3522a9acc8256a35230b07f2e7ab3fc08c827f33 mspeir Sat Sep 5 17:19:20 2026 -0700 singleCellSignalsPeaks: correct the hg38 description page counts and cite BrainVar, refs #38219 From the v503 code review. Commit 3aac3982aa2 dropped 5 hg38 subtracks and updated the makeDoc, but the dataset list on the description page was missed, so the page disagreed with both the .ra and the facet menu: - Risk Loci in Alzheimer's and Parkinson's: 1 peak subtrack, not 2 (neuro-degen-atac/peaks.bb was the one dropped) - BrainVar: 9 signal subtracks, not 13, and the text still described tracks "for all nuclei together" -- the 4 combined-stage tracks are exactly the ones dropped, so every remaining BrainVar track carries a life stage Counts re-derived from the .ra by type (bigWig = signal, else peak): the other 7 hg38 datasets and all 9 mm10 datasets were already right, 929 and 587 total. BrainVar was also the only hg38 dataset with no citation, while its methods text had grown quite specific. It now cites Werling et al. 2020, which described the cohort, with the caveat that the single-nucleus data shown here were not part of that paper -- the same distinction the Cell Browser desc.conf makes. Citing it bare would credit a bulk RNA-seq and WGS study for 10x Multiome data. References are alphabetical, so hg38 is now 10 and mm10 still 7. copySingleCellSignalsPeaksFiles.py: colors_json() reads the palette in a with block, and a malformed R,G,B row now raises the SystemExit the rest of the file uses, naming the file, line number and offending field, instead of a bare ValueError or TypeError from int() or the %02X format. Output is unchanged. makeDoc: the mm10 cell-class note now records that the bare "Progenitor" row in celltype-class.tsv is live rather than leftover -- build_stanzas' class_key() collapses plurals, so BrainVar's "Progenitors" looks up under the singular key and takes its class and color from that one row. Retiring or qualifying the row would grey out that track. A non-neural label needs a specific cell type added instead, which is how "Nephron progenitors" comes out Stromal. Also fixed a contradiction there: the file claimed celltype-class.tsv is built by build_celltype_crosswalks.py a hundred lines above the note saying, correctly, that it is hand-curated and not generated. The submitters confirmed BrainVar is 100 bp tiles, so the 1 kb in their methods text is an error and the page is right; recorded in the hg38 makeDoc. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> diff --git src/hg/makeDb/trackDb/human/hg38/singleCellSignalsPeaks.html src/hg/makeDb/trackDb/human/hg38/singleCellSignalsPeaks.html index b32c0c681fa..b6a12c180b0 100644 --- src/hg/makeDb/trackDb/human/hg38/singleCellSignalsPeaks.html +++ src/hg/makeDb/trackDb/human/hg38/singleCellSignalsPeaks.html @@ -1,335 +1,349 @@ <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 (bigBed and narrowPeak), split out by cell type. The datasets cover several human tissues, including brain, heart, and retina. </p> <p> The subtracks come from these datasets: </p> <ul> <li> <b><a href="https://cells.ucsc.edu/?ds=human-enhancer-atlas" target="_blank">Human Enhancer Atlas</a></b>: chromatin accessibility across 30 adult and 15 fetal human tissue types; the study mapped about 1.2 million candidate cis-regulatory elements across 222 cell types. 222 signal and 222 peak subtracks (Zhang et al. 2021). </li> <li> <b><a href="https://cells.ucsc.edu/?ds=cortex-atac" target="_blank">Cortex ATAC</a></b>: single-cell epigenomes of the developing human brain and cortical organoids. 12 signal and 69 peak subtracks (Ziffra et al. 2021). </li> <li> <b><a href="https://cells.ucsc.edu/?ds=retina" target="_blank">Human and Mouse Retina Cell Atlas</a></b>: accessibility across retinal cell types. 39 signal and 30 peak subtracks (Li et al. 2023, preprint). </li> <li> <b><a href="https://cells.ucsc.edu/?ds=neuro-degen-atac" target="_blank">Risk Loci in Alzheimer's and Parkinson's</a></b>: accessibility across six adult human brain regions, with 359,022 cell type-specific regulatory elements used to interpret disease-associated - variants. 64 signal and 2 peak subtracks (Corces et al. 2020). + variants. 64 signal and 1 peak subtrack (Corces et al. 2020). </li> <li> <b><a href="https://cells.ucsc.edu/?ds=multiomic-human-heart" target="_blank">Multiomic Human Heart</a></b>: accessibility across human cardiac development, aging, and disease, paired with expression data. 40 signal subtracks (Gao et al. 2026). </li> <li> <b><a href="https://cells.ucsc.edu/?ds=cardiogenesis-atac" target="_blank">Human Cardiogenesis</a></b>: accessibility of human fetal hearts at three early developmental stages, used to prioritize noncoding variants in congenital heart disease. 19 signal subtracks (Ameen et al. 2022). </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; the tracks here are the accessibility measured in oligodendroglia from adult human brain. 18 signal subtracks (Meijer et al. 2022). </li> <li> <b><a href="https://cells.ucsc.edu/?ds=brainvar+gene-activity" target="_blank">BrainVar</a></b>: gene-activity signal from 10x Multiome of human prefrontal cortex across 119 donors, sampling the frontal cerebral wall prenatally and the - dorsolateral prefrontal cortex postnatally. Tracks are given for all nuclei - together and for the prenatal and postnatal subsets separately; progenitors - are present in the prenatal subset only. 13 signal subtracks. + dorsolateral prefrontal cortex postnatally. Tracks are given separately for + the prenatal and postnatal subsets; progenitors are present in the prenatal + subset only. 9 signal subtracks. The BrainVar cohort was described by + Werling et al. 2020, which paired whole-genome sequencing with bulk RNA-seq + of the same developmental window; the single-nucleus data shown here extend + that resource to single-cell resolution and were not part of that paper. </li> <li> <b><a href="https://cells.ucsc.edu/?ds=sea-ad-mtg+cohort" target="_blank">SEA-AD Brain ATAC</a></b>: chromatin accessibility per cell subclass across the spectrum of Alzheimer's disease neuropathological change, from middle temporal gyrus and prefrontal cortex. 184 signal subtracks (Gabitto et al. 2024; Hawrylycz et al. 2024). </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> All signal subtracks that are turned on share one vertical scale, set from the data in the window being viewed, so tracks drawn at the same position can be compared directly. Because each dataset was normalized by its own pipeline, comparisons are most meaningful between subtracks of the same dataset. </p> <p> Most subtracks are marked <em>Healthy</em> in the condition facet. The SEA-AD brain tracks instead carry an ADNC level, short for Alzheimer's disease neuropathological change. This is the NIA-AA score that combines the anatomical distribution of amyloid plaques (Thal phase), the stage of neurofibrillary tangle spread (Braak stage), and neuritic plaque density (CERAD) into four ordinal levels, shown here as ADNC 0 (no AD), ADNC 1 (low), ADNC 2 (intermediate), and ADNC 3 (high). ADNC grades neuropathology rather than symptoms: donors with high ADNC do not necessarily have dementia, and SEA-AD records cognitive status as a separate measure. </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 mouse 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 other neural progenitors</td></tr> <tr><th style="background-color:rgb(0,158,115);width:2em"> </th> <td>Astrocyte: central nervous system astroglia, including fibrous and protoplasmic astrocytes, Bergmann glia, and retinal Müller glia</td></tr> <tr><th style="background-color:rgb(204,121,167);width:2em"> </th> <td>Oligodendrocyte: oligodendrocytes at 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(88,160,88);width:2em"> </th> <td>Other glia: glia that fit none of the glial classes above, such as olfactory ensheathing cells and clusters labeled only as glia</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: mixed types, and types that fit none of the classes above, such as peripheral glia (Schwann and enteric) 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 details. </p> <table class="stdTbl"> <tr><th>Dataset</th><th>Assay</th><th>Processing summary</th></tr> <tr> <td>Human Enhancer Atlas</td> <td>single-cell ATAC-seq (adult and fetal tissues)</td> <td>Accessibility profiled across 30 adult and 15 fetal tissues, integrated to call about 1.2 million candidate cis-regulatory elements across 222 cell types.</td> </tr> <tr> <td>Cortex ATAC</td> <td>10x single-cell ATAC-seq</td> <td>Developing human cortex and organoid nuclei clustered by accessibility; gene-activity signal derived from accessibility near genes.</td> </tr> <tr> <td>Human and Mouse Retina Cell Atlas</td> <td>single-nucleus ATAC-seq</td> <td>Retinal nuclei clustered into cell types, with per-cell-type coverage and peak calls.</td> </tr> <tr> <td>Risk Loci in Alzheimer's and Parkinson's</td> <td>10x single-cell ATAC-seq</td> <td>70,631 nuclei from six adult brain regions clustered by iterative LSI; peaks called per cluster with MACS2 (501 bp summits, blacklist-filtered), giving 359,022 cell type-specific elements.</td> </tr> <tr> <td>Multiomic Human Heart</td> <td>single-nucleus ATAC-seq (with snRNA-seq)</td> <td>106 snATAC datasets processed with SnapATAC2, batch-corrected by donor and study, integrated with expression from 299 donors.</td> </tr> <tr> <td>Human Cardiogenesis</td> <td>single-cell ATAC-seq</td> <td>Human fetal hearts from three early stages profiled to map dynamic regulatory elements across cardiac differentiation trajectories.</td> </tr> <tr> <td>Oligodendrocytes in Mouse EAE Model of MS</td> <td>single-cell ATAC-seq</td> <td>Accessibility of the oligodendrocyte lineage; the human tracks show accessibility in oligodendroglia from adult human brain.</td> </tr> <tr> <td>BrainVar</td> <td>10x Multiome (single-nucleus ATAC-seq with snRNA-seq)</td> <td>Nuclei from 119 postmortem donors processed with ArchR, which computed per-gene activity scores from local chromatin accessibility; cells were integrated with Harmony and annotated by label transfer from the paired snRNA-seq. Signal tracks are pseudobulk coverage built with the ArchR getGroupBW() function, one per broad cell type per developmental stage, using 100 bp tiles and at most 10,000 cells per group, normalized by ReadsInTSS.</td> </tr> <tr> <td>SEA-AD Brain ATAC</td> <td>single-nucleus ATAC-seq</td> <td>Pseudobulk accessibility per cell subclass across donors spanning the full range of Alzheimer's disease neuropathological change, from middle temporal gyrus and prefrontal cortex.</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/hg38/singleCellSignalsPeaks.txt" target="_blank">makeDoc</a>. </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/hg38/bbi/singleCellSignalsPeaks/" target="_blank">http://hgdownload.soe.ucsc.edu/gbdb/hg38/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=1000000 -end=1100000 \ http://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/singleCellSignalsPeaks/cardiogenesis-atac/hub/in-vivo/Veins.bw \ stdout bigBedToBed -chrom=chr21 -start=0 -end=48000000 \ http://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/singleCellSignalsPeaks/cortex-atac/hub/peaks.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. For questions about a particular subtrack, start with the dataset page it links to. </p> <h2>References</h2> <p> Ameen M, Sundaram L, Shen M, Banerjee A, Kundu S, Nair S, Shcherbina A, Gu M, Wilson KD, Varadarajan A <em>et al</em>. <a href="https://linkinghub.elsevier.com/retrieve/pii/S0092-8674(22)01503-3" target="_blank"> Integrative single-cell analysis of cardiogenesis identifies developmental trajectories and non- coding mutations in congenital heart disease</a>. <em>Cell</em>. 2022 Dec 22;185(26):4937-4953.e23. PMID: <a href="https://www.ncbi.nlm.nih.gov/pubmed/36563664" target="_blank">36563664</a>; PMC: <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10122433/" target="_blank">PMC10122433</a> </p> <p> Corces MR, Shcherbina A, Kundu S, Gloudemans MJ, Frésard L, Granja JM, Louie BH, Eulalio T, Shams S, Bagdatli ST <em>et al</em>. <a href="https://doi.org/10.1038/s41588-020-00721-x" target="_blank"> Single-cell epigenomic analyses implicate candidate causal variants at inherited risk loci for Alzheimer's and Parkinson's diseases</a>. <em>Nat Genet</em>. 2020 Nov;52(11):1158-1168. PMID: <a href="https://www.ncbi.nlm.nih.gov/pubmed/33106633" target="_blank">33106633</a>; PMC: <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7606627/" target="_blank">PMC7606627</a> </p> <p> Gabitto MI, Travaglini KJ, Rachleff VM, Kaplan ES, Long B, Ariza J, Ding Y, Mahoney JT, Dee N, Goldy J <em>et al</em>. <a href="https://doi.org/10.1038/s41593-024-01774-5" target="_blank"> Integrated multimodal cell atlas of Alzheimer's disease</a>. <em>Nat Neurosci</em>. 2024 Dec;27(12):2366-2383. PMID: <a href="https://www.ncbi.nlm.nih.gov/pubmed/39402379" target="_blank">39402379</a>; PMC: <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11614693/" target="_blank">PMC11614693</a> </p> <p> Gao W, Hu P, Wick B, Qiu Q, Zhang H, Li Y, Kang X, Bedi K, Haeussler M, Sasaki K <em>et al</em>. <a href="https://genomebiology.biomedcentral.com/articles/10.1186/s13059-026-04061-7" target="_blank"> An integrative single-nucleus multiomic atlas of the human left ventricle identifies gene regulatory network dynamics across cardiac development, aging, and disease</a>. <em>Genome Biol</em>. 2026 Apr 6;27(1). PMID: <a href="https://www.ncbi.nlm.nih.gov/pubmed/41937210" target="_blank">41937210</a>; PMC: <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13067603/" target="_blank">PMC13067603</a> </p> <p> Hawrylycz M, Kaplan ES, Travaglini KJ, Gabitto MI, Miller JA, Ng L, Close JL, Hodge RD, Long B, Mollenkopf T <em>et al</em>. <a href="https://doi.org/10.1038/s43587-024-00719-8" target="_blank"> SEA-AD is a multimodal cellular atlas and resource for Alzheimer's disease</a>. <em>Nat Aging</em>. 2024 Oct;4(10):1331-1334. PMID: <a href="https://www.ncbi.nlm.nih.gov/pubmed/39402332" target="_blank">39402332</a>; PMC: <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11577961/" target="_blank">PMC11577961</a> </p> <p> Li J, Wang J, Ibarra IL, Cheng X, Luecken MD, Lu J, Monavarfeshani A, Yan W, Zheng Y, Zuo Z <em>et al</em>. <a href="https://www.biorxiv.org/content/10.1101/2023.11.07.566105v1" target="_blank"> Integrated multi-omics single cell atlas of the human retina</a>. <em>bioRxiv</em>. 2023 Nov 8. </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> +Werling DM, Pochareddy S, Choi J, An JY, Sheppard B, Peng M, Li Z, Dastmalchi C, Santpere G, Sousa +AMM <em>et al</em>. +<a href="https://linkinghub.elsevier.com/retrieve/pii/S2211-1247(20)30407-9" target="_blank"> +Whole-Genome and RNA Sequencing Reveal Variation and Transcriptomic Coordination in the Developing +Human Prefrontal Cortex</a>. +<em>Cell Rep</em>. 2020 Apr 7;31(1):107489. +PMID: <a href="https://www.ncbi.nlm.nih.gov/pubmed/32268104" target="_blank">32268104</a>; PMC: <a +href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7295160/" target="_blank">PMC7295160</a> +</p> + <p> Zhang K, Hocker JD, Miller M, Hou X, Chiou J, Poirion OB, Qiu Y, Li YE, Gaulton KJ, Wang A <em>et al</em>. <a href="https://linkinghub.elsevier.com/retrieve/pii/S0092-8674(21)01279-4" target="_blank"> A single-cell atlas of chromatin accessibility in the human genome</a>. <em>Cell</em>. 2021 Nov 24;184(24):5985-6001.e19. PMID: <a href="https://www.ncbi.nlm.nih.gov/pubmed/34774128" target="_blank">34774128</a>; PMC: <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8664161/" target="_blank">PMC8664161</a> </p> <p> Ziffra RS, Kim CN, Ross JM, Wilfert A, Turner TN, Haeussler M, Casella AM, Przytycki PF, Keough KC, Shin D <em>et al</em>. <a href="https://doi.org/10.1038/s41586-021-03209-8" target="_blank"> Single-cell epigenomics reveals mechanisms of human cortical development</a>. <em>Nature</em>. 2021 Oct;598(7879):205-213. PMID: <a href="https://www.ncbi.nlm.nih.gov/pubmed/34616060" target="_blank">34616060</a>; PMC: <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8494642/" target="_blank">PMC8494642</a> </p>