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642 results for “Multiplexing”
Dataset: CODEX highly multiplexed tissue imaging in pancreas
<p><strong>Human pancreas</strong></p> <p>This dataset was acquired using CODEX, multiplexed single-cell imaging technology for spatial profiling, where all image data is in .tif format and it includes an associated imaging metadata .csv file. The combination of the targets present in this experiment define some of the main cell types and anatomical structures in human pancreas tissue.</p> <p>This dataset is a 12-highly multiplexed experiment performed on a human pancreas 5 μm section including the nuclear marker Hoechst and antibodies conjugated with oligo-sequences directed against the individual markers. Images were acquired using a Leica DMi8 widefield microscope, a digital CMOS camera (Hamamatsu, ORCA-Flash4.0 V3), and a 20x (0.75) NA dry objective. The light source was a SOLA-SM-II. All images were captured at a 16-bit depth with the following dimensions: x (0.325 μm), y (0.325 μm), and z (1.5 μm). In addition, images were processed, tiled and merged using the CODEX® Processor application (CODEX Processor 1.7.0.6).</p>
Multiplexed Immunofluorescence Staining Dataset - OMAP 7 - Lung, Cell DIVE
<p>This dataset contains an exemplary multiplexed immunofluorescence (MxIF) dataset for the antibody markers captured in Cell DIVE Lung OMAP (OMAP #7). The slide type is a TMA of FFPE tissue, and it contains a range of human lung tissue.</p> <p> </p> <p> </p>
Dataset for Multiplex-PCR detection and Nanopore-based genotyping of fish pathogens
<p>This is a revised zip file contains scripts, initial fastq files, assembled amplicon (public and from this study) as well as bioinformatics intermediate files used for this study.</p> <p>Changelog:</p> <p>1. Fixed a bug in the 02_consensus.sh to enable proper removal of amplicons with zero depth</p> <p>2. Added a script (06_unclassified_read.sh) to extract and annotate reads that previously could not align to the 4 reference gene segment. Now the previously unclassified reads will be re-align (raw fastq) back to the gene segments as well as an additional tilapia genome assembly to gauge amount of reads mapping to the host genome. Furthermore, any read that still fail to align with minimap2 was subsequently aligned using blastn (-word_size 15 -evalue 0.01) against the same sequences.</p> <p>File Structure and Descriptions</p> <p>├── 01_process.sh : primer trimming, length-based filtering, read alignment, alignment filtering (unique hit) and extraction of uniquely hit reads for consensus generation<br> ├── 02_consensus.sh : [need artic conda env] Generation of consensus based on uniquely-mapped reads and minimal read depth of 20x required to call a variant (or it will be masked)<br> ├── 03_cleanup.sh: General folder and intermediate file re-organization<br> ├── 04_filter.sh: [need quast conda env] statistic of consensus generated and filtering of consensus with one or more ambiguous base (N), not suitable for haplotype<br> ├── 05_cluster.sh: clustering of consensus based on 100% identity threshold to generate putative haplotype<br> ├── 06_unclassified_read.sh: Extraction and annotation of unclassified reads using lenient criteria and with host reference genome as added reference<br> ├── Amplicon_FastQ folder: uniquely mapped fastq files for consensus generation<br> ├── BAM: alignment files generated from minimap2 used as input for the artic pipeline to identify variants<br> ├── Cluster_Rep.txt: Consensus sequences that were chosen to represent each haplotype<br> ├── Consensus folder: consensus fasta files generated for each sample containing sequences for each specific pathogen<br> ├── Coverage folder: coverage and base-level read depth for each sample and each pathogen reference genes<br> ├── Filter: individual fasta sequences (only 1 sequence per file) for each pathogen and each sample without any ambiguous base for subsequent clustering analysis<br> ├── Full_Haplotype.fasta: all possible haplotype sequences generated for each pathogen<br> ├── Gap_Analysis.tsv: Table with percentage of gap (0-100%) for each consensus sequence generated (used for filtering)<br> ├── Haplotype folder: Intermediate file and sample-level haplotype used to infer final haplotype and generate haplotype summary<br> ├── Haplotype_summary.tsv: Table with sample ID and their respectively pathogen haplotype<br> ├── Minimap2_PAF: Intermediate alignment generated from minimap2 used to generate the count table<br> ├── FailMinimap2 folder: FastQ files that didn't align using minimap2. Will be subsequently aligned using blastN (more sensitive) against the same reference sequences as minimap2<br> ├── Host_4Pathogen.fasta: Fasta file containing the tilapia genome and 4 pathogen (primer binding site included)<br> ├── Original: fastq with original naming prior to renaming based on sampleID. a script (rename.sh) was included to show renaming scheme<br> ├── primer.fasta: Primer sequences used for identifying and trimming reads with flanking primer sequence<br> ├── primer.fasta.fai: the index file for primer.fasta<br> ├── PrimerTrim folder: Primer-trimmed reads<br> ├── quast_results: consensus statistics generated by quast<br> ├── RawCount.tsv: Count table generated that can used as a input to generate figure<br> ├── RawFastq folder: Raw reads that have been renamed to reflect sample information<br> ├── readme.md: The current readme file<br> ├── ref_full_latest.fasta: Reference sequence of (gene segments) 4 pathogens e.g. TilV, ISKNV, SAG (Streptococcus agalactiae), FNO (Francisella noatunensis subsp. orientalis)<br> ├── ref_full_latest.primer.fasta: Same as above but with their primer binding sequence trimmed similar to the processed reads<br> ├── ref_full_latest.primer.fasta.fai<br> ├── RenameHaplotype: Script to perform reorganization of cdhit output<br> ├── Seq.stat.tsv: Sequencing statistics<br> ├── Uniq_PAF: Minimap2 alignment file for raw reads that initially failed quality check (no primer present and/or less than 80% query coverage / not unique alignment)<br> ├── Unmap: Raw reads that initial failed quality check (no primer on both ends / less than 80% query coverage / not unique alignment) <br> └── VCF: VCF files from medaka variant calling used to generate the final consensus</p>
A Multiplex Assay to Assess the Transaminase Activity toward Chemically Diverse Amine Donors
<p>The development of methods to engineer and immobilize amine transaminases (ATAs) to improve their functionality and operational stability is gaining momentum. The quest for robust, fast, and easy-to-use methods to screen the activity of large collections of transaminases, is essential. This work presents a novel and multiplex fluorescence-based kinetic assay to assess ATA activity using 4-dimethylamino-1-naphthaldehyde as an amine acceptor. The developed assay allowed us to screen a battery of amine donors using free and immobilized ATAs from different microbial sources as biocatalysts. As a result, using chromatographic methods, 4-hydroxybenzylamine was identified as the best amine donor for the amination of hydroxy methyl furfural. Finally, we adapted this method to determine the apparent Michaelis-Menten parameters of a model immobilized ATA at the microscopic (single-particle) level. Our studies promote the use of this multiplex, multidimensional assay to screen ATAs for further improvement.</p>
Model-based analysis of sample index hopping reveals its widespread artifacts in multiplexed single-cell RNA-sequencing
<p>Supplementary data that are needed to rerun the reproducible notebooks from the first steps using Alevin output and configuration files.</p> <p>Intermediate R data object that can be used to rerun the reproducible notebooks after the filtering steps.</p> <p>Validation data for inferring the sample index hopping rate. The <em>hiseq4000_joined_datatable_plexed_nonplexed.zip file contains read counts for four samples (two non-multiplexed and two multiplexed) joined by a cell-barcode, UMI, and gene-ID (CUG) key combination. The hiseq4000_inner_joined_with_labels.zip file contains only those CUGs that are observed in both the non-multiplexed and multiplexed samples.</em><em> </em></p>
scProAtlas: an atlas of multiplexed single-cell spatial proteomics imaging in human tissues
<p>All analysis results for the spatial proteomics imaging techniques in the scProAtlas database are stored in compressed files named accordingly. Within each compressed file, the folders are organized in a fixed storage structure in the following order: Analysis module > Imaging Technique > Dataset > Tissue > ROI.</p> <p>Each folder contains the corresponding metadata (including original sample information, cell type annotations, and neighborhood annotations) stored in a file named <code>cells.tsv</code>. Additionally, the module used to identify spatial pattern genes includes an <code>anndata</code> format file, named <code>adata_moran.h5ad</code>, which stores the integrated results of scRNA-seq and spatial proteomics.</p> <p>scProAtlas_analysis_code.tar.gz contains example codes for all analysis modules in scProAtlas. Here, we provide the example using <strong>SCP_CODEX1 - Large intestine. </strong>The codes include all the scripts used for the entire workflow, from image segmentation to scRNA-spatial proteomics integration, and spatial analysis.</p> <p>We have also uploaded the raw protein channel matrices with AnnData format in <strong>version 3 and 4.</strong></p>
Ultra-sensitive and multiplexed tracking of single cells using whole-body PET/CT
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Human intestine processed CODEX multiplexed images for donors B004-6, B008 (Part 1/2)
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Differential effects of multiplex and uniplex affiliative relationships on biomarkers of inflammation
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Human intestine processed CODEX multiplexed images for donors B009-B012 (Part 2/2)
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Data from: Multiplexed subspaces route neural activity across brain-wide networks
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Processed single cell data from CODEX multiplexed imaging of the human intestine
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Boosting multiplexing capabilities for error-robust spatial transcriptomic methods using a set exchange approach
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Multiplexed histology reveals phenotypic and spatial characteristics of human Innate Lymphoid Cells in chronic inflammation - Data tables
<p>Data tables containing image-based, single cell information for a tonsil and a colon data-set analyzed in CellProfiler: cell numbers, MFI of all markers, annotated cell types and cell allocations (X, Y coordinates).</p> <p> </p>
A 63-bp insertion in exon 2 of the porcine KIF21A gene is associated with arthrogryposis multiplex congenita
<p>This dataset includes genotype and phenotype data of 11 affected (case) and 23 unaffected (control) pigs for AMC case-control haplotype association testing and a VCF file of 809 candidate variants compatible with recessive inheritance of AMC.</p> <ol> <li>Genotype data (AMC.bed, AMC.bim and AMC.fam): 34 pigs genotyped with Illumina PorcineSNP60 Genotyping Bead chips.</li> <li>Phenotype data (AMC.pheno): the first, second and third column is Family ID, Animal ID and phenotype (1=case;2=control), respectively.</li> <li>AMC candidate variants compatible with recessive inheritance (AMC_candidate_muations.vcf.gz and AMC_candidate_muations.vcf.gz.tbi). Sequencing data of five animals in AMC pedigree have also been deposited at the Sequence Read Archive of the NCBI at the BioProject PRJNA622908 under sample accessions SAMN14532191, SAMN14532478, SAMN14532769, SAMN14532790 and SAMN14532792.</li> </ol>
SKR and QBER calculations for a BB84-QKD link multiplexed with intense classical signal in a WSS-based node
<p>This set will comprise a set of theoretical calculations on the effect of the filtering parameters of the WSS-based node on the quantum layer performance. In more detail, this data set will consist of the calculated QBER and SKR for the phase coding BB84 protocol by considering bandwidth and shape parameters of channel bands for Liquid Crystal on Silicon (LCoS)-based WSS nodes. Critical system requirements such as insertion loss and center frequency drifts are also considered. The obtained ultra-high sensitivity of QKD link performance on the filtering parameters of the WSS-based node, as confirmed by the generated data, can be useful for monitoring and control purposes at classical networks exploiting cascaded ROADMs.</p>
Remote near infrared identification of pathogens with multiplexed nanosensors - source data file for Nißler et al. 2020 (Nat. Commun.)
<p>source data file for Nißler et al. 2020 (Nat. Commun.)</p> <p>entitled: </p> <p>Remote near infrared identification of pathogens with multiplexed nanosensors</p>
Novel multiplex TaqMan assay for differentiation of the four major pathogenic Brachyspira species in swine
<p>Table S1</p> <p>503 Brachyspira samples used for validation of the novel 5-plex qPCR assay and comparison of results obtained by four independent PCR assays</p>
Asynchronous updates can promote the evolution of cooperation on multiplex networks
<p>Code is included to run the model described for varying enhancement factors, and the different versions of the social dilemmas (public goods game and prisoners dilemma) described in the publication. Code is also included to calculate the payoff probabilities described in the publication. The data used to plot the mean cooperation against the enhancement factors is also included for each of the models permutations. Which code files are for which permutation are described in the accompanying pdf.</p>
The public goods game on multiplex networks
<p>The code and data used to create the figures in chapters 3-7 in the thesis "The public goods game on multiplex networks". The data is labelled for which figure it corresponds to.</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.