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Fig.ç6.Ec hinoderes ohtsukai sp. nov., holotype, male (ZIHU 3976), Nomarski photomicrographs. A, Segments 1 and 2, ventral view; B, segments 4 and 5, ventral view. Abbreviations: dss, droplet-shaped sensory spot; gco1, glandular cell outlet type I; gco2, modi ed glandular cell outlet type II; lvt, lateroventral tubule; pac, pachycyclus; pf, pectinate fringe; rss, rounded sensory spot. in A New Brackish-water Species of Echinoderes (Kinorhyncha: Cyclorhagida) from the Seto Inland Sea, Japan
Fig.ç6.Ec hinoderes ohtsukai sp. nov., holotype, male (ZIHU 3976), Nomarski photomicrographs. A, Segments 1 and 2, ventral view; B, segments 4 and 5, ventral view. Abbreviations: dss, droplet-shaped sensory spot; gco1, glandular cell outlet type I; gco2, modi ed glandular cell outlet type II; lvt, lateroventral tubule; pac, pachycyclus; pf, pectinate fringe; rss, rounded sensory spot.
Fig.ç5.Ec hinoderes ohtsukai sp. nov., holotype, male (ZIHU 3976), Nomarski photomicrographs. A, Segments 1 and 2, dorsal view; B, segment 4, dorsal view. Abbreviations: dss, droplet-shaped sensory spot; gco1, glandular cell outlet type I; gco2, modi ed glandular cell outlet type II; mds, middorsal spine; pac, pachycyclus; pf, pectinate fringe; ps, perforation site; rss, rounded sensory spot. in A New Brackish-water Species of Echinoderes (Kinorhyncha: Cyclorhagida) from the Seto Inland Sea, Japan
Fig.ç5.Ec hinoderes ohtsukai sp. nov., holotype, male (ZIHU 3976), Nomarski photomicrographs. A, Segments 1 and 2, dorsal view; B, segment 4, dorsal view. Abbreviations: dss, droplet-shaped sensory spot; gco1, glandular cell outlet type I; gco2, modi ed glandular cell outlet type II; mds, middorsal spine; pac, pachycyclus; pf, pectinate fringe; ps, perforation site; rss, rounded sensory spot.
Fig.ç7.Ec hinoderes ohtsukai sp. nov., paratype, female (ZIHU 3980), Nomarski photomicrographs. A, Segments 5 and 6, ventral view; B, segments 8 and 9, ventral view. Abbreviations: dss, droplet-shaped sensory spot; gco2, modi ed glandular cell outlet type II; lvt, lateroventral tubule; si, sieve plate; sp, sternal plate; tp, tergal plate. in A New Brackish-water Species of Echinoderes (Kinorhyncha: Cyclorhagida) from the Seto Inland Sea, Japan
Fig.ç7.Ec hinoderes ohtsukai sp. nov., paratype, female (ZIHU 3980), Nomarski photomicrographs. A, Segments 5 and 6, ventral view; B, segments 8 and 9, ventral view. Abbreviations: dss, droplet-shaped sensory spot; gco2, modi ed glandular cell outlet type II; lvt, lateroventral tubule; si, sieve plate; sp, sternal plate; tp, tergal plate.
Fig.ç3.Ec hinoderes ohtsukai sp. nov., scanning electron micrographs. A, B, Paratype, female (ZIHU 3983); C–E, paratype, male (ZIHU 3982). A, General habitus, lateral view; B, neck and segments 1–4, lateral view; C, enlargement of segment 7, lateral view; D, enlargement of segment 9, lateral view; E, enlargement of segments 10 and 11, lateroventral view. Abbreviations: ch, cuticular hair; dss, droplet-shaped sensory spot; gco2, modi ed glandular cell outlet type II; ldt, laterodorsal tubule; pf, pectinate fringe; po, pore; ps1, penile spine 1; ps2, penile spine 2; ps3, penile spine 3; rss, rounded sensory spot; si, sieve plate; ss, sensory spot. in A New Brackish-water Species of Echinoderes (Kinorhyncha: Cyclorhagida) from the Seto Inland Sea, Japan
Fig.ç3.Ec hinoderes ohtsukai sp. nov., scanning electron micrographs. A, B, Paratype, female (ZIHU 3983); C–E, paratype, male (ZIHU 3982). A, General habitus, lateral view; B, neck and segments 1–4, lateral view; C, enlargement of segment 7, lateral view; D, enlargement of segment 9, lateral view; E, enlargement of segments 10 and 11, lateroventral view. Abbreviations: ch, cuticular hair; dss, droplet-shaped sensory spot; gco2, modi ed glandular cell outlet type II; ldt, laterodorsal tubule; pf, pectinate fringe; po, pore; ps1, penile spine 1; ps2, penile spine 2; ps3, penile spine 3; rss, rounded sensory spot; si, sieve plate; ss, sensory spot.
Fig.ç2.Ec hinoderes ohtsukai sp. nov., camera lucida drawings. A, B, Holotype, male (ZIHU 3976), entire animal, dorsal and ventral view, respectively; C, D, allotype, female (ZIHU 3977), segments 9–11, dorsal and ventral view, respectively. Abbreviations: dss, droplet-shaped sensory spot; gco1, glandular cell outlet type I; gco2, modi ed glandular cell outlet type II; ldt, laterodorsal tubule; lts, lateral terminal spine; lvt, lateroventral tubule; mds, middorsal spine; ne, neck; ps, penile spine; rss, rounded sensory spot; si, sieve plate. in A New Brackish-water Species of Echinoderes (Kinorhyncha: Cyclorhagida) from the Seto Inland Sea, Japan
Fig.ç2.Ec hinoderes ohtsukai sp. nov., camera lucida drawings. A, B, Holotype, male (ZIHU 3976), entire animal, dorsal and ventral view, respectively; C, D, allotype, female (ZIHU 3977), segments 9–11, dorsal and ventral view, respectively. Abbreviations: dss, droplet-shaped sensory spot; gco1, glandular cell outlet type I; gco2, modi ed glandular cell outlet type II; ldt, laterodorsal tubule; lts, lateral terminal spine; lvt, lateroventral tubule; mds, middorsal spine; ne, neck; ps, penile spine; rss, rounded sensory spot; si, sieve plate.
Рис. 2. Продольный (А–Д) и поперечный (Е–З) среЗы череЗ наружный покров ноги моллюска с раЗными типами складок: А, Б – Широкие складки в виде плато, В, Г – длинные иЗвилистые складки, Д, Е – складки с округлыми и бокаловидными клетками в субЭпителиальном слое, Ж, З – слабовыраженные складки с больШими полостЯми (синусами) длЯ гемолимфы под субЭпителиальным слоем. МасШтабные линейки 200 мкм (А, В, Ж, З) и 100 мкм (Б, Г–Е). вК – клетки с вакуолЯми, сэ – субЭпителиальный слой, БК – бокаловиднаЯ клетка, сг – синусы длЯ гемолимфы, ф – фолликулы, а – ацинусы. Fig. 2. Saggital (А–Д) and transverse (Е–З) sections of pedal integument with different types of plicae: А, Б – broad plateau-shaped plicae, В, Г – long, tortuous plicae, Д, Е – plicae with round and goblet cells in the subepithelial layer, Ж, З – mild plicae with large cavities (sinuses) for hemolymph under subepithelial layer. Scale bars 200 µm (А, В, Ж, З) and 100 µm (Б, Г–Е). вК – cells with vacuoles, сэ – subepithelial layer, БК – goblet cell, сг – sinuses for hemolymph, ф – follicles, а – acini. in Nodularia vladivostokensis (Bivalvia: Unionidae) from Razdolnaya River (Primorye, Russia)
Рис. 2. Продольный (А–Д) и поперечный (Е–З) среЗы череЗ наружный покров ноги моллюска с раЗными типами складок: А, Б – Широкие складки в виде плато, В, Г – длинные иЗвилистые складки, Д, Е – складки с округлыми и бокаловидными клетками в субЭпителиальном слое, Ж, З – слабовыраженные складки с больШими полостЯми (синусами) длЯ гемолимфы под субЭпителиальным слоем. МасШтабные линейки 200 мкм (А, В, Ж, З) и 100 мкм (Б, Г–Е). вК – клетки с вакуолЯми, сэ – субЭпителиальный слой, БК – бокаловиднаЯ клетка, сг – синусы длЯ гемолимфы, ф – фолликулы, а – ацинусы. Fig. 2. Saggital (А–Д) and transverse (Е–З) sections of pedal integument with different types of plicae: А, Б – broad plateau-shaped plicae, В, Г – long, tortuous plicae, Д, Е – plicae with round and goblet cells in the subepithelial layer, Ж, З – mild plicae with large cavities (sinuses) for hemolymph under subepithelial layer. Scale bars 200 µm (А, В, Ж, З) and 100 µm (Б, Г–Е). вК – cells with vacuoles, сэ – subepithelial layer, БК – goblet cell, сг – sinuses for hemolymph, ф – follicles, а – acini.
YY1 mutations disrupt corticogenesis through a cell type-specific rewiring of cell-autonomous and non-cell-autonomous transcriptional programs
<p>This supplementary data includes counts from bulk and pseudobulk omic experiments, h5ad for single-cell experiments, and outputs of differential expression and enrichments performed on different omics assays.</p>
Datasets for CASSL: A cell-type annotation method for single cell transcriptomics data using semi-supervised learning
<p>This repository contains datasets used in the project CASSL: A cell-type annotation method for single cell transcriptomics data using semi-supervised learning. This project aims at learning cell annotations for missing cell labels via NMF and recursive k-Means clustering.</p>
Cell type specificity of glucocorticoid signaling in the adult mouse hippocampus
<p>The present study is based on the 10X scRNA-seq dataset published by the Allen Institute for Brain Science and publicly available at: <a href="https://portal.brain-map.org/atlases-and-data/RNA-seq/mouse-whole-cortex-and-hippocampus-10x">https://portal.brain-map.org/atlases-and-data/RNA-seq/mouse-whole-cortex-and-hippocampus-10x</a>. The cells from the hippocampus region were selected from the gene count expression matrix and pre-processed in R v3.6.1 according to the Seurat v3.1.5 standard pre-processing workflow for quality control, normalization, and analysis of scRNA-seq data. Here we make the final seurat object and other datasets further used in the code (<a href="https://github.com/eviho/10XHip2021_VihoEMG">https://github.com/eviho/10XHip2021_VihoEMG</a>) available for download. </p>
Harnessing single cell RNA sequencing to identify dendritic cell types, characterize their biological states and infer their activation trajectory
<p><strong>Summary: </strong>Dendritic cells (DCs) orchestrate innate and adaptive immunity, by translating the sensing of distinct danger signals into the induction of different effector lymphocyte responses, to induce different defense mechanisms suited to face distinct types of threats. Hence, DCs are very plastic, which results from two key characteristics. First, DCs encompass distinct cell types specialized in different functions. Second, each DC type can undergo different activation states, fine-tuning its functions depending on its tissue microenvironment and the pathophysiological context, by adapting the output signals it delivers to the input signals it receives. Hence, to better understand DC biology and harness it in the clinic, we must determine which combinations of DC types and activation states mediate which functions, and how.<br> To decipher the nature, functions and regulation of DC types and their physiological activation states, one of the methods that can be harnessed most successfully is ex vivo single cell RNA sequencing (scRNAseq). However, for new users of this approach, determining which analytics strategy and computational tools to choose can be quite challenging, considering the rapid evolution and broad burgeoning of the field. In addition, awareness must be raised on the need for specific, robust and tractable strategies to annotate cells for cell type identity and activation states. It is also important to emphasize the necessity of examining whether similar cell activation trajectories are inferred by using different, complementary methods. In this chapter, we take these issues into account for providing a pipeline for scRNAseq analysis and illustrating it with a tutorial reanalyzing a public dataset of mononuclear phagocytes isolated from the lungs of naïve or tumor-bearing mice. We describe this pipeline step-by-step, including data quality controls, dimensionality reduction, cell clustering, cell cluster annotation, inference of the cell activation trajectories and investigation of the underpinning molecular regulation. It is accompanied with a more complete tutorial on Github. We anticipate that this method will be helpful for both wet lab and bioinformatics researchers interested in harnessing scRNAseq data for deciphering the biology of DCs or other cell types, and that it will contribute to establishing high standards in the field.</p> <p> </p> <p><strong>Data:</strong></p> <p>1. negative_cDC1_relative_signatures.csv : Negative signatures for performing Connectivity Map (cMAP) Analysis</p> <p>2. positive_cDC1_relative_signatures.csv : Positive signatures for performing Connectivity Map (cMAP) Analysis</p>
# Single-cell network biology characterizes cell type gene regulation for drug repurposing and phenotype prediction in Alzheimer's disease
<p>Dysregulation of gene expression in Alzheimer’s disease (AD) remains elusive, especially at the cell type level. Gene regulatory network, a key molecular mechanism linking transcription factors (TFs) and regulatory elements to govern target gene expression, can change across cell types in the human brain and thus serve as a model for studying gene dysregulation in AD. However, it is still challenging to understand how cell type networks work abnormally under AD. To address this, we integrated single-cell multi-omics data and predicted the gene regulatory networks in AD and control for four major cell types, excitatory and inhibitory neurons, microglia and oligodendrocytes. Importantly, we applied network biology approaches to analyze the changes of network characteristics across these cell types, and between AD and control. For instance, many hub TFs target different genes between AD and control (rewiring). Also, these networks show strong hierarchical structures in which top TFs (master regulators) are largely common across cell types, whereas different TFs operate at the middle levels in some cell types (e.g., microglia). The regulatory logics of enriched network motifs (e.g., feed-forward loops) further uncover cell type-specific TF-TF cooperativities in gene regulation. The cell type networks are highly modular and several network modules with cell-type-specific expression changes in AD pathology are enriched with AD-risk genes and putative targets of approved and pending AD drugs, suggesting possible cell-type genomic medicine in AD. Finally, using the cell type gene regulatory networks, we developed machine learning models to classify and prioritize additional AD genes. We found that top prioritized genes predict clinical phenotypes (e.g., cognitive impairment) with reasonable accuracy. Overall, this single-cell network biology analysis provides a comprehensive map linking genes, regulatory networks, cell types and drug targets and reveals dysregulated cell type gene dysregulatory mechanisms in AD.</p>
SCADIE: simultaneous estimation of cell type proportions and cell type-specific gene expressions using SCAD-based iterative estimating procedure
<p>This repository contains the source code and data used in the paper: SCADIE: simultaneous estimation of cell type proportions and cell type-specific gene expressions using SCAD-based iterative estimating procedure.1.</p>
Text-fig. 14. Photomicrographs of thin sections of specimen BP/16/1732 Palmoxylon dutoitii from Mhengere Hill, Gorongosa, Mozambique. a: transverse section (TS) with four fibre vascular bundles (fvb) and poorly preserved parenchyma between; b: diagram of one of the fvbs in (a) of the reniform type (f – fibres, mx – metaxylum, p – phloem, px – protoxylum); c: close up of the vascular part of a fvb with 2 metaxylem elements and the collapsed cells to the lower left represents the phloem; d: fvb with 2 metaxylem elements, fibrous part to the left and parencymarous ground tissue to the right; e: lower magnification of fvb in (c); f: fvb with three metaxylem elements and phloem patch below; g: longitudinal section (LS) showing the vascular sections alternating with the fibrous sections; h: LS showing the horizontal thickening on the walls of the metaxylem vessels and a patch of parenchyma to the right (darker cells); i: spheroid echinate phytoliths that are typical of Hyphaene and Borassus. in Stratigraphy, Chronology And Palaeontology Of The Tertiary Rocks Of The Cheringoma Plateau, Mozambique
Text-fig. 14. Photomicrographs of thin sections of specimen BP/16/1732 Palmoxylon dutoitii from Mhengere Hill, Gorongosa, Mozambique. a: transverse section (TS) with four fibre vascular bundles (fvb) and poorly preserved parenchyma between; b: diagram of one of the fvbs in (a) of the reniform type (f – fibres, mx – metaxylum, p – phloem, px – protoxylum); c: close up of the vascular part of a fvb with 2 metaxylem elements and the collapsed cells to the lower left represents the phloem; d: fvb with 2 metaxylem elements, fibrous part to the left and parencymarous ground tissue to the right; e: lower magnification of fvb in (c); f: fvb with three metaxylem elements and phloem patch below; g: longitudinal section (LS) showing the vascular sections alternating with the fibrous sections; h: LS showing the horizontal thickening on the walls of the metaxylem vessels and a patch of parenchyma to the right (darker cells); i: spheroid echinate phytoliths that are typical of Hyphaene and Borassus.
Text-fig. 5. Mastixiopsis nyssoides KIRCHH. a, b, g–n: Organic preservation. a, b: Lignitic, unpermineralized, early Eocene Dorset Pipe clays at Arne, V. 40762. a: Ventral view (original illustration from pl. 18, fig. 1 of Chandler 1962). b: Transverse fracture, somewhat distorted by compression. c–f: Pyrite permineralization. c: Ventral view, V. 22963(1) from Sheppey, originally listed as Mastixia cantiensis. d: Lateral view, V. 22969 from Sheppey (identified as Mastixia grandis by Reid and Chandler 1933: pl. 25, fig. 8). e: Equatorial transverse physical section from (c). f: Equatorial transverse physical section from (d). g: Detail of pericarp from (e), showing endocarp formed of dense fibrous tissue, surrounded by mesocarp of anticlinally oriented larger cells. h: Detail of pericarp from (f). i–n: Type material from Eocene of Riestadt, Germany, MNB. i: Ventral view. j, k: Ventral and apical views of holotype. l: View of the transversely fractured surface from (j) showing horseshoe shaped locule. m: Equatorial transverse physical cut of the specimen in (i); note yellow resin cavity (arrow). n: Scanning electron microscopy of pericarp from (l) with locule lining at lower edge of image. Note dense endocarp tissue composed of small cells (fibres and sclereids), extending about 3/5 of distance to periphery, surrounded by mesocarp of larger, anticlinally oriented cells. Scale bars 1 cm in (a–f), (i–k), 1 mm in (g), 2 mm in (h), 3 mm in (l), m, 250 Μm in (n). Bar in (d) applies also to (c). Bar in (l) also applies to (m). Bar in (i) also applies to (j) and (k). in Mastixioid Fruits (Cornales) From The Early Eocene London Clay Flora: Morphology, Anatomy And Nomenclatural Revision
Text-fig. 5. Mastixiopsis nyssoides KIRCHH. a, b, g–n: Organic preservation. a, b: Lignitic, unpermineralized, early Eocene Dorset Pipe clays at Arne, V. 40762. a: Ventral view (original illustration from pl. 18, fig. 1 of Chandler 1962). b: Transverse fracture, somewhat distorted by compression. c–f: Pyrite permineralization. c: Ventral view, V. 22963(1) from Sheppey, originally listed as Mastixia cantiensis. d: Lateral view, V. 22969 from Sheppey (identified as Mastixia grandis by Reid and Chandler 1933: pl. 25, fig. 8). e: Equatorial transverse physical section from (c). f: Equatorial transverse physical section from (d). g: Detail of pericarp from (e), showing endocarp formed of dense fibrous tissue, surrounded by mesocarp of anticlinally oriented larger cells. h: Detail of pericarp from (f). i–n: Type material from Eocene of Riestadt, Germany, MNB. i: Ventral view. j, k: Ventral and apical views of holotype. l: View of the transversely fractured surface from (j) showing horseshoe shaped locule. m: Equatorial transverse physical cut of the specimen in (i); note yellow resin cavity (arrow). n: Scanning electron microscopy of pericarp from (l) with locule lining at lower edge of image. Note dense endocarp tissue composed of small cells (fibres and sclereids), extending about 3/5 of distance to periphery, surrounded by mesocarp of larger, anticlinally oriented cells. Scale bars 1 cm in (a–f), (i–k), 1 mm in (g), 2 mm in (h), 3 mm in (l), m, 250 Μm in (n). Bar in (d) applies also to (c). Bar in (l) also applies to (m). Bar in (i) also applies to (j) and (k).
Рис. 7. Варианты преΑсказанной Αоменной структуры моΛекуΛ аΑгезии гемоцитов моΛΛюсков Planorbarius corneus. УсΛовные обозначения и сокращения: 1–3 — β-интегрины, 4–5 — α-интегрины, 6–7 — сеΛектины, 8–11 — моΛекуΛы семейства САМ (сell adhesiom molecues), INB — субъеΑиницы β-интегрина, IntegrinBcyt — цитопΛазматический Αомен β-интегрина, CY — цистатинопоΑобный Αомен, Int alpha — Αомен α-интегрина, FN3 — Αомен фибронектина типа 3, CCP — Αомен контроΛя компΛемента Fig. 7. Variants of the predicted domain structure of adhesion molecules from hemocytes of Planorbarius corneus molluscs. Symbols and abbreviations: 1–3 — β-integrins, 4–5 — α–integrins, 6–7 — selectins, 8–11 — molecules of the СAM family (cell adhesion molecules), INB — β-integrin subunits, IntegrinBcyt — cytoplasmic domain of β-integrin, CY — cystatin-like domain, Int alpha — α-integrin domain, FN3 — fibronectin type 3 domain, CCP — complement control protein domain in Pathogen recognition molecules from hemocytes of Planorbarius corneus molluscs (Planorbidae, Pulmonata)
Рис. 7. Варианты преΑсказанной Αоменной структуры моΛекуΛ аΑгезии гемоцитов моΛΛюсков Planorbarius corneus. УсΛовные обозначения и сокращения: 1–3 — β-интегрины, 4–5 — α-интегрины, 6–7 — сеΛектины, 8–11 — моΛекуΛы семейства САМ (сell adhesiom molecues), INB — субъеΑиницы β-интегрина, IntegrinBcyt — цитопΛазматический Αомен β-интегрина, CY — цистатинопоΑобный Αомен, Int alpha — Αомен α-интегрина, FN3 — Αомен фибронектина типа 3, CCP — Αомен контроΛя компΛемента Fig. 7. Variants of the predicted domain structure of adhesion molecules from hemocytes of Planorbarius corneus molluscs. Symbols and abbreviations: 1–3 — β-integrins, 4–5 — α–integrins, 6–7 — selectins, 8–11 — molecules of the СAM family (cell adhesion molecules), INB — β-integrin subunits, IntegrinBcyt — cytoplasmic domain of β-integrin, CY — cystatin-like domain, Int alpha — α-integrin domain, FN3 — fibronectin type 3 domain, CCP — complement control protein domain
IMMUcan panel 1 cell type classification
<p>This repo contains Imaging Mass Cytometry data from 179 cancer patients collected during the IHI2 funded IMMUcan project.</p> <p> </p> <p>Background:</p> <p>IMMUcan (https://immucan.eu/) collects samples from cancer patients for multimodal analysis. Amongst those Imaging Mass Cytometry (IMC). Samples collection occurs over 6 years. For reproducible cell typing over time, a cell phenotype classifier was built from manually annotated data. The classifier was trained on a first batch of annotated images (V1) and then trained again with a second round of annotated images (V3). A final classifier was build which is used within IMMUcan to classify cell phenotypes for IMC panel 1 for all cancer patients.</p> <p> </p> <p>The dataset contains:</p> <ul> <li>sce_labelled_V1.rds_ is the `SingleCellExperiment` object containing all labelled cells from the first batch of labelling</li> <li>sce_labelled_V3.rds_ is the `SingleCellExperiment` object containing all labelled cells from the second batch of labelling</li> <li>rf_images_DCfix.rds_ is the RF model which can be used for cell type predictions</li> <li>sce_raw.rds_ is the `SingleCellExperiment` object containing all cells from the 179 images. the trained classifier has been applied to this dataset which can be used to view the classification results of cell phenotypes on images using the bioconductor packages cytomapper or cytoviewer. </li> <li>all_images.rds a `CytoImageList` object which contains all the images from all images</li> <li>all_masks.rds a `CytoImageList` object which contains all single-cell segmentation masks from all images</li> <li>a zip archive `images_masks` containing the tiff images and masks from all 179 images, a CytoImageList Object for images (all_images.rds) and a CytoListImage object for all masks (all_masks.rds)</li> </ul> <p> </p> <p> </p> <p> </p> <p> </p>
Extention to the TNBC dataset, brain section and cell type
<p>This dataset is partly an extension of the TNBC dataset that can be found in the following link: https://zenodo.org/record/1175282#.Ws2n_vkdhfA</p> <p>In particular, we give the cellular type annotations of each annotated cell.<br> In addition to the TNBC, we added 18 annotated images of brain section downloaded from the TCGA.<br> This is still officially unpublished and has been submitted.<br> Currently, if you use this dataset please our paper "Scale dependant layer for self-supervised nuclei encoding" by Peter Naylor, Yao-Hung Hubert Tsai, Marick Laé and Makoto Yamada.</p>
A Data-Driven Epigenetic Characterization of Morning Fatigue Severity in Oncology Patients Receiving Chemotherapy: Associations with Epigenetic Age Acceleration, Blood Cell Types, and Expression-Associated Methylation
<p>This dataset contains supplementary materials including the eCpG mapping analysis results and annotation. The manuscript has been accepted for publication at Cancer Medicine. Please cite both the paper as well as the DOI of this dataset if you make use of the data.</p>
Data for accurate cell type deconvolution in spatial transcriptomics using a batch effect-free strategy
<p>Simulated and experimental data used in the ReSort manuscript. It is necessary and sufficient to reproduce the results in the paper.</p>
Summary statistics of cell-type specific cis-eQTLs in eight brain cell-types
<p>This dataset contains eQTL summary statistics for all SNPs-gene pairs in 8 major brain cell types (within 1MB window surrounding the TSS of each expressed gene). For each cell type, there is one file per chromosome.</p> <p>Column description:</p> <p>1. Gene_id</p> <p>2. SNP_id</p> <p>3. Distance to TSS</p> <p>4. Nominal p-value</p> <p>5. Beta</p> <p>In addition, a file contains the SNP positions (snp_pos.txt) and tested allele.</p> <p>Update February 2023: The summary statistics from our 'tissue-like' analysis were added (pb[1-22].gz). These file contain eQTL summary statistics after aggregating all reads from all nuclei for each individual (instead of per cell type).</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.