Integrated Single-Cell Multiomic Profiling of Caudate Nucleus Suggests Key Mechanisms in Alcohol Use Disorder
Single-cell RNA-seq · human · 485 samples
- What it is
- Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
- Organism
- Homo sapiens (human)
- Samples
- 485 samples: 485 samples, each with a distinct title — no replicated groupsGroups are read from the sample names, not from the paper. Check the list below.
- Data you can get
- A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
- Paper
- Integrated Single-Cell Multiomic Profiling of Caudate Nucleus Suggests Key Mechanisms in Alcohol Use DisorderbioRxiv : the preprint server for biology · 2024
Read the full summary ↓
Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.
Processed table
The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.
Supplementary files
The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE277313_RAW.tar (it can run to tens of gigabytes).
Raw sequencing reads
The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.
What the files usually look like: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.
Also filed as BioProject PRJNA1156550 and SRA study SRP530395. Searching any of these in the dataset finder brings you back here.
Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.
How we read this: Read from the first 400 of 485 sample titles: 400 distinct titles with little repetition. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.
- GSM8518573 BTRC_102_10X _HT_snRNA-seq
- GSM8518574 BTRC_103_10X _HT_snRNA-seq
- GSM8518575 BTRC_105_10X _HT_snRNA-seq
- GSM8518576 BTRC_106_10X _HT_snRNA-seq
- GSM8518577 BTRC_107_10X _HT_snRNA-seq
- GSM8518578 BTRC_11_10X _HT_snRNA-seq
- GSM8518579 BTRC_112_10X _HT_snRNA-seq
- GSM8518580 BTRC_115_10X _HT_snRNA-seq
- GSM8518581 BTRC_116_10X _HT_snRNA-seq
- GSM8518582 BTRC_12_10X _HT_snRNA-seq
- GSM8518583 BTRC_121_10X _HT_snRNA-seq
- GSM8518584 BTRC_123_10X _HT_snRNA-seq
- GSM8518585 BTRC_124_10X _HT_snRNA-seq
- GSM8518586 BTRC_125_10X _HT_snRNA-seq
- GSM8518587 BTRC_127_10X _HT_snRNA-seq
- GSM8518588 BTRC_128_10X _HT_snRNA-seq
- GSM8518589 BTRC_131_10X _HT_snRNA-seq
- GSM8518590 BTRC_132_10X _HT_snRNA-seq
- GSM8518591 BTRC_134_10X _HT_snRNA-seq
- GSM8518592 BTRC_136_10X _HT_snRNA-seq
- GSM8518593 BTRC_137_10X _HT_snRNA-seq
- GSM8518594 BTRC_139_10X _HT_snRNA-seq
- GSM8518595 BTRC_142_10X _HT_snRNA-seq
- GSM8518596 BTRC_143_10X _HT_snRNA-seq
- GSM8518597 BTRC_145_10X _HT_snRNA-seq
- GSM8518598 BTRC_146_10X _HT_snRNA-seq
- GSM8518599 BTRC_147_10X _HT_snRNA-seq
- GSM8518600 BTRC_148_10X _HT_snRNA-seq
- GSM8518601 BTRC_149_10X _HT_snRNA-seq
- GSM8518602 BTRC_15_10X _HT_snRNA-seq
- GSM8518603 BTRC_150_10X _HT_snRNA-seq
- GSM8518604 BTRC_158_10X _HT_snRNA-seq
- GSM8518605 BTRC_159_10X _HT_snRNA-seq
- GSM8518606 BTRC_16_10X _HT_snRNA-seq
- GSM8518607 BTRC_161_10X _HT_snRNA-seq
- GSM8518608 BTRC_162_10X _HT_snRNA-seq
- GSM8518609 BTRC_163_10X _HT_snRNA-seq
- GSM8518610 BTRC_164_10X _HT_snRNA-seq
- GSM8518611 BTRC_167_10X _HT_snRNA-seq
- GSM8518612 BTRC_168_10X _HT_snRNA-seq
+ 445 more — browse all 485 samples with per-sample file links →
- GSE305146 Single-Cell Multiomic Atlas of Human Cortical Development in Down Syndrome - Fetal Cortex Multiome Dataset 92 samples shares single-cell, multiomic
- GSE277469 Integrated single-cell chromatin and transcriptomic analyses of human peripheral immune cells in patients with alopecia areata. 32 samples shares single-cell, integrated
- GSE196198 Longitudinal single-cell multiomics data for different severity of COVID-19 100 samples shares single-cell, multiomic
- GSE290367 Single-cell multiomic integration identifies widespread, cell-type resolved fetal reactivation in the diseased human heart 90 samples shares single-cell, multiomic
- GSE278694 Integrated Single-Cell and Spatial Transcriptomics Uncover Distinct Cellular Subtypes Involved in Neural Invasion in PDAC 88 samples shares single-cell, integrated
- GSE337246 Integrated single-cell profiling of RNA and DNA interactomes reveals targetable chromatin architectures in cancer [scRNA-Seq organoids] 35 samples shares single-cell, integrated
- GSE337829 Integrated single-cell profiling of RNA and DNA interactomes reveals targetable chromatin architectures in cancer [ChIP-Seq] 32 samples shares single-cell, integrated
- GSE278688 Integrated Single-Cell and Spatial Transcriptomics Uncover Distinct Cellular Subtypes Involved in Neural Invasion in PDAC [scRNA-Seq] 29 samples shares single-cell, integrated
Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.