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1,434 results for “subtypes”
Novel multi-omics deconfounding variational autoencoders can obtain meaningful disease subtyping
<h3>TCGA pan-cancer mRNA and DNA data augmented with artificial confounders utilised in "Novel multi-omics deconfounding variational autoencoders can obtain meaningful disease subtyping" by Zuqi Li and Sonja Katz (manuscript in preparation).</h3> <p>The following data curation steps were carried out: </p> <ul> <li><strong>Step 1. Download data from TCGA</strong> <ul> <li>R package `TCGAbiolinks`</li> <li>2547 patients (after step 2) with 6 cancer types: <ul> <li>BRCA (731)</li> <li>THCA (408)</li> <li>BLCA (387)</li> <li>LUSC (297)</li> <li>HNSC (412)</li> <li>KIRC (312)</li> </ul> </li> <li>mRNA expression profiles</li> <li>DNAm expression profiles</li> <li>Clinical data: <ul> <li>tumor stage: i, ia, ib, ii, iia, iib, iii, iiia, iiib, iiic, iv, iva, ivb, ivc, x</li> <li>age at diagnosis</li> <li>race: 'white', 'black or african amarican', 'asian', 'american indian or alaska native'</li> <li>gender<br><br></li> </ul> </li> </ul> </li> <li><strong>Step 2. Removal criteria</strong> <ul> <li>Patients with <ul> <li>NA or 'not reported' clinical data</li> <li>race 'american indian or alaska native'</li> <li>tumor stage x</li> </ul> </li> <li>mRNA and DNAm probes with <ul> <li>0 variance across all included patients</li> <li>not shared across all cancer types</li> <li>with missing values<br><br></li> </ul> </li> </ul> </li> <li> <strong>Step 3. Encode clinical vairables and save datasets</strong> <ul> <li>mRNA dataset: 2547 patients x 58,456 mRNAs</li> <li>DNAm dataset: 2547 patients x 232,088 DNAm</li> <li>clinic dataset: 2547 patients x 6 variables<br> 1. patient ID<br> 2. tumor stage: 1, 1, 1, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4<br> 3. age at diagnosis<br> 4. race: asian(1), black or african amarican(2), white(3)<br> 5. gender: female(0), male(1)<br> 6. cancer type: BRCA(1), THCA(2), BLCA(3), LUSC(4), HNSC(5), KIRC(6)<br> </li> </ul> </li> <li><strong> Step 4. Pre-process the datasets</strong> <ul> <li>mRNA dataset: '<em>TCGA_mRNAs_processed.csv'</em><br> <ul> <li>Take the 2000 mRNAs with highest variance</li> <li>Rescale every feature to [0,1]</li> <li>--> 2547 patients x 2000 mRNAs</li> </ul> </li> <li>DNAm dataset: <em>'TCGA_DNAm_processed.csv'</em><br> <ul> <li>Take the 2000 DNAm with highest variance</li> <li>Rescale every feature to [0,1]</li> <li>--> 2547 patients x 2000 DNAm</li> </ul> </li> <li>clinic dataset:<em> 'TCGA_clinic.csv'<br><br></em></li> </ul> </li> <li><strong>Step 5. Simulate confounders (instructions can be found in Methods section of manuscript)</strong> <ul> <li>Linear confounder: <ul> <li><em>'TCGA_confounder_linear.csv' -</em> linear confounding classes<em><br></em></li> <li><em>'TCGA_DNAm_confounded_linear.csv' </em>- linearly confounded DNAm data<em><br></em></li> <li><em>'TCGA_mRNA2_confounded_linear.csv' </em> - linearly confounded mRNA data<em><br></em></li> </ul> </li> <li>Squared confounder <ul> <li><em>'TCGA_confounder.csv' -</em> squared confounding classes<em><br></em></li> <li><em>'TCGA_DNAm_confounded.csv' </em>- squared confounded DNAm data<em><br></em></li> <li><em>'TCGA_mRNA2_confounded.csv' </em> - squared confounded mRNA data</li> </ul> </li> <li>Categorical confounder <ul> <li><em>'TCGA_confounder_categ2.csv' -</em> categorical confounding classes<em><br></em></li> <li><em>'TCGA_DNAm_confounded_categ2.csv' </em>- categorically confounded DNAm data<em><br></em></li> <li><em>'TCGA_mRNA2_confounded_categ2.csv' </em> - categorically confounded mRNA data</li> </ul> </li> <li>Multiple confounders - combined effect (linear + squared + categorical)<br> <ul> <li><em>'TCGA_confounder_multi.csv' -</em> confounding classes for combined effect<em><br></em></li> <li><em>'TCGA_DNAm_confounded_multi.csv' </em>- DNAm data with combined effect<em><br></em></li> <li><em>'TCGA_mRNA2_confounded_multi.csv' </em> - mRNA data with combined effect</li> </ul> </li> </ul> </li> </ul> <p> </p> <p> </p>
Study of catenal separation in the herbaceous layer of South African Savanna subtypes.
This dataset provides a comprehensive inventory of herbaceous plant community composition and absolute density along topographic (catenal) gradients in the South African savanna. Data were collected across three distinct reserves representing diverse ecological zones: Letlapa Pula Game Reserve (LPGR; Central Bushveld bioregion), Selati Game Reserve (SGR; Mopane bioregion), and Kempiana Nature Reserve (KNR; Lowveld bioregion). The study utilized a nested hierarchical sampling design to quantify the distribution of grasses and forbs across three primary terrain units: Crest, Midslope, and Footslope. In each reserve, 90 plots of 2 x 2 m were surveyed (30 plots per terrain unit, nested within 40 x 40 m quadrats), resulting in a total of 270 sampling units. The dataset includes counts of individual plants per species (108 species in LPGR, 93 species in SGR, and 74 species in KNR). The dataset is organized into three CSV files, one for each study area, containing: 1. Bioregion and Site Identifiers: Locating the data within the South African National Biodiversity Institute (SANBI) framework. 2. Topographic Context: Classification by catenal position (Crest, Midslope, Footslope). 3. Species Abundance Matrix: Absolute density counts of all identified herbaceous species. This data is intended to support research into catenal separation, environmental filtering, beta diversity, and the functional role of the herbaceous layer in savanna ecosystem resilience. It provides a baseline for understanding how local topography and regional climatic factors interact to shape plant community structure.
Sample data for evaluating Scholix relationship SubTypes for linked data publications
<p>Scholix links provide a standardized framework for establishing connections between research publications and their associated datasets or related data publications, thereby fostering improved discoverability, reusability, and reproducibility of research data.<br><br>This dataset aims to facilitate the evaluation of the degree of relatedness between literature publications and their associated linked data publications. It comprises 3,600 tuples, each representing a pair of a literature publication (A) and a linked data publication (B) connected through Scholix links.</p> <p><strong>Dataset Contents</strong></p> <p>1. <em>Scholix Links</em>: The dataset includes 450 Scholix links for each of the eight most frequently observed relationship types between literature and linked data publications, as expressed in the "RelationshipType - SubType" field of Scholix metadata:</p> <ul> <li>IsSupplementedBy</li> <li>IsReferencedBy</li> <li>IsRelatedTo</li> <li>References</li> <li>Documents</li> <li>Cites</li> <li>IsSupplementTo</li> <li>IsCitedBy</li> </ul> <p>2. <em>Publication Metadata</em>: In addition to the Scholix links, the dataset is augmented with metadata for each publication, including titles and author names. This metadata was harvested from the Crossref and DataCite APIs.</p> <p>3. <em>Relatedness Measures</em>: To estimate the degree of relatedness between literature and linked data publications, the dataset includes numeric measures for the similarity of authors' lists and publication titles for each tuple.</p> <p><strong>Data Sources</strong></p> <ul> <li>Scholix links were harvested from the Scholexplorer API.</li> <li>Publication metadata (titles and author names) were obtained from the Crossref and DataCite APIs.</li> </ul> <p>This dataset can be valuable for researchers and practitioners working on linked literature and data publications, evaluating the quality of existing links, or developing algorithms to identify related publications across different domains.</p>
Supplementary files for Molecular Differences Between Squamous Cell Carcinoma and Adenocarcinoma Cervical Cancer Subtypes: Potential Prognostic Biomarkers
<p>Supplementary files for Molecular Differences Between Squamous Cell Carcinoma and Adenocarcinoma Cervical Cancer Subtypes: Potential Prognostic Biomarkers</p>
Gene expression count matrix for 4 T cell subtypes from ROSMAP participants
<p><span>Peripheral blood mononuclear cells (PBMCs) from participants in the Rush Religious Orders Study/Memory and Aging Project (ROSMAP) were isolated by Ficoll gradient centrifugation, then sorted by high-speed flow cytometry into the following T cell subtypes:<span> </span>CD4+CD45RO-, CD4+CD45RO+, CD8+CD45RO-, and CD8+CD45RO+.<span> </span>Total RNA was extracted using buffer TCL (Qiagen), then RNA-seq libraries were prepared according to the Single Cell RNA Barcoding and Sequencing method originally developed for single-cell RNA-seq</span><span>, adapted for extracted total RNA.<span> </span>RNA libraries were collected on a single 384-well plate and sequenced on the Illumina HiSeq </span><span>using the High-throughput 3<span>’</span> Digital Gene Expression (DGE) library</span><span>.<span> The "RNA count matrix" file is the raw counts from the 384-well plate, while the "ROSMAP_Tcell_DGE_PlateMap" file contains metadata for the wells on the plate, by well position.</span></span></p>
Transcriptomic analyses of normal-appearing CNS white matter from multiple sclerosis donors reveal subtype-specific molecular signatures of disease (REVISED)
<p>Datasets of bulk RNA-sequencing of NAWM from MS donors + supplementary images of RNA deconvolution of cell trajectories</p>
Generation of beta-like cell subtypes from differentiated human induced pluripotent stem cells in 3D spheroids
<p>This repository contains single-cell RNA-sequencing data files (raw FASTQ files generated from Illumina HiSeq sequencing) related to the article entitled "Generation of beta-like cell subtypes from differentiated human induced pluripotent stem cells in 3D spheroids" by Lisa Morisseau et al. (2023) published in the Molecular Omics journal (DOI: 10.1039/d3mo00050h).</p> <p> </p>
WorldCOM Deliverable 1: Prevalence of ESBL subtypes in bacterial pathogens and a sequence database of selected alleles
<p><strong>OHEJP Project: WorldCOM, Deliverable 1, Work Package 1.</strong></p> <p>This dataset is connected to Work Package 1, Task1 of the WorldCOM consortium grant within the One Health EJP group. The aim was to analyse publicly available sequences for antimicrobial resistance genes associated with <em>Salmonella</em>, <em>Campylobacter</em> and <em>E. coli</em>. For the initial phase of this work package, we have focused on ESBL-related AMR genes. As these genes are absent from <em>Campylobacter</em>, we have not included this bacterium in these analyses, and have used the important pathogens <em>Klebsiella</em> and <em>Acinetobacter</em>. All types and subtypes of Extended Spectrum β-Lactamases (ESBLs) and plasmid-mediated colistin resistance genes have been analysed for frequency among reported and extracted sequences. High frequency resistant genes subtypes have been highlighted for further sequence analysis to illustrate geographic distribution and geographic-specific single nucleotide polymorphisms (SNPs). The data shown are work in progress. </p>
High-Throughput Sequencing of Human Immunoglobulin Variable Regions with Subtype Identification
<p>Raw Illumina MiSeq data in zipped FASTQ format. The data set includes demultiplexed samples from three different time points (_wk*_) of patient ZA159 (159_*), samples from four different preps of a healthy donor (HD1_*), and samples from IgG subtype sorted cells of a healthy donor (HD3_*). Every sample consists of forward (_R1_), reverse (_R2_) and index read 1 (_I1_). </p>
Supplementary File 7 from: Rapier-Sharman N et. al., Secondary Transcriptomic Analysis of Triple-Negative Breast Cancer Reveals Reliable Universal and Subtype-Specific Mechanistic Markers, 2024
<p>Supplementary Materials File 7. Please note that though the order of the supplementary materials has changed since initial upload (File S7 was previously File S9 or S10), the contents of this zipped folder remain the same.</p>
Supplemental Movie 4: Subtypes of ICC-IM with different Ca2+ firing patterns in the IAS.
<p><strong><span>Supplemental Movie 4:<span> </span>S</span>ubtypes of ICC-IM with different Ca<sup>2+</sup> firing patterns in the IAS</strong></p> <p><span>Video from the distal edge of the internal anal sphincter (IAS) from a mouse expressing GCaMP6f exclusively in ICC using a 20x objective. Active ICC-IM show 2 patterns of Ca<sup>2+</sup> transients.<span> </span>Type I cells (* and green text) displayed stochastic Ca<sup>2+</sup> transients with short distances of spatial spread.<span> </span>Type II cells (* and yellow text) showed whole-cell flashes of activity. The still image and spatio-temporal (ST) maps (derived from the highlighted cells) and Ca<sup>2+</sup> traces shown in Fig. 20A-E were generated from this recording.<span> </span>Data correspond to figure in reference </span><span><span>(136)</span></span><span>.<span> </span></span></p>
Subtyping of common complex diseases and disorders by integrating heterogeneous data. Identifying clusters among women with lower urinary tract symptoms in the LURN study
<p>We present a methodology for subtyping of persons with a common clinical symptom complex by integrating heterogeneous continuous and categorical data. We illustrate it by clustering women with lower urinary tract symptoms (LUTS), who represent a heterogeneous cohort with overlapping symptoms and multifactorial etiology. Data collected in the Symptoms of Lower Urinary Tract Dysfunction Research Network (LURN), a multi-center observational study, included self-reported urinary and non-urinary symptoms, bladder diaries, and physical examination data for 545 women. Heterogeneity in these multidimensional data required thorough and non-trivial preprocessing, including scaling by controls and weighting to mitigate data redundancy, while the various data types (continuous and categorical) required novel methodology using a weighted Tanimoto indices approach. Data domains only available on a subset of the cohort were integrated using a semi-supervised clustering approach. Novel contrast criterion for determination of the optimal number of clusters in consensus clustering was introduced and compared with existing criteria. Distinctiveness of the clusters was confirmed by using multiple criteria for cluster quality, and by testing for significantly different variables in pairwise comparisons of the clusters. Cluster dynamics were explored by analyzing longitudinal data at 3- and 12-month follow-up. Five clusters of women with LUTS were identified using the developed methodology. None of the clusters could be characterized by a single symptom, but rather by a distinct combination of symptoms with various levels of severity. Targeted proteomics of serum samples demonstrated that differentially abundant proteins and affected pathways are different across the clusters. The clinical relevance of the identified clusters is discussed and compared with the current conventional approaches to the evaluation of LUTS patients. The rationale and thought process are described for the selection of procedures for data preprocessing, clustering, and cluster evaluation. Suggestions are provided for minimum reporting requirements in publications utilizing clustering methodology with multiple heterogeneous data domains.</p>
Pancreatic adecarcinoma fibroblast subtype using RNAseq
<p>Cancer-associated fibroblasts (CAFs) are orchestrators of the pancreatic ductal adenocarcinoma (PDAC) microenvironment. Previously we described four CAF subtypes with specific molecular and functional features. Here, we have refined our CAF subtype signatures using RNAseq and immunostaining with the goal to define bioinformatically the phenotypic stromal and tumor epithelial states associated with CAF diversity. We used primary CAF cultures grown from patient PDAC tumors, human datasets (in-house and public, including single-cell analyses), genetically engineered mouse PDAC tissues, and patient-derived xenografts (PDX) grown in mice. We found that CAF subtype RNAseq signatures correlated with immunostaining. Tumors rich in periostin-positive CAFs were significantly associated with shorter overall survival of patients. Periostin-positive CAFs were characterized by high proliferation and protein synthesis rates, low αSMA expression, and were found in peri-/pre-tumoral areas. They were associated with highly cellular tumors and with macrophage infiltrates. Podoplanin-positive CAFs were associated with immune-related signatures and recruitment of dendritic cells. Importantly, we showed that the combination of periostin-positive CAFs and podoplanin-positive CAFs was associated with specific tumor microenvironment features in terms of stromal abundance and immune cell infiltrates. Podoplanin-positive CAFs identified an iCAF-like subset whereas periostin-positive CAFs were not correlated with the published myCAF/iCAF classification.</p> <p>Taken together, these results suggest that a periostin-positive CAF is an early, activated CAF, associated with aggressive tumors, whereas a podoplanin-positive CAF is associated with an immune-related phenotype. These two subpopulations cooperate to define specific tumor microenvironment and patient prognosis, and are of putative interest for future therapeutic stratification of patients.</p> <p> </p> <p><strong>Material and methods</strong></p> <p>Total RNA was extracted from FFPE sections using a high pure FFPE RNA isolation kit (Roche®, Basel, Switzerland) following the manufacturer’s protocol. RNA yield and quality was determined using a NanoDrop™ One spectrophotometer and fragment size was analyzed using an RNA ScreenTape assay run on a 4200 Bioanalyzer (Agilent Technologies®, Santa Clara, CA, USA . DV200 values representing the percentage of RNA fragments above 200 nucleotides in length were estimated, and cases with DV200 more than 30% were included for library preparation.<br> Library preparation was performed using QuantSeq 3’ mRNA-Seq REV (Lexogen® , Vienna, Austria) with an input of 150 ng of total FFPE RNA. The pool was sequenced on a NovaSeq 6000 system flow cell SP (Illumina Inc., San Diego, CA) using a 75-cycle, paired-end protocol providing approximately 10 million reads per sample. Base call files were converted to fastq format using Bcl2Fastq (Illumina®, San Diego, CA). All RNA-seq reads were aligned to the human reference genome (GRCh37, hg19) using STAR (version 2.6.1a_08-27), quantified using FeatureCount and Upper-Quartile normalized.<br> </p>
Raw diffraction images of human orexin 2 receptor bound to the subtype-selective antagonist EMPA
<p>Human orexin 2 receptor (OX2R) is a member of G protein-coupled receptors (GPCR). OX2R plays important roles in modulating feeding behavior and regulating the sleep-wake cycle.</p> <p>805 small-wedge (1-6°/crystal) datasets collected from loop-harvested microcrystals using MX225HS CCD detector at a wavelength of 1 Å on BL32XU, SPring-8. The crystals belonged to space group C2 with unit cell parameters a=94.1, b=75.5, c=95.9 Å, β=111.4°.</p> <p>631 datasets were merged at 1.96 Å resolution in the published result (Suno et al. Structure 2017; PDB code: 5WQC) using KAMO; see https://github.com/keitaroyam/yamtbx/wiki/Processing-OX2R-data-(5WQC)</p> <p>Note</p> <ul> <li>Most frames have lipid rings.</li> <li>One dataset (not included in the published result) was lost for some reason (so 804 datasets are available).</li> </ul>
No Man's Sky Patch Types and Subtypes for the article "Adapting the Harris Matrix for Software Stratigraphy"
<p>Full data set of <em>No Man's Sky</em> patch types and subtypes used in the article "Adapting the Harris Matrix for Software Stratigraphy" published in <em>Advances in Archaeological Practice</em>.</p>
Fig. 1 in A new subtype of Entamoeba gingivalis: BE. gingivalis ST2, kamaktli variant^
Fig. 1 Electrophoresis on 1.2% TBE agarose gel and ethidium bromide stained of amplicons obtained with different primer pairs (shown on the top of each line) and DNA from some clinical samples. Line 1, 100 bp DNA Ladder Molecular size Marker (MM) GeneDirex®, of 100 to 1500 bp. The primers used were GEI18SF/GE18SR (18/18) line 2, Entam1-Entam2 (E1/E2) line 3, GEI18SF-P2 (18/P2) lines 4, Entam1-RD3 (E1/RD3) lines 6 and 7, Entam1-GE18SR (E1/18) lines 8 and 9, and RD5/RD3 (RD5′-RD3′) lines 10 and 11. Lines 2, 4, and 6 did not show visible bands
Conserved angio-immune subtypes of the cancer microenvironment predict response to immune checkpoint blockade therapy
<p>Immune checkpoint blockade (ICB) therapy has revolutionized cancer treatment. However, only a fraction of the patients respond to ICB therapy. Accurate prediction of patients to likely respond to ICB would maximize the efficacy of ICB therapy. The tumor microenvironment (TME) dictates tumor progression and therapy outcome. Here, we classify the TME by analyzing the transcriptome from 11,069 cancer patients based on angiogenesis and T-cell activity. We find three distinct angio-immune TME subtypes conserved across 30 non-hematological cancers. There is a clear inverse relationship between angiogenesis and anti-tumor immunity in TME. Remarkably, patients displaying TME with low angiogenesis with strong anti-tumor immunity show the most significant responses to ICB therapy in four cancers. Re-evaluation of the renal cell carcinoma clinical trials provides compelling evidence that the baseline angio-immune state is robustly predictive of ICB responses. This study offers a rationale for incorporating baseline angio-immune scores for future ICB treatment strategies.</p>
Fig. 2 in A new subtype of Entamoeba gingivalis: BE. gingivalis ST2, kamaktli variant^
Fig. 2 Unrooted phylogenetic tree reconstruction of Entamoeba species" based on 18S rRNA sequences. The values of the nodes indicate the bootstrap proportions and Bayesian posterior probabilities in the following order: maximum likelihood/maximum parsimony/Bayesian analysis. The sequences reported by the present study are indicated in bold. The asterisks indicate a new subtype BE. gingivalis ST2, kamaktli variant.^ Bar 0.1 substitutions per site
Fig. 2 in First subtyping of Blastocystis sp. from pet rodents in southwestern China
Fig. 2. Phylogenetic relationships among nucleotide sequences of Blastocystis partial small subunit ribosomal RNA (SSU rRNA) genes. The neighbor-joining method was used to construct the trees from the Kimura-2- parameter model. Branch numbers represent percent bootstrapping values from 1000 replicates, with values of more than 50% shown in the tree. Each sequence is identified by its accession number, subtypes, host origin, and country. Blastocystis subtypes identified in the present study are indicated in bold-type.▲ are subtypes in this study.
Figure 1 in The Combined Expression Patterns of Ikaros Isoforms Characterize Different Hematological Tumor Subtypes
Figure 1. - Location of sites cited in table I, where the new records were gathered. 1) San Jorge, 2) La Poma, 3) Punta del Diablo, 4) Patos Island, 5) El Chivero, 6) La Reina, 7) La Tordilla, 8) San Pedro Mártir.
ScienceDex guides
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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.