Release Updates

  • 08/14/2018 adds UCSC Trackhub and BigWigs section which provides documentation on new bigWig files and a UCSC Trackhub for visualization.
  • 08/10/2018 adds another metadata file option, cell_metadata.tissue_freq_filtered.txt, which removes cells in each tissue belonging to a cell_label that accounts for less than 0.5% of the cells in that tissue. These very low frequency labels are often not cell types expected their respective tissues and could be due to slight imperfections in clustering, for example.
  • 08/06/2018:
    • Switches any references to Colon to LargeIntestine and Intestine to SmallIntestine. This impacts any file that lists a tissue name, but all files were previously consistent with one another and usable.
    • Updates specificity score section including added tutorial input files and minor updates to existing gene-level specificity scores.
    • Adds a BAM Files for BAM downloads.
  • 08/02/2018: Initial release.

ATAC Matrices

Similar to sc-RNA-seq, sci-ATAC-seq data is typically analyzed in sparse peak (row) by cell (column) matrices. The first set we provide are binarized counts. The second set has rare peaks filtered out and is then normalized with TFIDF to allow for input to PCA/TSNE, for example. Note that only cells in our final QC filtered set are included.

See tutorials for examples of how to read these formats into R or python along with documentation on lots of other downstream analysis.

Matrix Formats

In general, we provide two formats for all matrices:

File Type Description
.mtx.gz

Same as format generated by 10X Genomics cellranger pipeline (matrix market format). .mtx files are provided with .txt files containing peak and cell IDs that correspond to the rows and columns of the matrix, respectively.
.rds

RDS format that can be read into R directly with readRDS function.
Name Last modified Size Description
atac_matrix.binary.qc_filtered.mtx.gz 08/02/18 1.1GB Binarized peak by cell matrix in matrix market format.
atac_matrix.binary.qc_filtered.cells.txt 08/02/18 2.9MB Cell IDs (columns) of binarized matrix.
atac_matrix.binary.qc_filtered.peaks.txt 08/02/18 10.0MB Peak IDs (rows) of binarized matrix.
atac_matrix.binary.qc_filtered.rds 08/02/18 1.0GB Binarized peak by cell matrix in RDS format.
atac_matrix.tfidf.qc_filtered.mtx.gz 08/02/18 3.4GB TFIDF normalized peak by cell matrix in matrix market format.
atac_matrix.tfidf.qc_filtered.cells.txt 08/02/18 2.9MB Cell IDs (columns) of TFIDF matrix.
atac_matrix.tfidf.qc_filtered.peaks.txt 08/02/18 3.8MB Peak IDs (rows) of TFIDF matrix.
atac_matrix.tfidf.qc_filtered.rds 08/02/18 5.4GB TFIDF normalized peak by cell matrix in RDS format.

Activity Score Matrices

We also report "gene activity scores", where a single number is calculated based on a weighted combination of proximal and distal sites for each gene (see manuscript for details; both quantitative and binarized calculations provided below). Unlike the ATAC matrices above, these are in gene (row) by cell (column) format.

Important: Size Factor Normalization

Quantitative scores provided here are not normalized by size factors, so you may apply size factor normalization to these values if needed.

Name Last modified Size Description
activity_scores.quantitative.mtx.gz 08/02/18 754.1MB Quantitative gene activity score matrix in matrix market format.
activity_scores.quantitative.cells.txt 08/02/18 2.9MB Cell IDs (columns) of quantitative gene activity score matrix.
activity_scores.quantitative.genes.txt 08/02/18 150.4KB Gene names (common names; columns) of quantitative gene activity score matrix.
activity_scores.quantitative.rds 08/02/18 664.0MB Quantitative gene activity score matrix in RDS format.
activity_scores.binarized.mtx.gz 08/02/18 218.1MB Binarized gene activity score matrix in matrix market format.
activity_scores.binarized.cells.txt 08/02/18 2.9MB Cell IDs (columns) of binarized gene activity score matrix.
activity_scores.binarized.genes.txt 08/02/18 150.4KB Gene names (common names; columns) of binarized gene activity score matrix.
activity_scores.binarized.rds 08/02/18 151.9MB Binarized gene activity score matrix in RDS format.

Metadata

For all cells and peaks used in our QC filtered set, we report tables of metadata including information about tissue source, cell type assignment, TSNE coordinates, cluster assignments, etc. for cells, and intersections with genes (TSS only) for peaks.

Name Last modified Size Description
cell_metadata.txt 08/06/18 13.3MB Metadata for cells in TSV format, including several features such as TSNE coordinates, cluster assignments, and cell type assignments.
  • cell: cell barcode (combined and corrected)
  • tissue: the tissue that this cell originated from
  • tissue_replicate: same as tissue, but each replicate has a unique id
  • cluster: cluster assignment in initial t-SNE
  • subset_cluster: cluster assignment in iterative t-SNE space
  • tsne_1: t-SNE1 coordinate in initial t-SNE
  • tsne_2: t-SNE2 coordinate in initial t-SNE
  • subset_tsne1: t-SNE1 coordinate in iterative t-SNE
  • subset_tsne2: t-SNE2 coordinate in iterative t-SNE
  • id: combined ID for major + iterative cluster assignment
  • cell_label: assigned cell type
cell_metadata.tissue_freq_filtered.txt 08/10/18 13.0MB Same as cell_metadata.txt, but removes cells in each tissue belonging to a cell_label that accounts for less than 0.5% of the cells in that tissue. These very low frequency labels are often not cell types expected their respective tissues and could be due to slight imperfections in clustering, for example. Provided matrices would need to be subsetted to match this set of cells if using this metadata.
peak_promoter_intersections.txt 08/02/18 6.9MB Metadata for peaks-intersected TSS pairs in TSV format
  • peak_id: ID of peak (chr_start_end)
  • peak_chr: chromosome for peak location
  • peak_start: start coordinate for peak location
  • peak_end: end coordinate for peak location
  • ensembl_id: ensembl ID for intersected gene TSS
  • gene_short_name: common name for intersected gene TSS
  • ensembl_transcript_id: ensembl ID for intersected transcript TSS
  • biotype: biotype for intersected gene TSS
  • strand: strand for intersected gene TSS
cell_type_assignments.xlsx 08/06/18 134.1KB Excel document with three tabs expected cell types, cell type markers, and Cell type assignments that contain a pairs of tissues and expected cell types, a list of positive markers for each cell type, and the table of cell type assignments with extra details about assignment criteria when applicable, respectively. This is meant to document justification for cell type assignments provided in cell_metadata.txt above.
expected cell types
  • tissue: the tissue in the cell type tissue pair
  • expected_cell_type: the cell type in the tissue cell type pair
cell type markers
  • Cell type: the cell type from above table
  • subset_cluster: a list of positive markers for that cell type
Cell type assignments
  • cluster: cluster assignment in initial t-SNE
  • subset_cluster: cluster assignment in iterative t-SNE space
  • pipeline_cell_type: broad automatic assignment made by classifier (see manuscript).
  • manual_cell_type: a broad assigned cell type where applicable
  • cell_label: assigned cell type (same as in cell metadata file)
  • notes: extra notes about assignment criteria where applicable

Differential Accessibility

We report results from differential accessibility (DA) tests performed between each cluster of cells (final iterative clusters) and a set of 2K sampled cells. See manuscript for details. These are reported using both the binarized ATAC matrix (contains all peaks) and the binarized gene activity score matrix (contains a single entry per gene).

DA Test Format

The following columns are provided in each DA test file. Note that files contain one entry per combination of cluster, subset_cluster, and peak/gene_short_name.

Column Description
status status of successful test completion reported by monocle (OK for all in this case)
family the distribution family used by monocle (binomialff in this case)
pval Uncorrected P-value returned by monocle.
beta Beta derived from the model returned by monocle. The coefficient of the term noting cluster membership. Negative values indicate less accessibility within the specified cluster than the 2K sampled set.
qval Q-value returned by monocle corrected for all tests performed for the specified cluster.
peak/gene_short_name For ATAC matrix this is the peak ID of the peak being tested (chr_start_stop; header peak). For activity score matrix, this is the common gene name of the gene being tested (header gene).
cluster Cluster assignment in initial t-SNE.
subset_cluster Cluster assignment in iterative t-SNE space.
Name Last modified Size Description
atac_matrix.binary.da_results.txt 08/02/18 2.4GB DA test results from ATAC matrix in TSV format.
atac_matrix.binary.da_results.sig_open.txt 08/02/18 118.0MB DA test results from ATAC matrix for peaks with significant positive betas in TSV format.
activity_scores.binarized.da_results.txt 08/02/18 83.5MB DA test results from binarized gene activity score matrix in TSV format.
activity_scores.binarized.da_results.sig_open.txt 08/02/18 14.2MB DA test results from binarized gene activity score matrix for genes with significant positive betas in TSV format.

Specificity Scores

We report specificity scores to rank elements by their restricted accessiblity within each of our clusters (see manuscript for details). Only sites that had significant specificity scores at our empirically determined false discovery rate threshold are reported.

These are provided in both Excel format and text format to allow browsing of results.

Name Last modified Size Description
peak_specificity_scores.long_format.txt.gz 08/02/2018 568.7MB Long format file with specificity scores for all sites across all clusters.
  • specificity_score: specificity of accessibility for this peak/cluster combination (see methods)
  • site: ID of peak (chr_start_end)
  • cluster: cluster assignment in initial t-SNE
  • subset_cluster: cluster assignment in iterative t-SNE space
  • cluster_name: an ID combining cluster and subset_cluster into one string.
peak_specificity_scores.long_format.rds 08/02/2018 496.2MB Same as above in RDS format.
gene_specificity_scores.long_format.txt.gz 08/06/2018 17.9MB Same as peak_specificity_scores.long_format.txt.gz, but gene-level specificity calculated from activity scores. site column replaced with gene_short_name.
gene_specificity_scores.long_format.rds 08/06/2018 12.6MB Same as above in RDS format.
peak_specificity_scores_sigsites.long_format.txt.gz 08/02/2018 3.8MB Same as peak_specificity_scores.long_format.txt.gz, but filtered to significantly specific sites (see methods).
peak_specificity_scores_sigsites.long_format.rds 08/02/2018 4.5MB Same as above in RDS format.
gene_specificity_scores_sigsites.long_format.txt.gz 08/06/2018 626.1KB Same as gene_specificity_scores.long_format.txt.gz, but filtered to significantly specific genes (see methods).
gene_specificity_scores_sigsites.long_format.rds 08/06/2018 891.0KB Same as above in RDS format.
specificity_data.tar.gz 08/06/2018 166.4MB Data files necessary to complete the tutorial on specificity scoring.

Comparisons to scRNA-seq Datasets

In our manuscript we also examine similarity between our sci-ATAC-seq dataset and several sc-RNA-seq datasets. We do this using a cluster-level correlation-based approach and a cell-by-cell KNN-based approach. Both used activity scores as calculated by Cicero as input (see above). Here we provide the cluster-level correlations for each dataset/tissue and the cell-by-cell KNN results for each dataset we have compared to.

Name Last modified Size Description
knn_results.txt 08/06/2018 8.0MB KNN results listing assignments of each cell in our study in TSV format. Some cells will be missing and designated as "NA" due to the filtering of very low frequency cell type assignments within each tissue and thresholds on the number of non-zero peaks per cell as described in methods.
  • cell: cell barcode (combined and corrected)
  • tissue: the tissue in the cell type tissue pair
  • cell_label: assigned cell type in our study
  • assigned_cell_label_microwell: Label assigned from MCA dataset for applicable cells (Han et al.). See columns below for explanation of "NA" values.
  • assigned_cell_label_tm: Label assigned from Tabula Muris Consortium dataset for applicable cells. See columns below for explanation of "NA" values.
  • label_status_microwell: In cases where labels assigned from microwell dataset are "NA" this column provides a reason why. Entries with non-NA labels assigned with have "assigned" in this column. NA valuesin in the assigned_cell_label_microwell column can be NA due to tissues not overlapping between the two datasets (not_compared), a cell not being included in a comparison due it not meeting our thresholds for number of non-zero sites (filtered_by_depth), or KNN neighbors not providing a clear majority label (low_support).
  • label_status_tm: same as label_status_microwell but referring to the assigned Tabula Muris label.
correlation_results.txt 08/06/2018 201.9KB Cluster-level Spearman correlations as a metric of similarity between each of our chromatin profiles and cell type assignments made in each of the same sc-RNA-seq studies in TSV format.
  • id: combined ID for major + iterative cluster assignment
  • tissue: the tissue in the cell type tissue pair
  • cell_label: assigned cell type in our study for the specified cluster
  • scrna_seq_dataset: The sc-RNA-seq study used to compute the correlation. One of "tabula_muris" or "microwell".
  • scrna_seq_label: Label assigned from sc-RNA-seq dataset.
  • correlation: Spearman correlation between the activity scores and expression values for genes with variable gene expression as described in manuscript.
microwell_truncated_labels.txt 08/02/2018 6.6KB As described in our manuscript, to facilitate comparisons to the MCA dataset, we made minor modifications to the labels provided in this study (see methods for details). This file provides original labels used in the MCA paper and then the set of labels that we used in TSV format.
  • mca_label: the original label for this cell provided in the MCA data release
  • mca_label.modified: the modified label that we used for comparisons in the manuscript

Cicero Maps

We have also run Cicero (Pliner et al.), which connects regulatory elements to their target genes using coaccessibility as a measure of connectedness, as measured by sci-ATAC-seq. We have generated Cicero maps for each cluster in the dataset. Maps and peak sets are combined into single files with columns to indicate the cluster and subset_cluster entries correspond to.

Name Last modified Size Description
master_cicero_conns.txt 01/11/2018 3.1GB Cicero was run on the peak sets provided below (accessible in at least 1% of cells for a given cluster). This is the resulting map provided in TSV format.
  • Peak1: Peak ID (see below) for first peak in connection.
  • Peak2: Peak ID (see below) for second peak in connection.
  • coaccess: coaccessibility score for connection. Higher values indicate stronger signal. See Pliner et al. for details.
  • peak1.isproximal: Peak1 is proximal if it has overlap with 5kb upstream and 1kb downstream of any TSS (set to "Yes" if proximal).
  • peak1.tss.gene_name: Common gene name of the proximal TSS overlapping Peak1
  • peak1.tss.gene_id: Ensemble gene id of the proximal TSS overlapping Peak1
  • peak2.isproximal: same as for peak1
  • peak2.tss.gene_name: same as for peak1
  • peak2.tss.gene_id: same as for peak1
  • conn_type: one of proximal_proximal, distal_proximal, distal_ distal to describe the combination of proximal and distal status of Peak1 and Peak2.
  • cluster: cluster assignment in initial t-SNE
  • subset_cluster: cluster assignment in iterative t-SNE space
  • subcluster: combined cluster ID (cluster + subset cluster)
master_cicero_conns.rds 01/11/2018 286.3MB Same as above in RDS format.
open_sites_0.01.txt 01/12/2018 455.3MB Peaks that served as input to Cicero in TSV format (accessible in >=1% of cells in a given cluster).
  • site: ID of peak (chr_start_end)
  • num_cells_expressed: number of cells in cluster with at least one read overlapping this site
  • fraction_cells_expressed: fraction of cells in cluster with at least one read overlapping this site
  • cluster: cluster assignment in initial t-SNE
  • subset_cluster: cluster assignment in iterative t-SNE space
open_sites_0.01.rds 01/11/2018 91.5MB Same as above in RDS format.

Basset Results

We have also trained convolutional neural network (CNN) models with Basset (Github; Kelley et al.) to find motifs that distinguish our clusters from one another. Here we provide results relevant to interpretation of these motifs as well as the actual models generated by Basset so they may be used in downstream analyses. To interpret the "filters" in the first layer of the CNN model, we utilize common tools for interpreting PWMs such as TomTom and MEME from the MEME suite in conjunction with the Hocomoco PWM database.
Name Last modified Size Description
training_params.txt 01/11/2018 286.0B Parameters used to train the CNN using Basset. See Basset documentation.
trained_model.th 01/11/2018 88.0MB CNN model trained using Basset. See Basset documentation.
train_test_data.h5 01/11/2018 4.3GB Data used to train and test the CNN in the hdf5 format. See Bassett documentation.
filter_influences.txt 01/11/2018 1.2MB Filter influences as calculated using the CNN model in our manuscript in TSV format.
  • filter: name of filter PWM
  • cluster: cluster assignment in initial t-SNE
  • subset_cluster: cluster assignment in iterative t-SNE space
  • infl: Influence score. See manuscript.
  • filter_id: Numeric ID for the filter
filter_sd_mean.txt 01/11/2018 16.5KB Contains summary statistics for filter activity over the test data.
  • filter: name of filter PWM
  • ic: information content of the filter over all clusters
  • mean: mean influence of the filter over all clusters
  • sd: standard deviation for influence of the filter over all clusters
filters_meme.txt 01/11/2018 343.2KB PWMs for all the first layer filters in the trained CNN (used for interpretation of filters). See MEME documentation.
tomtom_hits.txt 01/11/2018 247.8KB Filters matched to known motifs using TomTom with value < 0.1.
  • #Query ID: name of filter PWM
  • Target ID: motif name in Hocomoco database
  • Optimal offset: See TomTom documentation
  • Overlap: See TomTom documentation
  • Query consensus sequence: See TomTom documentation
  • Target consensus sequence: See TomTom documentation
  • Orientation: See TomTom documentation
cells_by_motifs.txt 01/11/2018 3.4GB Aggregated motif scores for cells.
  • filter: name of filter PWM used to scan accessible sites using TomTom
  • cell: cell barcode (combined and corrected)
  • motif_score: the total number of sites that had this matched this PWM
  • total_motifs: total number of motifs detected in all the accessible sites for this cell
  • motif_activity: shows the relative frequency of this motif relative with all the motifs scored for this cell (motif score/total motifs)
cells_by_motifs.rds 01/11/2018 674.4MB Same as above in RDS format.

Annotation Enrichments

To aid in interpretation we have found it helpful to calculate gene set enrichments using peaks that are DA and have positive betas (are open). We report these enrichments for a number of different gene sets.

Specificity Score Format

The following columns are provided to report annotation enrichments for each cluster:

Column Description
cluster Cluster assignment in initial t-SNE.
subset_cluster Cluster assignment in the iterative t-SNE space.
term Term for the gene set reported in the original gene set.
name A cleaned version of the term column used in figures.
p.value P-value of the reported enrichment.
Adjusted.p.value P-value of the reported enrichment adjusted for multiple testing.
fold_change Fold change of the reported enrichment.
gene_coverage Fraction of the gene set covered in enrichment test.
Name Last modified Size Description
enrichment_all_pathways.txt 01/16/2018 25.8MB Enrichments for terms in all pathways gene sets in TSV format.
enrichment_all_pathways.rds 01/16/2018 5.7MB Same as above in RDS format.
enrichment_GO_bp.txt 01/16/2018 65.1MB Enrichments for terms in GO biological processes gene sets in TSV format.
enrichment_GO_bp.rds 01/16/2018 12.3MB Same as above in RDS format.
enrichment_mouse_phenotype.txt 01/16/2018 16.3MB Enrichments for terms in mouse phenotypes gene sets in TSV format.
enrichment_mouse_phenotype.rds 01/16/2018 1.8MB Same as above in RDS format.
enrichment_reactome.txt 01/16/2018 14.0MB Enrichments for terms in reactome gene sets in TSV format.
enrichment_reactome.rds 01/16/2018 3.2MB Same as above in RDS format.

GWAS h2 Enrichments

As described in the manuscript, we report enrichments in heritability (h2) in DA peaks with positive betas for each cluster across many human traits as measured by GWAS.

These enrichments are calculated using a tool called partitioned LD score regression (LDSC; Finucane et al.; Github). We also report the trained LDSC models and baseline model which could be used to calculate enrichments for any other trait given the appropriate summary statistics.

Name Last modified Size Description
gwas_metadata.xlsx 08/02/2018 27.2KB A table with information about all GWAS (other than UKBB) used in our analysis and where summary statistics can be accessed.
  • gwas_name: the trait examined in the GWAS
  • data_url: URL to the file summary statistics were obtained from
  • lead_author: the lead author(s) of the publication
  • year: year of publication
  • journal: the journal the study was published in
  • study_url: A URL to the publication associated with the dataset
  • other_notes: any other comments about data from this study
gwas.all_results.txt 08/02/2018 538.6KB Heritability enrichments for GWAS examined in manuscript (not UKBB) for each cluster in TSV format.
  • cluster: cluster assignment in initial t-SNE
  • subset_cluster: cluster assignment in iterative t-SNE space
  • gwas: the phenotype studied in GWAS
  • h2: the heritability estimated for the trait in question (gwas column)
  • h2_se: standard error of h2
  • snp_count: The total SNPs reported to be used by LDSC in log files.
  • prop_snps: The proportion of snp_count that fall into DA peaks for the specified cluster
  • prop_h2: The proportion of total h2 that the DA peaks account for.
  • coefficient: The coefficient reported by the LDSC model for the DA peak membership term.
  • scaled_coefficient: coefficient column divided by the per SNP heritability. Roughly interpretable as an enrichment
  • enrichment: an enrichment calculated independent of the baseline enrichment
  • coefficient_zscore: the z-score reported for the unscaled coefficient
  • coefficient_std_error: standard error for coefficient
  • coefficient_pval: P-value for the coefficient being non-zero (enriched over baseline)
  • coefficient_qval: pvalue adjusted for multiple testing
gwas.clustered_matrix.txt 08/02/2018 8.0KB A matrix of -log10(qval) for any significant enrichments from above file in TSV format.

Each row is a trait (as indicated by trait column) and each additional column is a cluster of cells. Entries appear in order according to the hierarchical clustering that appears in the manuscript. Zero entries indicate enrichments that are not significant.
gwas.tissues.all_results.txt 08/06/2018 100.1KB Heritability enrichments for GWAS examined in manuscript (not UKBB) for peaks called from each tissue in TSV format.
  • tissue: the tissue (replicates are not collapsed)
  • gwas: the phenotype studied in GWAS
  • h2: the heritability estimated for the trait in question (gwas column)
  • h2_se: standard error of h2
  • snp_count: The total SNPs reported to be used by LDSC in log files.
  • prop_snps: The proportion of snp_count that fall into DA peaks for the specified cluster
  • prop_h2: The proportion of total h2 that the DA peaks account for.
  • coefficient: The coefficient reported by the LDSC model for the DA peak membership term.
  • scaled_coefficient: coefficient column divided by the per SNP heritability. Roughly interpretable as an enrichment
  • enrichment: an enrichment calculated independent of the baseline enrichment
  • coefficient_zscore: the z-score reported for the unscaled coefficient
  • coefficient_std_error: standard error for coefficient
  • coefficient_pval: P-value for the coefficient being non-zero (enriched over baseline)
  • coefficient_qval: pvalue adjusted for multiple testing
gwas.tissues.clustered_matrix.txt 08/06/2018 9.6KB A matrix of -log10(qval) for any significant enrichments from above file in TSV format.

Each row is a trait (as indicated by trait column) and each additional column a tissue. Entries appear in order according to the hierarchical clustering that appears in the manuscript. Zero entries indicate enrichments that are not significant.
ukbb.all_results.txt 08/02/2018 9.4MB Heritability enrichments for UKBB GWAS examined in manuscript for each cluster in TSV format.
  • cluster: cluster assignment in initial t-SNE
  • subset_cluster: cluster assignment in iterative t-SNE space
  • field: the phenotype description provided by the Neale Lab
  • field_cleaned: a (sometimes) shortened version of field to remove redundant text
  • field_code: the non-descriptive ID for each field value
  • effective_n: the effective sample size for this trait
  • h2: the heritability estimated for the trait in question (gwas column)
  • h2_se: standard error of h2
  • snp_count: The total SNPs reported to be used by LDSC in log files.
  • prop_snps: The proportion of snp_count that fall into DA peaks for the specified cluster
  • prop_h2: The proportion of total h2 that the DA peaks account for.
  • coefficient: The coefficient reported by the LDSC model for the DA peak membership term.
  • scaled_coefficient: coefficient column divided by the per SNP heritability. Roughly interpretable as an enrichment
  • enrichment: an enrichment calculated independent of the baseline enrichment
  • coefficient_zscore: the z-score reported for the unscaled coefficient
  • coefficient_std_error: standard error for coefficient
  • coefficient_pval: P-value for the coefficient being non-zero (enriched over baseline)
  • coefficient_qval: pvalue adjusted for multiple testing
ukbb.clustered_matrix.txt 08/02/2018 81.9KB A matrix of -log10(qval) for any significant enrichments from above file in TSV format.

Each row is a trait (as indicated by trait column) and each additional column a cluster in TSV format. Entries appear in order according to the hierarchical clustering that appears in the manuscript. Zero entries indicate enrichments that are not significant.
ukbb.subset.clustered_matrix.txt 08/02/2018 45.0KB A matrix of -log10(qval) for a subset of phenotypes (as shown in manuscript) in TSV format.

Each row is a trait (as indicated by trait column) and each additional column a cluster. Entries appear in order according to the hierarchical clustering that appears in the manuscript. Zero entries indicate enrichments that are not significant.
ld_score_regression_models.tar.gz 08/02/2018 9.7GB Set of models for each cluster and a baseline model for comparison trained with LDSC.

When unpacked with tar -xzvf ld_score_regression_models.tar.gz, will contain cluster_models and baseline_model subdirectories. These contain the models trained using DA peaks from each cluster and the baseline model to compare against, respectively.

Note that cluster_models will contain one file per cluster per chromosome, so there will be a large number of files in this subdirectory.

These files may then be used in conjuction with the final step of LDSC (see Github for usage and format descriptions) to calculate enrichments of heritability within the DA peaks for a given cluster for summary statistics from any GWAS.

BAM Files

While we provide some raw data on GEO (GSE111586), we also provide BAM files of the sequences aligned to mm9 here in case users would like to use them for their own pipelines or methods development. Below we provide one file per tissue, named by their tissue.replicate ID as specified in the tissue.replicate column of the cell_metadata.txt file in the metadata section above. This means there will be two files for tissues where we performed a replicate and a single file for all other tissues (in addition to BAM index files).

Important: BAM Details

  • Each read is assigned to a cell ID (the sequence specified in the cell column of the same metadata file mentioned above). This is encoded in the read name as cellid:otherinfo, so the sequence before the colon is the corrected cell barcode sequence for the read.
  • Reads are already deduplicated.
  • There will be cell IDs that do not appear in our final set of cells, as data is a superset of what ultimately passes our QC steps.
  • Files may not download correctly in Chrome (and other web browsers), but they can easily be downloaded with wget or curl, by right clicking and copying the link address. For example:
    • wget http://krishna.gs.washington.edu/content/members/ajh24/mouse_atlas_data_release/bams/BoneMarrow_62016.bam
Name Last modified Size Description
BoneMarrow_62016.bam 07/04/2017 7.3GB BAM file for BoneMarrow_62016.
BoneMarrow_62016.bam.bai 07/12/2017 2.9MB BAM index for BoneMarrow_62016.
BoneMarrow_62216.bam 07/04/2017 6.6GB BAM file for BoneMarrow_62216.
BoneMarrow_62216.bam.bai 07/12/2017 2.5MB BAM index for BoneMarrow_62216.
Cerebellum_62216.bam 07/04/2017 2.1GB BAM file for Cerebellum_62216.
Cerebellum_62216.bam.bai 07/12/2017 2.0MB BAM index for Cerebellum_62216.
LargeIntestineA_62816.bam 07/04/2017 4.8GB BAM file for LargeIntestineA_62816.
LargeIntestineA_62816.bam.bai 07/12/2017 2.1MB BAM index for LargeIntestineA_62816.
LargeIntestineB_62816.bam 07/04/2017 8.9GB BAM file for LargeIntestineB_62816.
LargeIntestineB_62816.bam.bai 07/12/2017 3.3MB BAM index for LargeIntestineB_62816.
HeartA_62816.bam 07/04/2017 9.9GB BAM file for HeartA_62816.
HeartA_62816.bam.bai 07/12/2017 3.4MB BAM index for HeartA_62816.
SmallIntestine_62816.bam 07/04/2017 9.3GB BAM file for SmallIntestine_62816.
SmallIntestine_62816.bam.bai 07/13/2017 3.9MB BAM index for SmallIntestine_62816.
Kidney_62016.bam 07/04/2017 8.2GB BAM file for Kidney_62016.
Kidney_62016.bam.bai 07/13/2017 2.9MB BAM index for Kidney_62016.
Liver_62016.bam 07/04/2017 8.1GB BAM file for Liver_62016.
Liver_62016.bam.bai 07/13/2017 2.8MB BAM index for Liver_62016.
Lung1_62216.bam 07/04/2017 5.4GB BAM file for Lung1_62216.
Lung1_62216.bam.bai 07/13/2017 2.4MB BAM index for Lung1_62216.
Lung2_62216.bam 07/04/2017 6.3GB BAM file for Lung2_62216.
Lung2_62216.bam.bai 07/13/2017 2.4MB BAM index for Lung2_62216.
PreFrontalCortex_62216.bam 07/04/2017 11.1GB BAM file for PreFrontalCortex_62216.
PreFrontalCortex_62216.bam.bai 07/13/2017 4.0MB BAM index for PreFrontalCortex_62216.
Spleen_62016.bam 07/04/2017 5.0GB BAM file for Spleen_62016.
Spleen_62016.bam.bai 07/13/2017 2.5MB BAM index for Spleen_62016.
Testes_62016.bam 07/04/2017 12.5GB BAM file for Testes_62016.
Testes_62016.bam.bai 07/13/2017 5.7MB BAM index for Testes_62016.
Thymus_62016.bam 07/04/2017 10.2GB BAM file for Thymus_62016.
Thymus_62016.bam.bai 07/13/2017 3.4MB BAM index for Thymus_62016.
WholeBrainA_62216.bam 07/04/2017 8.9GB BAM file for WholeBrainA_62216.
WholeBrainA_62216.bam.bai 07/13/2017 3.4MB BAM index for WholeBrainA_62216.
WholeBrainA_62816.bam 07/04/2017 6.4GB BAM file for WholeBrainA_62816.
WholeBrainA_62816.bam.bai 07/13/2017 2.7MB BAM index for WholeBrainA_62816.

UCSC Trackhub and Bigwigs

We also provide bigWig files and a UCSC trackhub to visualize aggregated pseudo-bulk ATAC-seq profiles for the cells from each cluster. Note that for the smallest clusters, the data will appear fairly sparse even in aggregate at any single locus. In general, we prefer methods for assessing differential accessibility or specificity computationally over visual inspection, although viewing tracks is often useful to get a sense for the data at a given locus.

UCSC Trackhub

You may access our UCSC trackhub here. By default the hub will contain a track at the top called _All_Peak_Calls, which annotates regions that we called within LSI clusters for each tissue (see Methods). These peaks were used as our features for all downstream analysis.

The trackhub will also contain a track for each cluster in the dataset named according to the convention cell_label-id, where cell_label and id are defined in the same way as they are in our cell_metadata.txt file above. Spaces and periods in cell labels have been removed or replaced as necessary.

Important

We have generated these tracks using DeepTools3 and CPM or Counts Per Million normalization with using the bamCoverage command with arguments -bs 1 --normalizeUsing CPM --skipNAs. The default range displayed on UCSC is 0 to 4 for all tracks, which we find generally works well. However, it is possible that it may need to be adjusted in some cases.

BigWig Files

In case you would like access to the files used to make the trackhub above, we provide them for download below. Each file is named in the same manner as described above with a .bw or .bb extension.

Name Last modified Size Description
master_peaks_track.bb 08/14/2018 3.9MB bigBed formatted file with our master set of peak calls.
Activated_B_cells-clusters_4-cluster_4.bw 08/14/2018 77.4MB bigWig formated track for cluster.
Alveolar_macrophages-clusters_17-cluster_2.bw 08/14/2018 166.7MB bigWig formated track for cluster.
Astrocytes-clusters_19-cluster_1.bw 08/14/2018 144.0MB bigWig formated track for cluster.
Astrocytes-clusters_19-cluster_2.bw 08/14/2018 69.2MB bigWig formated track for cluster.
Astrocytes-clusters_19-cluster_3.bw 08/14/2018 32.4MB bigWig formated track for cluster.
Astrocytes-clusters_19-cluster_4.bw 08/14/2018 29.5MB bigWig formated track for cluster.
B_cells-clusters_16-cluster_1.bw 08/14/2018 323.2MB bigWig formated track for cluster.
B_cells-clusters_4-cluster_1.bw 08/14/2018 168.2MB bigWig formated track for cluster.
B_cells-clusters_4-cluster_2.bw 08/14/2018 178.3MB bigWig formated track for cluster.
B_cells-clusters_4-cluster_3.bw 08/14/2018 350.2MB bigWig formated track for cluster.
Cardiomyocytes-clusters_7-cluster_1.bw 08/14/2018 960.2MB bigWig formated track for cluster.
Cerebellar_granule_cells-clusters_8-cluster_1.bw 08/14/2018 331.5MB bigWig formated track for cluster.
Cerebellar_granule_cells-clusters_8-cluster_2.bw 08/14/2018 245.5MB bigWig formated track for cluster.
Collecting_duct-clusters_18-cluster_5.bw 08/14/2018 42.5MB bigWig formated track for cluster.
Collisions-clusters_17-cluster_4.bw 08/14/2018 45.8MB bigWig formated track for cluster.
Collisions-clusters_17-cluster_5.bw 08/14/2018 82.0MB bigWig formated track for cluster.
Collisions-clusters_26-cluster_2.bw 08/14/2018 56.3MB bigWig formated track for cluster.
Collisions-clusters_26-cluster_3.bw 08/14/2018 100.5MB bigWig formated track for cluster.
Collisions-clusters_27-cluster_2.bw 08/14/2018 176.9MB bigWig formated track for cluster.
Collisions-clusters_27-cluster_3.bw 08/14/2018 56.0MB bigWig formated track for cluster.
Collisions-clusters_30-cluster_3.bw 08/14/2018 19.2MB bigWig formated track for cluster.
DCT_CD-clusters_18-cluster_1.bw 08/14/2018 138.0MB bigWig formated track for cluster.
Dendritic_cells-clusters_17-cluster_1.bw 08/14/2018 101.6MB bigWig formated track for cluster.
Dendritic_cells-clusters_17-cluster_3.bw 08/14/2018 123.7MB bigWig formated track for cluster.
Distal_convoluted_tubule-clusters_18-cluster_4.bw 08/14/2018 90.0MB bigWig formated track for cluster.
Endothelial_II_cells-clusters_23-cluster_1.bw 08/14/2018 282.6MB bigWig formated track for cluster.
Endothelial_II_cells-clusters_25-cluster_2.bw 08/14/2018 50.0MB bigWig formated track for cluster.
Endothelial_II_cells-clusters_25-cluster_3.bw 08/14/2018 14.2MB bigWig formated track for cluster.
Endothelial_II_cells-clusters_9-cluster_2.bw 08/14/2018 168.3MB bigWig formated track for cluster.
Endothelial_II_cells-clusters_9-cluster_3.bw 08/14/2018 45.5MB bigWig formated track for cluster.
Endothelial_I_(glomerular)-clusters_22-cluster_2.bw 08/14/2018 67.7MB bigWig formated track for cluster.
Endothelial_I_cells-clusters_22-cluster_1.bw 08/14/2018 83.4MB bigWig formated track for cluster.
Endothelial_I_cells-clusters_22-cluster_3.bw 08/14/2018 21.8MB bigWig formated track for cluster.
Endothelial_I_cells-clusters_22-cluster_4.bw 08/14/2018 20.5MB bigWig formated track for cluster.
Enterocytes-clusters_6-cluster_1.bw 08/14/2018 1.2GB bigWig formated track for cluster.
Erythroblasts-clusters_13-cluster_1.bw 08/14/2018 483.1MB bigWig formated track for cluster.
Ex_neurons_CPN-clusters_5-cluster_1.bw 08/14/2018 736.2MB bigWig formated track for cluster.
Ex_neurons_CThPN-clusters_5-cluster_3.bw 08/14/2018 405.7MB bigWig formated track for cluster.
Ex_neurons_CThPN-clusters_5-cluster_4.bw 08/14/2018 441.8MB bigWig formated track for cluster.
Ex_neurons_SCPN-clusters_29-cluster_1.bw 08/14/2018 108.3MB bigWig formated track for cluster.
Ex_neurons_SCPN-clusters_5-cluster_2.bw 08/14/2018 609.6MB bigWig formated track for cluster.
Hematopoietic_progenitors-clusters_10-cluster_1.bw 08/14/2018 1.1GB bigWig formated track for cluster.
Hepatocytes-clusters_3-cluster_1.bw 08/14/2018 1.1GB bigWig formated track for cluster.
Immature_B_cells-clusters_28-cluster_1.bw 08/14/2018 41.3MB bigWig formated track for cluster.
Immature_B_cells-clusters_28-cluster_2.bw 08/14/2018 58.7MB bigWig formated track for cluster.
Inhibitory_neurons-clusters_15-cluster_1.bw 08/14/2018 585.5MB bigWig formated track for cluster.
Inhibitory_neurons-clusters_15-cluster_2.bw 08/14/2018 227.5MB bigWig formated track for cluster.
Inhibitory_neurons-clusters_5-cluster_5.bw 08/14/2018 156.9MB bigWig formated track for cluster.
Loop_of_henle-clusters_18-cluster_2.bw 08/14/2018 126.0MB bigWig formated track for cluster.
Loop_of_henle-clusters_18-cluster_3.bw 08/14/2018 69.8MB bigWig formated track for cluster.
Macrophages-clusters_16-cluster_2.bw 08/14/2018 181.5MB bigWig formated track for cluster.
Microglia-clusters_16-cluster_3.bw 08/14/2018 81.1MB bigWig formated track for cluster.
Monocytes-clusters_24-cluster_1.bw 08/14/2018 132.3MB bigWig formated track for cluster.
Monocytes-clusters_24-cluster_2.bw 08/14/2018 151.4MB bigWig formated track for cluster.
NK_cells-clusters_12-cluster_3.bw 08/14/2018 63.8MB bigWig formated track for cluster.
Oligodendrocytes-clusters_21-cluster_1.bw 08/14/2018 124.0MB bigWig formated track for cluster.
Oligodendrocytes-clusters_21-cluster_2.bw 08/14/2018 84.1MB bigWig formated track for cluster.
Podocytes-clusters_25-cluster_1.bw 08/14/2018 89.2MB bigWig formated track for cluster.
Proximal_tubule-clusters_11-cluster_1.bw 08/14/2018 137.2MB bigWig formated track for cluster.
Proximal_tubule-clusters_11-cluster_2.bw 08/14/2018 222.5MB bigWig formated track for cluster.
Proximal_tubule-clusters_11-cluster_3.bw 08/14/2018 168.0MB bigWig formated track for cluster.
Proximal_tubule-clusters_11-cluster_5.bw 08/14/2018 192.6MB bigWig formated track for cluster.
Proximal_tubule_S3-clusters_11-cluster_4.bw 08/14/2018 252.0MB bigWig formated track for cluster.
Purkinje_cells-clusters_27-cluster_1.bw 08/14/2018 95.1MB bigWig formated track for cluster.
Regulatory_T_cells-clusters_12-cluster_2.bw 08/14/2018 101.2MB bigWig formated track for cluster.
SOM+_Interneurons-clusters_15-cluster_3.bw 08/14/2018 222.1MB bigWig formated track for cluster.
Sperm-clusters_14-cluster_1.bw 08/14/2018 274.3MB bigWig formated track for cluster.
Sperm-clusters_14-cluster_2.bw 08/14/2018 137.4MB bigWig formated track for cluster.
Sperm-clusters_14-cluster_3.bw 08/14/2018 140.9MB bigWig formated track for cluster.
T_cells-clusters_12-cluster_1.bw 08/14/2018 285.0MB bigWig formated track for cluster.
T_cells-clusters_12-cluster_4.bw 08/14/2018 90.2MB bigWig formated track for cluster.
T_cells-clusters_12-cluster_5.bw 08/14/2018 27.0MB bigWig formated track for cluster.
T_cells-clusters_2-cluster_1.bw 08/14/2018 1.1GB bigWig formated track for cluster.
T_cells-clusters_26-cluster_1.bw 08/14/2018 78.0MB bigWig formated track for cluster.
Type_II_pneumocytes-clusters_30-cluster_1.bw 08/14/2018 37.0MB bigWig formated track for cluster.
Type_I_pneumocytes-clusters_20-cluster_1.bw 08/14/2018 282.6MB bigWig formated track for cluster.
Unknown-clusters_1-cluster_1.bw 08/14/2018 1.3GB bigWig formated track for cluster.
Unknown-clusters_1-cluster_2.bw 08/14/2018 598.2MB bigWig formated track for cluster.
Unknown-clusters_1-cluster_3.bw 08/14/2018 369.5MB bigWig formated track for cluster.
Unknown-clusters_23-cluster_2.bw 08/14/2018 40.8MB bigWig formated track for cluster.
Unknown-clusters_30-cluster_2.bw 08/14/2018 66.9MB bigWig formated track for cluster.
Unknown-clusters_30-cluster_4.bw 08/14/2018 16.9MB bigWig formated track for cluster.
Unknown-clusters_5-cluster_6.bw 08/14/2018 102.6MB bigWig formated track for cluster.
Unknown-clusters_7-cluster_2.bw 08/14/2018 45.2MB bigWig formated track for cluster.
Unknown-clusters_9-cluster_1.bw 08/14/2018 613.1MB bigWig formated track for cluster.