Transcriptomic profiling of chronic rhinosinusitis with nasal polyps
RNA-seq (gene expression) · human · 22 samples
- What it is
- RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
- Organism
- Homo sapiens (human)
- Samples
- 22 samples: 9 × EosNP vs 8 × Control vs 5 × Non_EPGroups 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 GSE338426_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: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.
Also filed as BioProject PRJNA1494473 and SRA study SRP717554. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 22 samples.
How we read this: 3 replicated groups read from 22 sample titles; they account for 22 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.
- GSM9873897 Control1 Control
- GSM9873898 Control2 Control
- GSM9873899 Control3 Control
- GSM9873900 Control4 Control
- GSM9873901 Control5 Control
- GSM9873902 Control6 Control
- GSM9873903 Control7 Control
- GSM9873904 Control8 Control
- GSM9873905 EosNP_1 EosNP
- GSM9873906 EosNP_2 EosNP
- GSM9873907 EosNP_3 EosNP
- GSM9873908 EosNP_4 EosNP
- GSM9873909 EosNP_5 EosNP
- GSM9873910 EosNP_6 EosNP
- GSM9873911 EosNP_7 EosNP
- GSM9873912 EosNP_8 EosNP
- GSM9873913 EosNP_9 EosNP
- GSM9873914 Non_EP_1 Non_EP
- GSM9873915 Non_EP_2 Non_EP
- GSM9873916 Non_EP_3 Non_EP
- GSM9873917 Non_EP_4 Non_EP
- GSM9873918 Non_EP_5 Non_EP
- GSE276503 Myeloid-derived IL1B drives epithelial and stromal cell remodeling during chronic rhinosinusitis and nasal polyps [scRNA-seq] 33 samples shares rhinosinusitis, chronic, polyps, nasal
- GSE284820 Mechanism of Corynebacterium striatum-mediated inhibition of Staphylococcus aureus-induced epithelial barrier injury and inflammation activation in chronic rhinosinusitis [RNA-Seq] 50 samples shares rhinosinusitis, chronic
- GSE324445 Heterogeneity and Clinical Relevance of Group 2 Innate Lymphoid Cells Subsets in Nasal Polyps 10 samples shares polyps, nasal
- GSE235715 Multi-scaled transcriptomics of chronically inflamed nasal epithelium reveals immune-epithelial dynamics and tissue remodeling in nasal polyp formation 60 samples shares chronic, nasal
- GSE310058 Single-nucleus transcriptomic profiling of human lung tissue in chronic obstructive pulmonary disease (COPD) [snRNA-seq] 149 samples shares chronic
- GSE276114 Integrative proteo-transcriptomic characterization of advanced fibrosis in chronic liver disease across etiologies 177 samples shares chronic
- GSE301908 Single-cell spatial transcriptomic profiling defines a pathogenic inflammatory niche in chronic active multiple sclerosis lesions - snRNA-seq 17 samples shares chronic
- GSE246521 Chronic Lymphocytic Leukemia in African Ancestry Population is Characterized by Increased Genomic Instability 350 samples shares chronic
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.