FixNCut: Single-cell genomics through reversible tissue fixation and dissociation
Single-cell RNA-seq · human · 12 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
- 12 samples: 12 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
- FixNCut: single-cell genomics through reversible tissue fixation and dissociationGenome 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 GSE229944_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 PRJNA956865 and SRA study SRP433171. 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 12 sample titles: 12 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.
- GSM7181663 Mouse_Colon_fresh
- GSM7181664 Mouse_Colon_fixed_24h
- GSM7181665 Mouse_Lung_fresh
- GSM7181666 Mouse_Lung_fixed_24h
- GSM7181667 Mouse_Lung_cryo
- GSM7181668 Mouse_Lung_fixed_cryo
- GSM7181669 Human_PBMC_fresh
- GSM7181670 Human_PBMC_fixed
- GSM7181671 Human_colon_fresh
- GSM7181672 Human_colon_fixed
- GSM7181673 Human_colon_cryopreserved
- GSM7181674 Human_colon_fixed_cryopreserved
- GSE150672 Highly Efficient, Massively-Parallel Single-Cell RNA-Seq Reveals Cellular States and Molecular Features of Human Skin Pathology 438 samples shares single-cell
- GSE246613 Single-cell and spatial profiling identify three response trajectories to pembrolizumab and radiation therapy in triple negative breast cancer 266 samples shares single-cell
- GSE273803 PURE-seq Integrates FACS and PIP-seq for Single-Cell Genomics of Ultra-rare Cells 17 samples shares single-cell
- GSE38495 Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells 121 samples shares single-cell
- GSE92872 Pooled CRISPR screening with single-cell transcriptome read-out 100 samples shares single-cell
- GSE300217 Leveraging Two Novel Droplet-Based Single-Cell RNA Sequencing Platforms: a Comparative Study With 10x Genomics 9 samples shares single-cell
- GSE224478 ETMR stem-like state and chemo-resistance are supported by perivascular cells at single-cell resolution 51 samples shares single-cell
- GSE263817 Comparative single-cell analysis reveals IFN-g as a driver of respiratory sequelae post COVID-19 33 samples shares single-cell
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.