Recent technological advances in single cell capture and nano-scale reactions have led to a major revolution in single cell transcriptomics 1,2,3 . Single cell datasets are analyzed using computational and statistical frameworks that enable feature (gene) selection, dimensionality reduction, clustering and differential gene expression. Multiple software packages exist that allow researchers well versed in computational analysis to perform this analysis [4][5][6] . However, identifying the exact parameters required for cell type identification is an iterative process greatly improved when informed by biology. In addition, interactive exploration of single cell datasets incorporating a biologist's knowledge greatly improves data interpretation, yet often such experts do not have big data handling skills.Advances in web application frameworks and visualization methods for dense datasets facilitate the development of interactive applications to allow easy and intuitive exploration of single cell data. Here, we introduce an R Shiny 7 web application, CellView, that allows knowledge-based and hypothesis-driven exploration of processed single cell transcriptomic data. The input into CellView is an R dataset (. . CC-BY-NC-ND 4.0 International license It is made available under a was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint (which . http://dx.doi.org/10.1101/123810 doi: bioRxiv preprint first posted online with three pre-computed data frames containing expression, clustering, and gene symbol information.This file is agnostic of upstream computational approaches providing flexibility in algorithms used to calculate these data frames. This .Rds file can be shared with the end user, eliminating the need for hosting datasets, thereby decreasing the size of a virtual machine or cloud instance required to host and use CellView. Multiple tabs allow for easy access to the data and visualization of gene expression across and within clusters, aiding cell type identification.To illustrate the utility and power of CellView, we generated and analyzed single cell transcriptome data from peripheral blood mononuclear cells (PBMCs) using the 10X Genomics Chromium 8 . As defined by the CellRanger 8 pipeline, this data consisted of 6,554 single cells sequenced to 90.1% saturation with, on average, 824 genes and 2,077 molecules detected per cell. Dimensionality reduction using tSNE 9 was applied to genes selected by normalized dispersion, and with clustering by DBSCAN 10 . CellView automatically determines cluster numbers, updates the user interface, and renders a 3D scatter plot displaying cells clustered in tSNE space (Fig 1b) (Fig 1c), a marker of B-cells, and CD3D, a marker of T-cells (Fig 1d), provide representative views of the 'Explore' tab.The 'Co-expression' tab enables the generation of heatmaps to visualize expression of multiple genes either across all clusters, in the 'AllClusters' sub-menu, or on selected cells wi...

Genetic and environmental factors both contribute to islet dysfunction and failure, resulting in type 2 diabetes (T2D). The islet epigenome integrates these cues and can be remodeled by genetic and environmental variation. However, our knowledge of how genetic variants and T2D disease state alter human islet chromatin landscape and cis-regulatory element (RE) use is lacking. To fill this gap, we profiled and analyzed human islet chromatin accessibility maps from 19 genotyped individuals (5 with T2D) using ATAC-seq technology. Chromatin accessibility quantitative trait locus (caQTL) analyses identified 3001 sequence variants (FDR<10%) altering putative cis-RE use/activity. Islet caQTL were significantly and specifically enriched in islet stretch enhancers and islet-specific transcription factor binding motifs, such as FOXA2, NKX6.1, RFX5/6 and PDX1. Importantly, these analyses identified putative functional single nucleotide variants (SNVs) in 13 T2D-associated GWAS loci, including those previously associated with altered ZMIZ1, MTNR1B, RNF6, and ADCY5 islet expression, and linked the risk alleles to increased (n=8) or decreased (n=5) islet chromatin accessibility. Luciferase reporter assays confirmed allelic differences in cis-RE activity for 5/9 caQTL sequences tested, including a T2D-associated SNV in the IL20RA locus. Comparison of T2D and non-diabetic islets revealed 1882 open chromatin sites exhibiting T2D-associated chromatin accessibility changes (FDR<10%). Together, this study provides new insights into genetic variant and T2D disease state effects on islet cis-RE use and serves as an important resource to identify putative functional variants in T2D-and islet dysfunction-associated GWAS loci and link their risk allele to in vivo loss or gain of chromatin accessibility.

Transcription factor (TF) footprinting uncovers putative protein-DNA binding via combined analyses of chromatin accessibility patterns and their underlying TF sequence motifs. TF footprints are frequently used to identify TFs that regulate activities of cell/condition-specific genomic regions (target loci) in comparison to control regions (background loci) using standard enrichment tests. However, there is a strong association between the chromatin accessibility level and the GC content of a locus and the number and types of TF footprints that can be detected at this site. Traditional enrichment tests (e.g., hypergeometric) do not account for this bias and inflate false positive associations. Therefore, we developed a novel method, Bias-free Footprint Enrichment Test (BiFET), that corrects for the biases arising from the differences in chromatin accessibility levels and GC contents between target and background loci in footprint enrichment analyses. We applied BiFET on TF footprint calls obtained from human EndoC-βH1 ATAC-seq samples using three different algorithms (CENTIPEDE, HINT-BC, and PIQ) and showed BiFET's ability to increase power and reduce false positive rate when compared to hypergeometric test. Furthermore, we used BiFET to study TF footprints from human PBMC and pancreatic islet ATAC-seq samples to show its utility to identify putative TFs associated with cell-typespecific loci.

SUMMARYEndoC-βH1 is emerging as a critical human beta cell model to study the genetic and environmental etiologies of beta cell function, especially in the context of diabetes. Comprehensive knowledge of its molecular landscape is lacking yet required to fully take advantage of this model. Here, we report extensive chromosomal (spectral karyotyping), genetic (genotyping), epigenetic (ChIP-seq, ATAC-seq), chromatin interaction (Hi-C, Pol2 ChIA-PET), and transcriptomic (RNA-seq, miRNA-seq) maps of this cell model. Integrated analyses of these maps define known (e.g., PDX1, ISL1) and putative (e.g., PCSK1, mir-375) beta cell-specific chromatin interactions and transcriptional cis-regulatory networks, and identify allelic effects on cis-regulatory element use and expression.Importantly, comparative analyses with maps generated in primary human islets/beta cells indicate substantial preservation of chromatin looping, but also highlight chromosomal heterogeneity and fetal genomic signatures in EndoC-βH1. Together, these maps, and an interactive web application we have created for their exploration, provide important tools for the broad community in the design and success of experiments to probe and manipulate the genetic programs governing beta cell identity and (dys)function in diabetes.