UCSC Trackhub and BigWigssection which provides documentation on new bigWig files and a UCSC Trackhub for visualization.
08/10/2018adds another metadata file option,
cell_metadata.tissue_freq_filtered.txt, which removes cells in each tissue belonging to a
cell_labelthat 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.
BAM Filesfor BAM downloads.
08/02/2018: Initial release.
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
python along with documentation on lots of other downstream analysis.
In general, we provide two formats for all matrices:
Same as format generated by 10X Genomics
RDS format that can be read into R directly with
|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.|
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.
Quantitative scores provided here are not normalized by size factors, so you may apply size factor normalization to these values if needed.
|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.|
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.
|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.
|peak_promoter_intersections.txt||08/02/18||6.9MB||Metadata for peaks-intersected TSS pairs in TSV format
|cell_type_assignments.xlsx||08/06/18||134.1KB||Excel document with three tabs
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).
The following columns are provided in each DA test file. Note that files contain one entry per combination of
||status of successful test completion reported by monocle (OK for all in this case)|
||the distribution family used by monocle (binomialff in this case)|
||Uncorrected P-value returned by monocle.|
||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.|
||Q-value returned by monocle corrected for all tests performed for the specified cluster.|
||For ATAC matrix this is the peak ID of the peak being tested (chr_start_stop; header
||Cluster assignment in initial t-SNE.|
||Cluster assignment in iterative t-SNE space.|
|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.|
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.
|peak_specificity_scores.long_format.txt.gz||08/02/2018||568.7MB||Long format file with specificity scores for all sites across all clusters. format details
|peak_specificity_scores.long_format.rds||08/02/2018||496.2MB||Same as above in RDS format.|
|gene_specificity_scores.long_format.rds||08/06/2018||12.6MB||Same as above in RDS format.|
|peak_specificity_scores_sigsites.long_format.rds||08/02/2018||4.5MB||Same as above in RDS format.|
|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.|
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.
|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.
|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.
|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.
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.
|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.
|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).
|open_sites_0.01.rds||01/11/2018||91.5MB||Same as above in RDS format.|
|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
|filter_influences.txt||01/11/2018||1.2MB||Filter influences as calculated using the CNN model in our manuscript in TSV format.
|filter_sd_mean.txt||01/11/2018||16.5KB||Contains summary statistics for filter activity over the test data.
|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.
|cells_by_motifs.txt||01/11/2018||3.4GB||Aggregated motif scores for cells.
|cells_by_motifs.rds||01/11/2018||674.4MB||Same as above in RDS format.|
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.
The following columns are provided to report annotation enrichments for each cluster:
||Cluster assignment in initial t-SNE.|
||Cluster assignment in the iterative t-SNE space.|
||Term for the gene set reported in the original gene set.|
||A cleaned version of the
||P-value of the reported enrichment.|
||P-value of the reported enrichment adjusted for multiple testing.|
||Fold change of the reported enrichment.|
||Fraction of the gene set covered in enrichment test.|
|enrichment_all_pathways.txt||01/16/2018||25.8MB||Enrichments for terms in
|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
|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
|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
|enrichment_reactome.rds||01/16/2018||3.2MB||Same as above in RDS format.|
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.
|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.all_results.txt||08/02/2018||538.6KB||Heritability enrichments for GWAS examined in manuscript (not UKBB) for each cluster in TSV format.
|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
|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.
|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
|ukbb.all_results.txt||08/02/2018||9.4MB||Heritability enrichments for UKBB GWAS examined in manuscript for each cluster in TSV format.
|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
|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
|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
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.
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).
cellcolumn 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.
curl, by right clicking and copying the link address. For example:
|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.|
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.
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
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.
We have generated these tracks using DeepTools3 and
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.
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
|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.|