Characterizing epigenetic heterogeneity at the cellular level is a critical problem in the modern genomics era. Assays such as single cell ATAC-seq (scATAC-seq) offer an opportunity to interrogate cellular level epigenetic heterogeneity through patterns of variability in open chromatin. However, these assays exhibit technical variability that complicates clear classification and cell type identification in heterogeneous populations. We present scABC, an R package for the unsupervised clustering of single-cell epigenetic data, to classify scATAC-seq data and discover regions of open chromatin specific to cell identity.

Pooled CRISPR screens allow researchers to interrogate genetic causes of complex phenotypes at the genome-wide scale and promise higher specificity and sensitivity compared to competing technologies. Unfortunately, two problems exist, particularly for CRISPRi/a screens: variability in guide efficiency and large rare off-target effects. We present a method, CRISPhieRmix, that resolves these issues by using a hierarchical mixture model with a broad-tailed null distribution. We show that CRISPhieRmix allows for more accurate and powerful inferences in large-scale pooled CRISPRi/a screens. We discuss key issues in the analysis and design of screens, particularly the number of guides needed for faithful full discovery.Electronic supplementary materialThe online version of this article (10.1186/s13059-018-1538-6) contains supplementary material, which is available to authorized users.

Characterizing epigenetic heterogeneity at the cellular level is a critical problem in the modern genomics era. Assays such as single cell ATAC-seq (scATAC-seq) offer an opportunity to interrogate cellular level epigenetic heterogeneity through patterns of variability in open chromatin. However, these assays exhibit technical variability that complicates clear classification and cell type identification in heterogeneous populations. We present scABC, an R package for the unsupervised clustering of single cell epigenetic data, to classify scATAC-seq data and discover regions of open chromatin specific to cell identity.Recent advances in single cell technologies such as scATAC-seq 1, 2 and scChIP-seq 3 have expanded our understanding of epigenetic heterogeneity at the single cell level. However, datasets arising from such technologies are difficult to analyze due to the inherent sparsity. In particular, consider scATAC-seq, designed to interrogate open chromatin in single cells. Open sites in a diploid genome have at most 2 chances to be captured through the assay and only a few thousand distinct reads are generated per cells, resulting in a very low chance that a particular site is captured by the assay. Consequently, it is difficult to determine whether a region is absent in an individual cell due to the lack of openness or due to the sparse nature of data. This creates a challenging task in delineating distinct subpopulations, as only a few genomic regions will have overlapping reads in a large number of cells. To avoid this issue, many studies perform FACS sorting to identify subpopulations, followed by bulk sequencing to determine genomic regions of interest and guide the single cell analysis. If the population is unknown or marker genes are unavailable, then sub-population specific analysis becomes impractical with these techniques.To combat these challenges and allow for the de novo classification of individual cells by their epigenetic signatures, we present a statistical method for the unsupervised clustering of scATAC-seq data, named scABC (single cell Accessibility Based Clustering). In contrast to previous works 2, 4 that demand predefined accessible chromatin sites, our procedure relies solely on the patterns of read counts within genomic regions to cluster cells. It requires two inputs: the individual single cell mapped read files and the full set of called peaks (which can be obtained from the union of all of the individual cells without the need for additional experiments). We apply our method to publicly available scATAC-seq data 1, 2, 4 as well as a true biological mixture to show that our approach can cluster cells with similar epigenetic patterns and identify accessible regions specific to each cluster. We further demonstrate that the cluster specific accessible regions determined by scABC have functional meaning and are capable of determining cellular identity. In particular, we show that these cluster specific accessible regions are enriched for transcription factor motifs known to be...

Dimension reduction methods are commonly applied to highthroughput biological datasets. However, the results can be hindered by confounding factors, either biological or technical in origin. In this study, we extend principal component analysis (PCA) to propose AC-PCA for simultaneous dimension reduction and adjustment for confounding (AC) variation. We show that AC-PCA can adjust for (i) variations across individual donors present in a human brain exon array dataset and (ii) variations of different species in a model organism ENCODE RNA sequencing dataset. Our approach is able to recover the anatomical structure of neocortical regions and to capture the shared variation among species during embryonic development. For gene selection purposes, we extend AC-PCA with sparsity constraints and propose and implement an efficient algorithm. The methods developed in this paper can also be applied to more general settings. The R package and MATLAB source code are available at https://github.com/linzx06/AC-PCA. dimension reduction | confounding variation | transcriptome D imension reduction methods, such as multidimensional scaling (MDS) and principal component analysis (PCA), are commonly applied in high-throughput biological datasets to visualize data in a low-dimensional space, identify dominant patterns, and extract relevant features (1-6). MDS aims to place each sample in a lower-dimensional space such that the betweensample distances are preserved as much as possible (7). PCA seeks the linear combinations of the original variables such that the derived variables capture maximal variance (8). One advantage of PCA is that the principal components (PCs) are more interpretable by checking the loadings of the variables.Confounding factors, either biological or technical in origin, are commonly observed in high-throughput biological experiments. Various methods have been proposed to estimate the confounding variation, for example, regression models on known confounding factors (9) and factor models and surrogate vector analysis for unobserved confounding factors (10-15). However, limited work has been done in the context of dimension reduction. Confounding variation can affect PC-based visualization of the data points because it may obscure the desired biological variation, and it can also affect the loading of the variables in the PCs.Here we extend PCA to propose AC-PCA for simultaneous dimension reduction and adjustment for confounding (AC) variation. We introduce a class of penalty functions in PCA, which encourages the PCs to be invariant to the confounding variation. We demonstrate the performance of AC-PCA through its application to a human brain development exon array dataset (4), a model organism ENCODE (modENCODE) RNA sequencing (RNA-Seq) dataset (16, 17), and simulated data. We also implemented AC-PCA with sparsity constraints to enable variable/gene selection and better interpretation of the PCs.Results AC-PCA in a General Form. Let X denote the N × p data matrix, where N is the number of observations and p is...