Single-cell transcriptome analysis reveals the molecular mechanisms underlying age-related changes in human testis
Single-cell RNA-seq · human · 25 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
- 25 samples
- 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
- Decoding the pathogenesis of spermatogenic failure in cryptorchidism through single-cell transcriptomic profilingCell reports. Medicine · 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 GSE254315_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 PRJNA1066828 and SRA study SRP484733. Searching any of these in the dataset finder brings you back here.
The grouping that defines any comparison lives in the sample characteristics, which GEO records separately from the titles.
How we read this: Sample titles run Donor4 … Donor34 and carry no group information.
- GSM8037606 Donor4
- GSM8037607 Donor5
- GSM8037608 Donor11
- GSM8037609 Donor21
- GSM8037610 Donor7
- GSM8037611 Donor12
- GSM8037612 Donor18
- GSM8037613 Donor20
- GSM8037614 Donor24
- GSM8037615 Donor16
- GSM8037616 Donor17
- GSM8037617 Donor22
- GSM8037618 Donor23
- GSM8037619 Donor15
- GSM8037620 Donor25
- GSM8037621 Donor6
- GSM8037622 Donor8
- GSM8037623 Donor9
- GSM8037624 Donor10
- GSM8037625 Donor13
- GSM8037626 Donor14
- GSM8037627 Donor19
- GSM8037628 Donor35
- GSM8037629 Donor30
- GSM8037630 Donor34
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- GSE243013 A single-cell atlas of immune heterogeneity in anti-PD1-treated non-small cell lung cancer 243 samples shares single-cell
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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.