Altered bone marrow myelopoiesis contributes to kidney injury [scRNA-seq]
Single-cell RNA-seq · human · 11 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
- 11 samples: 11 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.
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 GSE277476_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 PRJNA1162492 and SRA study SRP533377. Searching any of these in the dataset finder brings you back here.
Supports a case/control comparison: 9 samples read as cases, 2 as controls.
How we read this: Read from 11 sample titles: 11 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.
- GSM8523011 Control 1, Healthy
- GSM8523012 Control 2, Healthy
- GSM8523013 Patient 1, 1° FSGS
- GSM8523014 Patient 2, 1° FSGS
- GSM8523015 Patient 3, 2° FSGS
- GSM8523016 Patient 4, 2° FSGS
- GSM8523017 Patient 5, 2° FSGS
- GSM8523018 Patient 6, 1° FSGS
- GSM8523019 Patient 7, Non-FSGS
- GSM8523020 Patient 8, 2° FSGS
- GSM8523021 Patient 10, Non-FSGS
- GSE304661 Identification of immune reconstitution and hematopoiesis restoration in bone marrow of multiple myeloma patients with high-dose melphalan following auto-HSCT by scRNA-seq 9 samples shares scrna-seq, marrow, bone
- GSE287541 Transcriptomic and proteomic characterization of cell and protein biomarkers of checkpoint inhibitor-induced liver injury [scRNA-seq] 92 samples shares scrna-seq, injury
- GSE269826 Conserved patterns of transcriptional dysregulation, heterogeneity and cell states in clear cell kidney cancer [scRNA-seq] 79 samples shares scrna-seq, kidney
- GSE292130 Longitudinal changes in DNA methylation in IDH-mutant glioma fuel disease progression through altered cell state differentiation [scRNA-seq] 31 samples shares scrna-seq, altered
- GSE212092 Systemic Inflammation Impairs Human Myelopoiesis [scRNA-Seq] 18 samples shares myelopoiesis, scrna-seq
- GSE309595 Longitudinal Analysis of Multiple Myeloma: Therapeutic Response and Immune Microenvironment Dynamics in the Context of Tumor Heterogeneity [scRNA-seq] 270 samples shares scrna-seq
- GSE234181 Long-term multi-modal recording reveals epigenetic adaptation routes in dormant breast cancer cells [scRNA-Seq] 184 samples shares scrna-seq
- GSE328048 Single-cell transcriptome reveals keratinocyte subclusters that contribute to altered differentiation and inflammatory responses in atopic dermatitis 116 samples shares altered
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.