b914d581f876d486caf9404e715e4f61ada38195 mspeir Mon Aug 3 16:19:44 2026 -0700 singleCellSignalsPeaks: make every subtrack label unique, fix cell-class regressions The harmonized labels were not distinguishing tracks. build_long_label composed from cell type + condition + tissue, but the upstream steps discard the discriminators on purpose to keep the facets compact, so hg38 had 925 subtracks sharing only 401 distinct longLabels and mm10 629 sharing 359. Worst case: cortex-atac's MACS, enhancer and cell-type-specific peak sets all read "Astrocytes and oligodendrocytes (Cortex ATAC)", with the peak method surviving only in the raw shortLabel. Labels are now built from the harmonized cell type plus a variant descriptor that recovers what was dropped (peak method, grouping level, cohort, signal vs peaks), and anything still colliding is qualified with its source cluster code. shortLabels are rebuilt too -- the old ones were raw source strings up to 50 chars with underscores, ArchR filename tails and R-mangled names -- abbreviated through a curated word table to 22 chars, with compact tokens where the longLabel distinction would otherwise be invisible (SEA-AD region + ADNC, CATLAS aging age). All 925/587 longLabels are now unique; no shortLabel exceeds 22 chars or contains an underscore. Also fixed, found while verifying the above: - Correcting source misspellings in the cell types broke the curated lookups, which are keyed on those same misspelled strings, and 16 hg38 tracks silently lost their Cell_class and color. The class map and the hub_config tables now normalize their keys on load, and a cell type with no broad class is reported instead of becoming "unknown". - Nephron progenitor was classed as Neural progenitor: the decode tables give it the bare broad class "Progenitor" and that was blanket-mapped to neural. It is Six2+ kidney cap mesenchyme, so it is now Stromal. The HTML legend had been worded to match the bug. - The plural/case merge picked the most frequent form, which was inconsistent -- singular for 15 of 17 merged groups but plural for Megakaryocytes/Oligodendrocytes. It now prefers the singular. - Removed 42 byte-identical Allen basal-ganglia bigWigs (md5-verified) that were served from four grouping directories and rendered as four indistinguishable mm10 subtracks, freeing 4.4 GB. Where the four copies genuinely differ all are kept and told apart by the grouping-level descriptor. mm10 goes 629 -> 587 subtracks. - Corrected five stale per-dataset subtrack counts in the description pages and spelled out what ADNC means, noting that it grades neuropathology rather than symptoms. - The SEA-AD Dataset facet link used the collection name, which is not a served Cell Browser slug; it now points at sea-ad-mtg+cohort. - Dropped a dead placeholder variable and made the hardcoded hub-build path overridable. refs #37914 Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> diff --git src/hg/makeDb/trackDb/mouse/mm10/singleCellSignalsPeaks.html src/hg/makeDb/trackDb/mouse/mm10/singleCellSignalsPeaks.html index 0ebde372f3c..52aaa0e673b 100644 --- src/hg/makeDb/trackDb/mouse/mm10/singleCellSignalsPeaks.html +++ src/hg/makeDb/trackDb/mouse/mm10/singleCellSignalsPeaks.html @@ -1,318 +1,318 @@ <h2>Description</h2> <p> This track collects the cell-type chromatin accessibility tracks from the single-cell ATAC-seq datasets in the <a href="https://cells.ucsc.edu" target="_blank">UCSC Cell Browser</a>. For each dataset it shows the read-coverage signal (bigWig) and, where the study reported them, the accessible-region peak calls (bigNarrowPeak), split out by cell type. The datasets cover several mouse tissues, including brain, kidney, and choroid plexus. </p> <p> The subtracks come from these datasets: </p> <ul> <li> <b><a href="https://cells.ucsc.edu/?ds=catlas-mouse-aging" target="_blank">CATLAS Mouse Aging Brain</a></b>: - chromatin accessibility across cell types of the aging mouse brain. 234 + chromatin accessibility across cell types of the aging mouse brain. 222 signal subtracks (Zhang et al. 2022). </li> <li> <b><a href="https://cells.ucsc.edu/?ds=catlas-mouse-brain" target="_blank">CATLAS Adult Mouse Brain</a></b>: chromatin accessibility across cell types of the adult mouse cerebrum. 160 signal subtracks (Li et al. 2021). </li> <li> <b><a href="https://brain-map.org/genetic-tools/genetic-tools-atlas" target="_blank">Allen Basal Ganglia ATAC</a></b>: accessibility across basal ganglia cell types, merged by region and neuron - subtype, from the Allen Institute Genetic Tools Atlas. 131 signal subtracks. + subtype, from the Allen Institute Genetic Tools Atlas. 89 signal subtracks. </li> <li> <b><a href="https://cells.ucsc.edu/?ds=olg-dyn-eae-multiome" target="_blank">Dynamic Responses of Oligodendroglia in EAE Mice</a></b>: accessibility (the ATAC half of a single-cell multiome study) of oligodendroglia across disease progression in a mouse model of multiple sclerosis. 28 signal subtracks (Zheng et al. 2025). </li> <li> <b><a href="https://cells.ucsc.edu/?ds=mouse-lvcp-multiome" target="_blank">Mouse Lateral Ventricle Choroid Plexus Multi-omics</a></b>: accessibility (the ATAC half of a multi-omics study) of the lateral ventricle choroid plexus, with per-cell-type peak calls. 17 signal and 6 peak subtracks. </li> <li> <b><a href="https://cells.ucsc.edu/?ds=catlas-paired-tag" target="_blank">CATLAS Adult Mouse Brain Paired-Tag</a></b>: the accessibility tracks from joint histone-and-transcriptome Paired-Tag profiling of the adult mouse brain. 21 signal subtracks (Zhu et al. 2021). </li> <li> <b><a href="https://cells.ucsc.edu/?ds=mouse-kidney-atac" target="_blank">Mouse Kidney Regulatory Landscape</a></b>: single-cell accessibility across mouse kidney cell types, used to map cellular differentiation programs and disease-associated regulatory - regions. 19 signal subtracks (Miao et al. 2021). + regions. 13 signal subtracks (Miao et al. 2021). </li> <li> <b><a href="https://cells.ucsc.edu/?ds=olg-eae-ms" target="_blank">Oligodendrocytes in Mouse EAE Model of MS</a></b>: accessibility in the oligodendrocyte lineage from a mouse model of multiple sclerosis. 16 signal subtracks (Meijer et al. 2022). </li> <li> <b><a href="https://cells.ucsc.edu/?ds=mouse-epi-juv-brain" target="_blank">Multimodal Chromatin Profiling of Juvenile Mouse Brain</a></b>: accessibility from nanobody-based single-cell CUT&Tag multimodal profiling of the juvenile mouse brain. 15 signal subtracks (Bartosovic and Castelo-Branco 2023). </li> </ul> <h2>Display Conventions and Configuration</h2> <p> This is a faceted collection, so the subtracks are chosen with filter menus rather than a long checkbox list. Use the facets on the track configuration page to narrow the subtracks by dataset, tissue, life stage, condition, data type, assay, and cell type, then turn on the ones you want. Signal subtracks draw as coverage graphs and peak subtracks draw as boxes. Each subtrack links back to its source dataset in the Cell Browser. </p> <p> Subtracks are colored by broad cell-type class, so the same class is shown in the same color across datasets (and matches the coloring of the corresponding human track). The classes are: </p> <table class="stdTbl"> <tr><th style="background-color:rgb(0,114,178);width:2em"> </th> <td>Excitatory neuron — glutamatergic neurons of the cortical layers, hippocampal CA fields and dentate gyrus, and claustrum</td></tr> <tr><th style="background-color:rgb(213,94,0);width:2em"> </th> <td>Inhibitory neuron — GABAergic neurons and interneurons (Pvalb, Sst, Lamp5, and CGE- and MGE-derived types)</td></tr> <tr><th style="background-color:rgb(230,159,0);width:2em"> </th> <td>Medium spiny neuron — striatal D1 and D2 projection neurons of the direct and indirect pathways</td></tr> <tr><th style="background-color:rgb(86,180,233);width:2em"> </th> <td>Other neuron — remaining neuronal types, such as Cajal-Retzius cells</td></tr> <tr><th style="background-color:rgb(51,34,136);width:2em"> </th> - <td>Neural progenitor — neuroblasts, radial glia, and nephron progenitors</td></tr> + <td>Neural progenitor — neuroblasts, radial glia, and other neural progenitors</td></tr> <tr><th style="background-color:rgb(0,158,115);width:2em"> </th> <td>Astrocyte — astrocytes, including Bergmann glia and fibrous and protoplasmic subtypes</td></tr> <tr><th style="background-color:rgb(204,121,167);width:2em"> </th> <td>Oligodendrocyte — oligodendrocytes across the newly-formed, myelin-forming, and mature stages</td></tr> <tr><th style="background-color:rgb(240,228,66);width:2em"> </th> <td>Oligodendrocyte precursor — oligodendrocyte precursor cells (OPCs) and committed precursors</td></tr> <tr><th style="background-color:rgb(0,0,0);width:2em"> </th> <td>Microglia — microglia and perivascular macrophages</td></tr> <tr><th style="background-color:rgb(68,170,153);width:2em"> </th> <td>Ependymal — ependymal cells lining the ventricles</td></tr> <tr><th style="background-color:rgb(136,34,85);width:2em"> </th> <td>Choroid plexus — choroid plexus epithelial cells</td></tr> <tr><th style="background-color:rgb(136,204,238);width:2em"> </th> <td>Endothelial — endothelial cells of arteries, capillaries, veins, and endocardium</td></tr> <tr><th style="background-color:rgb(221,204,119);width:2em"> </th> <td>Mural — pericytes, smooth muscle, and vascular leptomeningeal cells</td></tr> <tr><th style="background-color:rgb(238,102,119);width:2em"> </th> <td>Immune — lymphoid (B, T) and myeloid (macrophage, dendritic, basophil) immune cells</td></tr> <tr><th style="background-color:rgb(170,68,153);width:2em"> </th> <td>Erythroid — erythroid cells and erythroblasts</td></tr> <tr><th style="background-color:rgb(153,153,51);width:2em"> </th> <td>Hematopoietic stem/progenitor — hematopoietic stem and progenitor cells</td></tr> <tr><th style="background-color:rgb(170,68,0);width:2em"> </th> <td>Cardiomyocyte — heart muscle cells</td></tr> <tr><th style="background-color:rgb(102,17,0);width:2em"> </th> <td>Muscle — skeletal muscle myofibers, satellite cells, and junctional myonuclei</td></tr> <tr><th style="background-color:rgb(17,119,51);width:2em"> </th> <td>Epithelial — epithelial cells of many tissues, such as airway, gut, kidney tubule, and secretory epithelia</td></tr> <tr><th style="background-color:rgb(153,79,0);width:2em"> </th> <td>Stromal — fibroblasts, mesenchymal, and other stromal cells</td></tr> <tr><th style="background-color:rgb(153,153,153);width:2em"> </th> <td>Other — other or mixed cell types, such as olfactory ensheathing cells and melanocytes</td></tr> <tr><th style="background-color:rgb(187,187,187);width:2em"> </th> <td>Unknown — cell type not resolved from the source data</td></tr> </table> <h2>Methods</h2> <p> Each dataset was produced and processed by a different group, so the assays and analysis pipelines vary. The signal and peak files here are the same ones served by the individual Cell Browser datasets, copied into the browser without change. The table summarizes each dataset; see the linked publication for full detail. </p> <table class="stdTbl"> <tr><th>Dataset</th><th>Assay</th><th>Processing summary</th></tr> <tr> <td>CATLAS Mouse Aging Brain</td> <td>single-cell ATAC-seq</td> <td>Accessibility profiled across cell types of the aging mouse brain; per-cell-type coverage tracks.</td> </tr> <tr> <td>CATLAS Adult Mouse Brain</td> <td>single-cell ATAC-seq</td> <td>Adult mouse cerebrum nuclei clustered into cell types, with per-cell-type accessibility coverage.</td> </tr> <tr> <td>Allen Basal Ganglia ATAC</td> <td>single-nucleus ATAC-seq</td> <td>Basal ganglia nuclei grouped by region and neuron subtype (e.g. D1/D2 MSNs, dorsal/ventral), coverage per group.</td> </tr> <tr> <td>Dynamic Responses of Oligodendroglia in EAE Mice</td> <td>single-cell multiome (ATAC + RNA)</td> <td>Oligodendroglia profiled across EAE disease progression; the ATAC arm gives per-cell-type accessibility coverage.</td> </tr> <tr> <td>Mouse Lateral Ventricle Choroid Plexus Multi-omics</td> <td>single-cell multiome (ATAC + RNA)</td> <td>Choroid plexus nuclei profiled by multi-omics; ATAC arm gives per-cell-type coverage plus peak calls.</td> </tr> <tr> <td>CATLAS Adult Mouse Brain Paired-Tag</td> <td>Paired-Tag (histone + RNA)</td> <td>Joint histone-modification and transcriptome profiling of adult mouse brain; the accessibility tracks are shown here.</td> </tr> <tr> <td>Mouse Kidney Regulatory Landscape</td> <td>single-cell ATAC-seq</td> <td>Mouse kidney nuclei clustered into cell types; per-cell-type accessibility used to map differentiation programs and disease-relevant regulatory regions.</td> </tr> <tr> <td>Oligodendrocytes in Mouse EAE Model of MS</td> <td>single-cell ATAC-seq</td> <td>Accessibility of the oligodendrocyte lineage in a mouse EAE model of multiple sclerosis.</td> </tr> <tr> <td>Multimodal Chromatin Profiling of Juvenile Mouse Brain</td> <td>nanobody-based single-cell CUT&Tag</td> <td>Multimodal single-cell chromatin profiling of juvenile mouse brain; the accessibility tracks are shown here.</td> </tr> </table> <p> The steps used to assemble the files into this track are recorded in the <a href="https://github.com/ucscGenomeBrowser/kent/blob/master/src/hg/makeDb/doc/mm10/singleCellSignalsPeaks.txt" target="_blank">makeDoc</a>, which uses the scripts in the <a href="https://github.com/ucscGenomeBrowser/kent/tree/master/src/hg/makeDb/scripts/singleCellSignalsPeaks" target="_blank">singleCellSignalsPeaks</a> directory. </p> <h2>Data Access</h2> <p> The subtracks can be explored in table form with the <a href="hgTables">Table Browser</a> or the <a href="hgIntegrator">Data Integrator</a>, and read from scripts through our <a href="https://api.genome.ucsc.edu" target="_blank">API</a>. </p> <p> For automated download and analysis, the signal and peak files are stored under <a href="http://hgdownload.soe.ucsc.edu/gbdb/mm10/bbi/singleCellSignalsPeaks/" target="_blank">http://hgdownload.soe.ucsc.edu/gbdb/mm10/bbi/singleCellSignalsPeaks/</a>, keeping the same per-dataset subdirectories shown on the configuration page. The files can be read with the command-line tools <tt>bigWigToBedGraph</tt> (for the signal bigWigs) and <tt>bigBedToBed</tt> (for the peak files), which can be compiled from source or downloaded as <a href="http://hgdownload.soe.ucsc.edu/downloads.html#utilities_downloads" target="_blank">precompiled binaries</a>. Both take a region so you do not have to download the whole file, for example: </p> <pre><code>bigWigToBedGraph -chrom=chr1 -start=3000000 -end=3100000 \ http://hgdownload.soe.ucsc.edu/gbdb/mm10/bbi/singleCellSignalsPeaks/mouse-epi-juv-brain/hub/atac/BG.bw \ stdout bigBedToBed -chrom=chr19 -start=0 -end=61000000 \ http://hgdownload.soe.ucsc.edu/gbdb/mm10/bbi/singleCellSignalsPeaks/mouse-lvcp-multiome/scMultiome/atac/hub/peaks/all_cells.bb \ stdout</code></pre> <p> The underlying matrices, metadata, and per-dataset download details are on each dataset's page in the <a href="https://cells.ucsc.edu" target="_blank">UCSC Cell Browser</a>. </p> <h2>Credits</h2> <p> Thanks to the UCSC Cell Browser team and the research groups whose single-cell datasets are shown here. Questions about a particular subtrack are best directed to the dataset page it links to. </p> <h2>References</h2> <p> Zhang Y, Amaral ML, Zhu C, Grieco SF, Hou X, Lin L, Buchanan J, Tong L, Preissl S, Xu X <em>et al</em>. <a href="https://doi.org/10.1038/s41422-022-00719-6" target="_blank"> Single-cell epigenome analysis reveals age-associated decay of heterochromatin domains in excitatory neurons in the mouse brain</a>. <em>Cell Res</em>. 2022 Nov;32(11):1008-1021. PMID: <a href="https://www.ncbi.nlm.nih.gov/pubmed/36207411" target="_blank">36207411</a>; PMC: <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9652396/" target="_blank">PMC9652396</a> </p> <p> Li YE, Preissl S, Hou X, Zhang Z, Zhang K, Qiu Y, Poirion OB, Li B, Chiou J, Liu H <em>et al</em>. <a href="https://doi.org/10.1038/s41586-021-03604-1" target="_blank"> An atlas of gene regulatory elements in adult mouse cerebrum</a>. <em>Nature</em>. 2021 Oct;598(7879):129-136. PMID: <a href="https://www.ncbi.nlm.nih.gov/pubmed/34616068" target="_blank">34616068</a>; PMC: <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8494637/" target="_blank">PMC8494637</a> </p> <p> Zheng C, Hervé B, Meijer M, Rubio Rodríguez-Kirby LA, Guerreiro Cacais AO, Kukanja P, Kabbe M, Jimenez-Beristain T, Olsson T, Agirre E <em>et al</em>. <a href="https://doi.org/10.1038/s41593-025-02100-3" target="_blank"> Distinct transcriptomic and epigenomic responses of mature oligodendrocytes during disease progression in a mouse model of multiple sclerosis</a>. <em>Nat Neurosci</em>. 2025 Dec;28(12):2612-2627. PMID: <a href="https://www.ncbi.nlm.nih.gov/pubmed/41249698" target="_blank">41249698</a>; PMC: <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12672374/" target="_blank">PMC12672374</a> </p> <p> Zhu C, Zhang Y, Li YE, Lucero J, Behrens MM, Ren B. <a href="https://doi.org/10.1038/s41592-021-01060-3" target="_blank"> Joint profiling of histone modifications and transcriptome in single cells from mouse brain</a>. <em>Nat Methods</em>. 2021 Mar;18(3):283-292. PMID: <a href="https://www.ncbi.nlm.nih.gov/pubmed/33589836" target="_blank">33589836</a>; PMC: <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7954905/" target="_blank">PMC7954905</a> </p> <p> Miao Z, Balzer MS, Ma Z, Liu H, Wu J, Shrestha R, Aranyi T, Kwan A, Kondo A, Pontoglio M <em>et al</em>. <a href="https://doi.org/10.1038/s41467-021-22266-1" target="_blank"> Single cell regulatory landscape of the mouse kidney highlights cellular differentiation programs and disease targets</a>. <em>Nat Commun</em>. 2021 Apr 15;12(1):2277. PMID: <a href="https://www.ncbi.nlm.nih.gov/pubmed/33859189" target="_blank">33859189</a>; PMC: <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8050063/" target="_blank">PMC8050063</a> </p> <p> Meijer M, Agirre E, Kabbe M, van Tuijn CA, Heskol A, Zheng C, Mendanha Falcão A, Bartosovic M, Kirby L, Calini D <em>et al</em>. <a href="https://linkinghub.elsevier.com/retrieve/pii/S0896-6273(21)01089-8" target="_blank"> Epigenomic priming of immune genes implicates oligodendroglia in multiple sclerosis susceptibility</a>. <em>Neuron</em>. 2022 Apr 6;110(7):1193-1210.e13. PMID: <a href="https://www.ncbi.nlm.nih.gov/pubmed/35093191" target="_blank">35093191</a>; PMC: <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9810341/" target="_blank">PMC9810341</a> </p> <p> Bartosovic M, Castelo-Branco G. <a href="https://doi.org/10.1038/s41587-022-01535-4" target="_blank"> Multimodal chromatin profiling using nanobody-based single-cell CUT&Tag</a>. <em>Nat Biotechnol</em>. 2023 Jun;41(6):794-805. PMID: <a href="https://www.ncbi.nlm.nih.gov/pubmed/36536148" target="_blank">36536148</a>; PMC: <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10264246/" target="_blank">PMC10264246</a> </p>