Deciphering the heterogeneity of differentiating hPSC-derived corneal limbal stem cells through single-cell RNA-sequencing
Single-cell RNA-seq · human · 19 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
- 19 samples: 17 distinct titles, 2 of them repeatedGroups 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
- Deciphering the heterogeneity of differentiating hPSC-derived corneal limbal stem cells through single-cell RNA sequencingStem cell reports · 2024
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 GSE248497_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 PRJNA1044128 and SRA study SRP473769. Searching any of these in the dataset finder brings you back here.
How we read this: Read from 19 sample titles: 17 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.
- GSM7915973 hESC_FACS_rep1
- GSM7915974 hESC_FACS_rep2
- GSM7915975 hiPSC1_FACS_rep1
- GSM7915976 hiPSC1_FACS_rep2
- GSM7915977 hESC_d0_replica3_plate1
- GSM7915978 hESC_d11_replica3_plate1
- GSM7915979 hESC_d24_replica3_plate1
- GSM7915980 hiPSC1_d0_replica1_plate1
- GSM7915981 hiPSC1_d0_replica2_plate2
- GSM7915982 hiPSC1_d11_replica1_plate1
- GSM7915983 hiPSC1_d11_replica2_plate2
- GSM7915984 hiPSC1_d24_replica1_plate1
- GSM7915985 hiPSC1_d24_replica2_plate2
- GSM7915986 hiPSC2_d0_replica2_plate2
- GSM7915987 hiPSC2_d0_replica3_plate1
- GSM7915988 hiPSC2_d11_replica2_plate2
- GSM7915989 hiPSC2_d11_replica3_plate1
- GSM7915990 hiPSC2_d24_replica2_plate2
- GSM7915991 hiPSC2_d24_replica3_plate1
- GSE243013 A single-cell atlas of immune heterogeneity in anti-PD1-treated non-small cell lung cancer 243 samples shares heterogeneity, single-cell
- GSE264680 Leveraging Single-Cell Sequencing to Classify and Characterize Tumor Subgroups in Bulk RNA-Sequencing Data 167 samples shares rna-sequencing, single-cell
- GSE287301 Single-cell RNA-sequencing of HNSCC-infiltrating T cells 48 samples shares rna-sequencing, single-cell
- GSE269877 Single-cell RNA-sequencing of BCG naïve and recurrent non-muscle invasive bladder cancer reveals a CD6/ALCAM-mediated immune-suppressive pathway 38 samples shares rna-sequencing, single-cell
- GSE303762 Benchmarking long-read RNA-sequencing technologies with LongBench: a cross-platform reference dataset profiling cancer cell lines with bulk and single-cell approaches 38 samples shares rna-sequencing, single-cell
- GSE280639 Multichrome encoding-based multiplexed, spatially resolved imaging reveals single-cell RNA epigenetic modifications heterogeneity 21 samples shares heterogeneity, single-cell
- GSE204704 Live single-cell RNA-sequencing data from ALS donors only. 21 samples shares rna-sequencing, single-cell
- GSE319709 Single-cell transcriptomics reveals etiology-specific T-cell heterogeneity in hepatocellular carcinoma and implicates regulatory 18 samples shares heterogeneity, 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.