Using CellWalker, we mapped cell types derived from scRNA-seq data to a large set of scATAC-seq data. developing mouse cerebral cortex is definitely available from GEO accession quantity “type”:”entrez-geo”,”attrs”:”text”:”GSE126074″,”term_id”:”126074″GSE126074 . 10x Solitary Cell Multiome ATAC + Gene Exp chip data for human being healthy mind tissue is definitely available from 10x Genomics . Multi-sample mid-gestation human being telencephalon scATAC-seq data is definitely available from synapse.org id syn21392931. GZ TADS were previously generated  based on HiC data taken from Won et al.  and are available from those authors upon request. Regionally microdissected developmental mind pREs are available from GEO accession quantity “type”:”entrez-geo”,”attrs”:”text”:”GSE149268″,”term_id”:”149268″GSE149268 . GWAS data was downloaded from your NHGRI-EBI GWAS catalog . Disease gene units were downloaded from Werling et al. . CellWalker code and simulated data is definitely available under the GNU GPL-2.0 License at https://github.com/PollardLab/CellWalker (DOI: 10.5281/zenodo.4456095) along with a readme demonstrating how the method can be applied to sample data . Abstract Single-cell and bulk genomics assays have complementary advantages and weaknesses, and only neither strategy can fully capture regulatory elements across the diversity of cells in complex cells. We present CellWalker, a method that integrates single-cell open chromatin (scATAC-seq) data with gene manifestation (RNA-seq) and additional data types using a network model that simultaneously enhances cell labeling in noisy scATAC-seq and annotates cell type-specific regulatory elements in bulk data. We demonstrate CellWalkers robustness to sparse annotations and noise using simulations and combined RNA-seq and ATAC-seq in individual cells. We then apply CellWalker to the developing mind. We determine cells transitioning between transcriptional claims, resolve regulatory elements to cell types, and observe that autism and additional neurological traits can be mapped to specific cell types through their regulatory elements. Supplementary Information The online version consists of supplementary material available at 10.1186/s13059-021-02279-1. Background Gene regulatory elements are crucial determinants of cells and cell type-specific gene manifestation [1, 2]. Annotation of putative enhancers, promoters, and insulators offers rapidly improved through large-scale projects such as ENCODE , PsychENCODE , B2B , and Roadmap Epigenomics . However, both predictions and validations of regulatory elements have been made mainly in cell lines or bulk tissues lacking anatomical and cellular specificity . Bulk measurements miss regulatory elements specific to one cell type, especially minority ones . This lack of specificity limits our ability to determine how genes are differentially controlled across cell types and to discover the molecular and cellular mechanisms through which regulatory variants impact phenotypes. Single-cell genomics is an fascinating avenue to overcoming limitations of bulk cells studies [8, 9]. However, these technologies struggle with low-resolution measurements featuring high rates of dropout and few reads per cell [8, 9]. Many methods have been developed to address these problems in single-cell manifestation data (scRNA-seq) [8, 9]. However, these strategies generally fail on scATAC-seq data because there are fewer reads per cell, and the portion of the Simvastatin genome becoming Simvastatin sequenced is typically much larger than the transcriptome . Consequently, scATAC-seq offers much lower protection and worse signal-to-noise than scRNA-seq. Several scATAC-seq analysis methods have been developed to increase the number of helpful reads used per cell. These include Cicero , which aggregates reads from peaks that are co-accessible with gene promoters to emulate gene-focused scRNA-seq data, and SnapATAC , which computes cell similarity based on genome-wide binning of reads. Additional methods search for helpful reads based on known or expected regulatory areas [13, 14]. However, these methods often miss rare but known cell types . Other methods attempt to detect cell types in scATAC-seq data by either mapping the data into the same low-dimensional space as scRNA-seq data or by labeling cells in scATAC-seq to known cell-type manifestation profiles [15, 16]. While these provide a encouraging avenue towards adding labels to clusters of cells observed in scATAC-seq data, they do not help to increase the resolution of CD52 cell type detection. We present CellWalker, a generalizable network model that enhances the resolution of cell populations in scATAC-seq data, Simvastatin decides cell label similarity, and produces cell type-specific labels for bulk data by integrating info from scRNA-seq and a variety of bulk data. These Simvastatin labels can be generated concurrently from your same cells, but could also be from cell lines, sorted cells, or related cells. Our method goes beyond co-embedding or directly labeling cells with this prior.