Ovarian transcriptome sequencing (scRNA-seq and RNA-seq) of 6 and 9-month-old mice fed black rice, white brown rice and polished rice
Single-cell RNA-seq · mouse · 41 samples
- What it is
- Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
- Organism
- Mus musculus (mouse)
- Samples
- 41 samples: 14 conditions, mostly in triplicateGroups 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.
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 GSE235021_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 PRJNA984200 and SRA study SRP443975. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 41 samples.
How we read this: 14 replicated groups read from 41 sample titles; they account for 41 of them. 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.
- GSM7493305 SCB-6M-1 SCB-6M
- GSM7493306 SCB-6M-2 SCB-6M
- GSM7493307 SCB-6M-3 SCB-6M
- GSM7493308 SCP-6M-1 SCP-6M
- GSM7493309 SCP-6M-2 SCP-6M
- GSM7493310 SCP-6M-3 SCP-6M
- GSM7493311 SCW-6M-1 SCW-6M
- GSM7493312 SCW-6M-2 SCW-6M
- GSM7493313 SCW-6M-3 SCW-6M
- GSM7493314 SCB-9M-1 SCB-9M
- GSM7493315 SCB-9M-2 SCB-9M
- GSM7493316 SCB-9M-3 SCB-9M
- GSM7493317 SCP-9M-1 SCP-9M
- GSM7493318 SCP-9M-3 SCP-9M
- GSM7493319 SCW-9M-1 SCW-9M
- GSM7493320 SCW-9M-2 SCW-9M
- GSM7493321 SCW-9M-3 SCW-9M
- GSM8711819 B-6M-1 B-6M
- GSM8711820 B-6M-2 B-6M
- GSM8711821 B-6M-3 B-6M
- GSM8711822 P-6M-1 P-6M
- GSM8711823 P-6M-2 P-6M
- GSM8711824 P-6M-3 P-6M
- GSM8711825 W-6M-1 W-6M
- GSM8711826 W-6M-2 W-6M
- GSM8711827 W-6M-3 W-6M
- GSM8711828 B-9M-1 B-9M
- GSM8711829 B-9M-2 B-9M
- GSM8711830 B-9M-3 B-9M
- GSM8711831 P-9M-1 P-9M
- GSM8711832 P-9M-2 P-9M
- GSM8711833 P-9M-3 P-9M
- GSM8711834 W-9M-1 W-9M
- GSM8711835 W-9M-2 W-9M
- GSM8711836 W-9M-3 W-9M
- GSM8908022 B-OO-9M-1 B-OO-9M
- GSM8908023 B-OO-9M-2 B-OO-9M
- GSM8908024 B-OO-9M-3 B-OO-9M
- GSM8908025 P-OO-9M-1 P-OO-9M
- GSM8908026 P-OO-9M-2 P-OO-9M
+ 1 more — browse all 41 samples with per-sample file links →
- GSE254258 BBSome-dependent ciliary Hedgehog signaling governs cell fate in the white adipose tissue (scRNA-Seq) 10 samples shares scrna-seq, rna-seq, white
- GSE241770 ACSS2 mediates an epigenetic pathway to regulate maternal Beta-cell adaptation and progression of gestational diabetes (scRNA-Seq) 7899 samples shares scrna-seq, rna-seq
- GSE264105 Plate-based scRNA-seq of plasma-positive microglia and plasma-negative microglia in young mice 1111 samples shares scrna-seq, rna-seq
- GSE271100 Stem cells tightly regulate dead cell clearance to maintain tissue fitness [scRNA-seq] 1037 samples shares scrna-seq, rna-seq
- GSE326100 scRNA-seq of mouse cochlea organoid cells 768 samples shares scrna-seq, rna-seq
- GSE265909 yHSC and oHSC scRNAseq with oHSC AThi ATlo Index Sorting. Assessing the transcriptional changes associated with autophagy engagement in old hematopoietic stem cells at single cell resolution. [scRNA-Seq] 563 samples shares scrna-seq, rna-seq
- GSE217662 Decoding Trp53 regulatory program with knockout cell transcriptomic landscape analyses [scRNA-seq] 247 samples shares scrna-seq, rna-seq
- GSE228455 Gene expression profile at single cell level of monocytes sorted from C57BL/6J mouse ear tissue at 3 weeks post Leishmania major infection [scRNA-seq] 218 samples shares scrna-seq, rna-seq
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.