The biological mechanisms related to hub genes in the development of heart failure in dialysis patients were investigated through transcriptome sequencing
RNA-seq (gene expression) · human · 30 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
- 30 samples: 30 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 GSE253498_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 PRJNA1065902 and SRA study SRP484284. Searching any of these in the dataset finder brings you back here.
Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.
How we read this: Read from 30 sample titles: 30 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.
- GSM8021696 Normal1(MHD WITHOUT HEART FAILURE)
- GSM8021697 Normal2(MHD WITHOUT HEART FAILURE)
- GSM8021698 Normal3(MHD WITHOUT HEART FAILURE)
- GSM8021699 Normal4(MHD WITHOUT HEART FAILURE)
- GSM8021700 Normal5(MHD WITHOUT HEART FAILURE)
- GSM8021701 Normal6(MHD WITHOUT HEART FAILURE)
- GSM8021702 Normal7(MHD WITHOUT HEART FAILURE)
- GSM8021703 Normal8(MHD WITHOUT HEART FAILURE)
- GSM8021704 Normal9(MHD WITHOUT HEART FAILURE)
- GSM8021705 Normal10(MHD WITHOUT HEART FAILURE)
- GSM8021706 Normal11(MHD WITHOUT HEART FAILURE)
- GSM8021707 Normal12(MHD WITHOUT HEART FAILURE)
- GSM8021708 Normal13(MHD WITHOUT HEART FAILURE)
- GSM8021709 Normal14(MHD WITHOUT HEART FAILURE)
- GSM8021710 Normal15(MHD WITHOUT HEART FAILURE)
- GSM8021711 Case1(MHD WITH HEART FAILURE)
- GSM8021712 Case2(MHD WITH HEART FAILURE)
- GSM8021713 Case3(MHD WITH HEART FAILURE)
- GSM8021714 Case4(MHD WITH HEART FAILURE)
- GSM8021715 Case5(MHD WITH HEART FAILURE)
- GSM8021716 Case6(MHD WITH HEART FAILURE)
- GSM8021717 Case7(MHD WITH HEART FAILURE)
- GSM8021718 Case8(MHD WITH HEART FAILURE)
- GSM8021719 Case9(MHD WITH HEART FAILURE)
- GSM8021720 Case10(MHD WITH HEART FAILURE)
- GSM8021721 Case11(MHD WITH HEART FAILURE)
- GSM8021722 Case12(MHD WITH HEART FAILURE)
- GSM8021723 Case13(MHD WITH HEART FAILURE)
- GSM8021724 Case14(MHD WITH HEART FAILURE)
- GSM8021725 Case15(MHD WITH HEART FAILURE)
- GSE285217 Digital transformation of herbal medicine: Conversion to biological entity data using Qi related decoction and constituent herbal medicine-induced transcriptome sequencing_third_A549 270 samples shares biological, related
- GSE283054 Digital transformation of herbal medicine: Conversion to biological entity data using Qi related decoction and constituent herbal medicine-induced transcriptome sequencing_third_HepG2 270 samples shares biological, related
- GSE285424 Digital transformation of herbal medicine: Conversion to biological entity data using Qi related decoction and constituent herbal medicine-induced transcriptome sequencing [HT29] 270 samples shares biological, related
- GSE285554 Digital transformation of herbal medicine: Conversion to biological entity data using Qi related decoction and constituent herbal medicine-induced transcriptome sequencing_third_SW1783 270 samples shares biological, related
- GSE285552 Digital transformation of herbal medicine: Conversion to biological entity data using Qi related decoction and constituent herbal medicine-induced transcriptome sequencing_third_SW1783_batchD 60 samples shares biological, related
- GSE284437 Digital transformation of herbal medicine: Conversion to biological entity data using Qi related decoction and constituent herbal medicine-induced transcriptome sequencing_third_A549_batchC 60 samples shares biological, related
- GSE285420 Digital transformation of herbal medicine: Conversion to biological entity data using Qi related decoction and constituent herbal medicine-induced transcriptome sequencing_third_HT29_batchB 60 samples shares biological, related
- GSE285549 Digital transformation of herbal medicine: Conversion to biological entity data using Qi related decoction and constituent herbal medicine-induced transcriptome sequencing_third_SW1783_batchA 60 samples shares biological, related
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.