghan McGeary, Marcus W. Bosenberg, Aaron M. Ring. IL-18 pathway agonism expands intratumoral PD1+ Tcf1+ stem-like CD8+ cells and their polyfunctional effector progeny to promote anti-tumor immunity [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2156.

Abstract 5910: Single-cell multiomic analysis of SNV, CNV, and protein expression

Abstract Recent advancements in precision medicine, while highly promising, presents a major technical challenge to researchers due to disease heterogeneity. The emergence of single-cell technologies has greatly refined the resolution in which sample diversity can be investigated, enhancing the efficiency of selecting appropriate molecular targets. Additionally, applying multiomic analysis on single cells would further improve the understanding of cell-to-cell heterogeneity by providing unique insights on cellular and genetic composition. Using a two-step droplet microfluidic technology, the Mission Bio Tapestri Platform enables multiplex-PCR based high-throughput targeted DNA sequencing in single cells to obtain single-nucleotide variation (SNV) and copy number variation (CNV) information. By leveraging this technology, a new workflow is developed to detect protein expression in addition to DNA genotype in the same single cells. In this approach, cells are labeled with a pool of oligonucleotide-conjugated antibodies prior to loading the cells into the Tapestri Instrument for targeted DNA analysis. Sequencing libraries are then prepared from both antibody oligonucleotides and the amplified DNA sequences, followed by identification of single-cell DNA genotypes and protein signatures from the sequencing readout. In a mixed population of four cell lines, single-cell SNV and CNV information from 127 targeted amplicons and the protein data from 10 antibodies independently classified the cells into appropriate clusters. This method has been successfully performed on clinical samples with myeloid malignancies. In an acute myeloid leukemia (AML) sample, combined single-cell SNV, CNV, and protein expression data illustrated the heterogeneity within the sample. The data clearly identified CD3+ T cells and CD19+ B cells without pathogenic SNVs and CNVs. CD34hiCD11blo and CD34loCD11bhi subpopulations were also identified within the cells carrying the same pathogenic SNVs and CNVs. We believe that this novel multiomic technology will enable new discoveries in the complex relationship between genotype and phenotype, leading to a better understanding of disease biology, and subsequently better design of diagnostics and therapies. Citation Format: Aik Ooi, Pedro Mendez, Dalia Dhingra, Nigel Beard, David Ruff. Single-cell multiomic analysis of SNV, CNV, and protein expression [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5910.

Abstract B69: Epigenomics and single-cell sequencing define a developmental hierarchy in Langerhans cell histiocytosis

Abstract Langerhans cell histiocytosis (LCH) is a rare neoplasm predominantly affecting children. It occupies a characteristic hybrid position between cancer and inflammatory disease, which makes it an attractive model for studying cancer development. Constitutive activation of the ERK signaling pathway is a common feature of LCH, but its pathogenesis remains unclear. To explore the molecular mechanisms underlying the pathophysiology of LCH and the mechanisms that might cause the characteristic clinical heterogeneity of this disease, we investigated cellular heterogeneity in primary LCH lesions. We used a multilayered approach employing immunohistochemistry, flow cytometry, single-cell RNA-sequencing, and ATAC-sequencing of biopsies obtained from different patients with LCH. Based on the single-cell RNA expression data, we identified different LCH cell populations within LCH lesions that display distinct pathway signatures, including a stem cell-like, proliferative state and different subsets with a more differentiated phenotype, including subsets similar to maturing antigen-presenting cells and a subset that could contribute to tissue destruction in LCH. Computational analysis discovered a hierarchy between these cells that suggests a directed developmental program in each tumor. We confirmed the presence of these distinct subsets using immunohistochemical analysis of different biopsies. Furthermore, using chromatin accessibility profiling in prospectively purified LCH cell subsets in combination with single-cell RNA-seq data and integrative bioinformatic analysis, we analyzed the transcription factors and gene regulatory networks that may underlie the observed developmental hierarchy in LCH lesions. This analysis reveals an intricate interplay of immune-regulatory (including JAK-STAT, AP-1, and NF-κB signaling) and developmental regulators (including epigenetic modifiers such as EP300 or KDMs) that may shape LCH cell development. Together, this study sketches a molecular portrait of LCH lesions and enables new, unprecedented insight into this disease. Moreover, it demonstrates the power of combining single-cell RNA-sequencing and epigenome profiling for dissecting complex developmental hierarchies and their regulatory underpinnings, and thus can serve as a template for the analysis of tumorigenesis beyond LCH. Citation Format: Florian Halbritter, Matthias Farlik, Raphaela Schwentner, Gunhild Jug, Nikolaus Fortelny, Ingrid Simonitsch-Klupp, Wolfgang Bauer, Christoph Bock, Caroline Hutter. Epigenomics and single-cell sequencing define a developmental hierarchy in Langerhans cell histiocytosis [abstract]. In: Proceedings of the AACR Special Conference on the Advances in Pediatric Cancer Research; 2019 Sep 17-20; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Res 2020;80(14 Suppl):Abstract nr B69.

Dynamics of the 4D genome during in vivo lineage specification and differentiation

Mammalian gene expression patterns are controlled by regulatory elements, which interact within topologically associating domains (TADs). The relationship between activation of regulatory elements, formation of structural chromatin interactions and gene expression during development is unclear. Here, we present Tiled-C, a low-input chromosome conformation capture (3C) technique. We use this approach to study chromatin architecture at high spatial and temporal resolution through in vivo mouse erythroid differentiation. Integrated analysis of chromatin accessibility and single-cell expression data shows that regulatory elements gradually become accessible within pre-existing TADs during early differentiation. This is followed by structural re-organization within the TAD and formation of specific contacts between enhancers and promoters. Our high-resolution data show that these enhancer-promoter interactions are not established prior to gene expression, but formed gradually during differentiation, concomitant with progressive upregulation of gene activity. Together, these results provide new insight into the close, interdependent relationship between chromatin architecture and gene regulation during development.

SelfE: Gene Selection via Self-Expression for Single-Cell Data

Single-cell RNA sequencing has been proved to be advantageous in discerning molecular heterogeneity in seemingly similar cells in a tissue. Due to the paucity of starting RNA, a large fraction of transcripts fail to amplify during the polymerase chain reaction cycle. This gets compounded by trivial biological noise such as variability in the cell cycle specific genes. As a result expression matrix obtained from a single-cell study is highly sparse with a large number of missing values. This hinders downstream analysis of single-cell expression data. It has been observed that feature engineering significantly improves the analysis outcomes. Feature extraction methods such as principal component analysis and zero-inflated factor analysis have been shown to be useful for subsequent steps of data analysis including clustering. However, too little or no visible efforts have been observed for developing feature selection techniques, which offer transparency for the analyst's consumption. We propose SelfE, a novel l2,0 -minimization algorithm that determines an optimal subset of feature vectors that preserves sub-space structures as observed in the data. We compared SelfE with the commonly used feature selection methods for single-cell expression data analysis.

Single-cell expression and Mendelian randomization analyses identify blood genes associated with lifespan and chronic diseases

The human lifespan is a heritable trait, which is intricately linked to the development of disorders. Here, we show that genetic associations for the parental lifespan are enriched in open chromatin of blood cells. By using blood expression quantitative trait loci (eQTL) derived from 31,684 samples, we identified for the lifespan 125 cis- and 559 trans-regulated expressed genes (eGenes) enriched in adaptive and innate responses. Analysis of blood single-cell expression data showed that eGenes were enriched in dendritic cells (DCs) and the modelling of cell ligand-receptor interactions predicted crosstalk between DCs and a cluster of monocytes with a signature of cytotoxicity. In two-sample Mendelian randomization (MR), we identified 16 blood cis-eGenes causally associated with the lifespan. In MR, the majority of cis-eGene-disorder association pairs had concordant effects with the lifespan. The present work underlined that the lifespan is linked with the immune response and identifies eGenes associated with the lifespan and disorders.

Integrating GWAS with bulk and single-cell RNA-sequencing reveals a role for LY86 in the anti-Candida host response.

Candida bloodstream infection, i.e. candidemia, is the most frequently encountered life-threatening fungal infection worldwide, with mortality rates up to almost 50%. In the majority of candidemia cases, Candida albicans is responsible. Worryingly, a global increase in the number of patients who are susceptible to infection (e.g. immunocompromised patients), has led to a rise in the incidence of candidemia in the last few decades. Therefore, a better understanding of the anti-Candida host response is essential to overcome this poor prognosis and to lower disease incidence. Here, we integrated genome-wide association studies with bulk and single-cell transcriptomic analyses of immune cells stimulated with Candida albicans to further our understanding of the anti-Candida host response. We show that differential expression analysis upon Candida stimulation in single-cell expression data can reveal the important cell types involved in the host response against Candida. This confirmed the known major role of monocytes, but more interestingly, also uncovered an important role for NK cells. Moreover, combining the power of bulk RNA-seq with the high resolution of single-cell RNA-seq data led to the identification of 27 Candida-response QTLs and revealed the cell types potentially involved herein. Integration of these response QTLs with a GWAS on candidemia susceptibility uncovered a potential new role for LY86 in candidemia susceptibility. Finally, experimental follow-up confirmed that LY86 knockdown results in reduced monocyte migration towards the chemokine MCP-1, thereby implying that this reduced migration may underlie the increased susceptibility to candidemia. Altogether, our integrative systems genetics approach identifies previously unknown mechanisms underlying the immune response to Candida infection.

SCelVis: exploratory single cell data analysis on the desktop and in the cloud.

BackgroundSingle cell omics technologies present unique opportunities for biomedical and life sciences from lab to clinic, but the high dimensional nature of such data poses challenges for computational analysis and interpretation. Furthermore, FAIR data management as well as data privacy and security become crucial when working with clinical data, especially in cross-institutional and translational settings. Existing solutions are either bound to the desktop of one researcher or come with dependencies on vendor-specific technology for cloud storage or user authentication.ResultsTo facilitate analysis and interpretation of single-cell data by users without bioinformatics expertise, we present SCelVis, a flexible, interactive and user-friendly app for web-based visualization of pre-processed single-cell data. Users can survey multiple interactive visualizations of their single cell expression data and cell annotation, define cell groups by filtering or manual selection and perform differential gene expression, and download raw or processed data for further offline analysis. SCelVis can be run both on the desktop and cloud systems, accepts input from local and various remote sources using standard and open protocols, and allows for hosting data in the cloud and locally. We test and validate our visualization using publicly available scRNA-seq data.MethodsSCelVis is implemented in Python using Dash by Plotly. It is available as a standalone application as a Python package, via Conda/Bioconda and as a Docker image. All components are available as open source under the permissive MIT license and are based on open standards and interfaces, enabling further development and integration with third party pipelines and analysis components. The GitHub repository is https://github.com/bihealth/scelvis.

Inferring TF activation order in time series scRNA-Seq studies.

Methods for the analysis of time series single cell expression data (scRNA-Seq) either do not utilize information about transcription factors (TFs) and their targets or only study these as a post-processing step. Using such information can both, improve the accuracy of the reconstructed model and cell assignments, while at the same time provide information on how and when the process is regulated. We developed the Continuous-State Hidden Markov Models TF (CSHMM-TF) method which integrates probabilistic modeling of scRNA-Seq data with the ability to assign TFs to specific activation points in the model. TFs are assumed to influence the emission probabilities for cells assigned to later time points allowing us to identify not just the TFs controlling each path but also their order of activation. We tested CSHMM-TF on several mouse and human datasets. As we show, the method was able to identify known and novel TFs for all processes, assigned time of activation agrees with both expression information and prior knowledge and combinatorial predictions are supported by known interactions. We also show that CSHMM-TF improves upon prior methods that do not utilize TF-gene interaction.

Dissecting transcriptomic signatures of neuronal differentiation and maturation using iPSCs

Human induced pluripotent stem cells (hiPSCs) are a powerful model of neural differentiation and maturation. We present a hiPSC transcriptomics resource on corticogenesis from 5 iPSC donor and 13 subclonal lines across 9 time points over 5 broad conditions: self-renewal, early neuronal differentiation, neural precursor cells (NPCs), assembled rosettes, and differentiated neuronal cells. We identify widespread changes in the expression of both individual features and global patterns of transcription. We next demonstrate that co-culturing human NPCs with rodent astrocytes results in mutually synergistic maturation, and that cell type-specific expression data can be extracted using only sequencing read alignments without cell sorting. We lastly adapt a previously generated RNA deconvolution approach to single-cell expression data to estimate the relative neuronal maturity of iPSC-derived neuronal cultures and human brain tissue. Using many public datasets, we demonstrate neuronal cultures are maturationally heterogeneous but contain subsets of neurons more mature than previously observed.

The Mnemiopsis Genome Project Portal: integrating new gene expression resources and improving data visualization.

Following the completion of the genome sequencing and gene prediction of Mnemiopsis leidyi, a lobate ctenophore that is native to the coastal waters of the western Atlantic Ocean, we developed and implemented the Mnemiopsis Genome Project Portal (MGP Portal), a comprehensive Web-based data portal for navigating the genome sequence and gene annotations. In the years following the first release of the MGP Portal, it has become evident that the inclusion of data from significant published studies on Mnemiopsis has been critical to its adoption as the centralized resource for this emerging model organism. With this most recent update, the Portal has significantly expanded to include in situ images, temporal developmental expression profiles and single-cell expression data. Recent enhancements also include implementations of an updated BLAST interface, new graphical visualization tools and updates to gene pages that integrate all new data types. Database URL: https://research.nhgri.nih.gov/mnemiopsis/.

Head and neck squamous cancer progression is marked by CLIC4 attenuation in tumor epithelium and reciprocal stromal upregulation of miR-142-3p, a novel post-transcriptional regulator of CLIC4.

Chloride intracellular channel 4 (CLIC4) is a tumor suppressor implicated in processes including growth arrest, differentiation, and apoptosis. CLIC4 protein expression is diminished in the tumor parenchyma during progression in squamous cell carcinoma (SCC) and other neoplasms, but the underlying mechanisms have not been identified. Data from The Cancer Genome Atlas suggest this is not driven by genomic alterations. However, screening and functional assays identified miR-142-3p as a regulator of CLIC4. CLIC4 and miR-142-3p expression are inversely correlated in head and neck (HN) SCC and cervical SCC, particularly in advanced stage cancers. In situ localization revealed that stromal immune cells, not tumor cells, are the predominant source of miR-142-3p in HNSCC. Furthermore, HNSCC single-cell expression data demonstrated that CLIC4 is lower in tumor epithelial cells than in stromal fibroblasts and endothelial cells. Tumor-specific downregulation of CLIC4 was confirmed in an SCC xenograft model concurrent with immune cell infiltration and miR-142-3p upregulation. These findings provide the first evidence of CLIC4 regulation by miRNA. Furthermore, the distinct localization of CLIC4 and miR-142-3p within the HNSCC tumor milieu highlight the limitations of bulk tumor analysis and provide critical considerations for both future mechanistic studies and use of miR-142-3p as a HNSCC biomarker.

Deep learning for inferring gene relationships from single-cell expression data

Several methods were developed to mine gene-gene relationships from expression data. Examples include correlation and mutual information methods for coexpression analysis, clustering and undirected graphical models for functional assignments, and directed graphical models for pathway reconstruction. Using an encoding for gene expression data, followed by deep neural networks analysis, we present a framework that can successfully address all of these diverse tasks. We show that our method, convolutional neural network for coexpression (CNNC), improves upon prior methods in tasks ranging from predicting transcription factor targets to identifying disease-related genes to causality inference. CNNC's encoding provides insights about some of the decisions it makes and their biological basis. CNNC is flexible and can easily be extended to integrate additional types of genomics data, leading to further improvements in its performance.

Reconstruction of Cell-type-Specific Interactomes at Single-Cell Resolution.

The human interactome is instrumental in the systems-level study of the cell and the contextualization of disease-associated gene perturbations. However, reference organismal interactomes do not capture the cell-type-specific context in which proteins and modules preferentially act. Here, we introduce SCINET, a computational framework that reconstructs an ensemble of cell-type-specific interactomes by integrating a global, context-independent reference interactome with a single-cell gene-expression profile. SCINET addresses technical challenges of single-cell data by robustly imputing, transforming, and normalizing the initially noisy and sparse expression of data. Inferred cell-level gene interaction probabilities and group-level interaction strengths define cell-type-specific interactomes. We use SCINET to reconstruct and analyze interactomes of the major human brain and immune cell types, revealing specificity and modularity of perturbations associated with neurodegenerative, neuropsychiatric, and autoimmune disorders. We report cell-type interactomes for brain and immune cell types, together with the SCINET package.

Improved dropClust R package with integrative analysis support for scRNA-seq data.

DropClust leverages Locality Sensitive Hashing (LSH) to speed up clustering of large scale single cell expression data. Here we present the improved dropClust, a complete R package that is, fast, interoperable and minimally resource intensive. The new dropClust features a novel batch effect removal algorithm that allows integrative analysis of single cell RNA-seq (scRNA-seq) datasets. dropClust is freely available at https://github.com/debsin/dropClust as an R package. A lightweight online version of the dropClust is available at https://debsinha.shinyapps.io/dropClust/. Supplementary data are available at Bioinformatics online.

UCSC Genome Browser enters 20th year.

The University of California Santa Cruz Genome Browser website (https://genome.ucsc.edu) enters its 20th year of providing high-quality genomics data visualization and genome annotations to the research community. In the past year, we have added a new option to our web BLAT tool that allows search against all genomes, a single-cell expression viewer (https://cells.ucsc.edu), a ‘lollipop’ plot display mode for high-density variation data, a RESTful API for data extraction and a custom-track backup feature. New datasets include Tabula Muris single-cell expression data, GeneHancer regulatory annotations, The Cancer Genome Atlas Pan-Cancer variants, Genome Reference Consortium Patch sequences, new ENCODE transcription factor binding site peaks and clusters, the Database of Genomic Variants Gold Standard Variants, Genomenon Mastermind variants and three new multi-species alignment tracks.