Abb5f71b-faf8-431b-ad45-168f47fd8666 - samples
Ontology highlight
ABSTRACT:
PROVIDER: EGAD00001010073 | EGA |
REPOSITORIES: EGA
Similar Datasets
Project description:3 samples of R1, R2 and R3 bone marrow monocytes were compared from 3 biological replicates in 3 separate experiments. R1, R2 and R3 were sorted from triplicate experiments from pools of mice
2016-11-24 | GSE90471 | GEO
Project description:The dataset contains 200 fastq files of Illumina 5'end RNA sequencing data of 50 PBMC samples. The paired-end data includes 100 fastq files (R1 and R2) of 50 full-length cDNA sequencing libraries and 100 fastq files (R1 and R2) of 50 HLA amplicon sequencing libraries.
| EGAD00001006838 | EGA
Project description:DIA and PRM raw data of mouse brain tissues. R1: Cerebellum, R2: Midbrain, R3: Spinal cord
2019-11-26 | PXD016441 |
Project description:This repository contains all the FASTQ files for the five data modalities (scRNA-seq, scATAC-seq, Multiome, CITE-seq+scVDJ-seq, and spatial transcriptomics) used in the article \\"An Atlas of Cells in The Human Tonsil,\\" published in Immunity in 2024. Inspired by the TCGA barcodes, we have named each fastq file with the following convention: [TECHNOLOGY].[DONOR_ID].[SUBPROJECT].[GEM_ID].[LIBRARY_ID].[LIBRARY_TYPE].[LANE].[READ].fastq.gz which allows to retrieve all metadata from the name itself. Here is a full description of each field: - TECHNOLOGY: scRNA-seq, scATAC-seq, Multiome, CITE-seq+scVDJ-seq, and spatial transcriptomics (Visium). We also include the fastq files associated with the multiome experiments performed on two mantle cell lymphoma patients (MCL). - DONOR_ID: identifier for each of the 17 patients included in the cohort. We provide the donor-level metadata in the file \\"tonsil_atlas_donor_metadata.csv\\", including the hospital, sex, age, age group, cause for tonsillectomy and cohort type for every donor. - SUBPROJECT: each subproject corresponds to one run of the 10x Genomics Chromium™ Chip. - GEM_ID: each run of the 10x Genomics Chromium™ Chip consists of up to 8 \\"GEM wells\\" (see https://www.10xgenomics.com/support/software/cell-ranger/getting-started/cr-glossary): a set of partitioned cells (Gel Beads-in-emulsion) from a single 10x Genomics Chromium™ Chip channel. We give a unique identifier to each of these channels. - LIBRARY_ID: one or more sequencing libraries can be derived from a GEM well. For instance, multiome yields two libraries (ATAC and RNA) and CITE-seq+scVDJ yields 4 libraries (RNA, ADT, BCR, TCR). - LIBRARY_TYPE: the type of library for each library_id. Note that we used cell hashing () for a subset of the scRNA-seq libraries, and thus the library_type can be \\"not_hashed\\", \\"hashed_cdna\\" (RNA expression) or \\"hashed_hto\\" (the hashtag oligonucleotides). - LANE: to increase sequencing depth, each library was sequenced in more than one lane. Important: all lanes corresponding to the same sequencing library need to be inputed together to cellranger, because they come from the same set of cells. - READ: for scATAC-seq we have three reads (R1, R2 or R3), see cellranger-atac's documentation. While we find these names to be the most useful, they need to be changed to follow cellranger's conventions. We provide a code snippet in the README file of the GitHub repository associated with the tonsil atlas to convert between both formats (https://github.com/Single-Cell-Genomics-Group-CNAG-CRG/TonsilAtlas/). Besides the fastq files, cellranger (and other mappers) require additional files, which we also provide in this repository: - cell_hashing_metadata.csv: as mentioned above, we ran cell hashing (10.1186/s13059-018-1603-1) to detect doublets and reduce cost per cell. This file provides the sequence of the hashtag oligonucleotides in cellranger convention to allow demultiplexing. - cite_seq_feature_reference.csv: similar to the previous file, this one links each protein surface marker to the hashtag oligonucleotide that identified it in the CITE-seq experiment. - V10M16-059.gpr and V19S23-039.gpr: these correspond to the two slides of the two Visium experiments performed in the tonsil atlas. They are needed to run spaceranger. - [GEM_ID]_[SLIDE]_[CAPTURE_AREA].jpg: 8 images associated with the Visium experiments. Here, GEM_ID refers to each of the 4 capture areas in each slide. - [TECHNOLOGY]_sequencing_metadata.csv: the GEM-level metadata for each technology. It includes the relationship between subproject, gem_id, library_id, library_type and donor_id. These are the other repositories associated with the tonsil atlas: - Expression and accessibility matrices: https://zenodo.org/records/10373041 - Seurat objects: https://zenodo.org/records/8373756 - HCATonsilData package: https://bioconductor.org/packages/release/data/experiment/html/HCATonsilData.html - Azimuth: https://azimuth.hubmapconsortium.org/ - Github: https://github.com/Single-Cell-Genomics-Group-CNAG-CRG/TonsilAtlas
2024-05-02 | E-MTAB-13687 | biostudies-arrayexpress
Project description:We profile 10 high-grade gliomas patient brain tumor samples by single-cell multiome ATAC + gene expression, using the 10X Chromium technology.
3 sets of fastq are provided for each samples: R1 and R2 for gene expression, R1 and R2 for ATAC-seq as well as index1 and index2 for ATAC-seq.
| EGAD00001008349 | EGA
Project description:Mononuclear phagocytes have important functions in regulating tissue injury and regeneration after AKI. We tested the hypothesis that kidney resident macrophages (R2) and infiltrative mononuclear phagocytes (R1) exist in distinct transcriptional subsets that are resolvable by single cell RNA sequencing. A further objective was to identify transcriptional signatures for those subsets. By cell sorting R2 and R1 cells from quiescent and injured mouse kidneys, we found remarkable heterogeneity among intrarenal innate immune cells. We define four subsets of dendritic cells and six subsets of R2 cells and specify differentially expressed genes that identify these cell types. Additionally, we observed subset-specific transcriptional responses to AKI. R2 cells, which express surface phenotype homogeneity, adopt distinct transcriptional programs in the steady state and after injury. Expression of MHC class II and invariant chain genes are decreased in a predominant subset of R2 cells after AKI. In contrast, other macrophage lineage-defining genes such as C1qa, b, and c, are expressed at similar levels across R2 cell subsets and their expression does not change in response to injury. This study has allowed us to identify R2 and R1 subsets that may prove to be therapeutic targets for inhibiting damaging inflammation and fibrosis or promoting tissue regeneration.
2023-09-01 | GSE157338 | GEO
Project description:We used microarrays to analyze gene expression changes in liver after treatment of rats with two compounds from drug development (R1, R2) to identify potential effects related to hepatotoxicity. The development cpds have been described in: Pierson et al., J Med Chem 52: 3855-62. R1 corresponds to 36, R2 corresponds to 38
2009-07-29 | GSE17388 | GEO
Project description:This dataset contains raw sequencing files associated with the paper "Neutrophils and emergency granulopoiesis drive immune suppression and an extreme response endotype during sepsis" (https://doi.org/10.1038/s41590-023-01490-5).
It is composed of two experiments, which are as follows:
1. Single-cell profiling of whole blood leukocytes: This experiment comprises sequencing files generated using the BD Rhapsody single-cell multi-omics profiling (RNA + protein) platform. This platform was used to profile the whole blood leukocyte population in a cohort of sepsis patients, cardiac surgery controls and healthy controls. There are 48 samples and four files per sample: two paired-end FASTQ files (R1 and R2) corresponding to the RNA-seq library, and two paired-end FASTQ files (R1 and R2) corresponding to the protein profiling (ADT-based AbSeq) library.
2. Single-cell profiling of circulating HSPCs: This experiment comprises sequencing files generated using the 10X single-cell multi-omics (RNA-seq + ATAC-seq) profiling platform. This platform was used to profile circulating HSPCs in a cohort of sepsis patients and healthy controls. There are 5 samples (or plexes), each of which consists of a pool of individuals for whom cells were multiplexed and sequenced as a single sample. There are four files per sample: two paired-end FASTQ files (R1 and R2) corresponding to the RNA-seq library, and two paired-end FASTQ files (R1 and R2) corresponding to the ATAC-seq library. Because these samples consists of multiplexed pools, index files are also provided. These can be used for sample deconvolution using the Cell Ranger pipeline.
NOTE: Due to EGA's constrains in the number of files permitted per sample, index files (I1 and I2) for the HSPC data set are provided as a separate experiment named "FASTQ index files (I1 and I2) for deconvolution of 10X single-cell multiomics libraries".
| EGAD00001010927 | EGA
Project description:This is the inaugural dataset for the new GRIP-seq method to map cis-regulatory regions genome-wide. This dataset is for the purple sea urchin embryo, S. purpuratus, at 24 hours post fertilization. There are two experimental samples using an anti-Pol(II) antibody to pull down regulatory region-promoter complexes and one control using IgG alone. Full details of the GRIP-seq methodology are available in the linked manuscript. Bam index (bai) files and tiled data (tdf) files are also available at http://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-3607/files/
2016-06-14 | E-MTAB-3607 | biostudies-arrayexpress
Project description:Using integrated genomics we identify a role for CLEC12A in antibacterial autophagy. Clec12a-/- mice are more susceptible to bacterial infection and CLEC12A deficient cells exhibit impaired antibacterial autophagy. We used transcriptional profilinf to understand the role of CLEC12A in the response to Salmonella and Listeria. Bone marrow-derived macrophages from WT or Clec12a-/- mice were infected with Salmonella enterica serovar Typhimurium or Listeria monocytogenes. Cells were harvested at 0,3,6, and 24hours post-infection for RNA analysis. Please note that single-end sequencing was performed but two files: R1 files that contained the sample barcodes (19 or 17bp reads) and R2 files that contained the single-end-sequenced 46bp cDNA reads were generated. Since the barcode info is mostly redundant, only R2 reads were submitted (described in 'raw_file_readme.txt').
2015-06-30 | E-GEOD-69132 | biostudies-arrayexpress