Using integrative bioinformatics approaches and machine‑learning strategies to Identify potential signatures for atrial fibrillation
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: 20 × AF vs 10 × CGroups 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.
- Paper
- Using integrative bioinformatics approaches and machine-learning strategies to identify potential signatures for atrial fibrillationInternational journal of cardiology. Heart & vasculature · 2025
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 GSE282504_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 PRJNA1189049 and SRA study SRP546761. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 30 samples.
How we read this: 2 replicated groups read from 30 sample titles; they account for 30 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.
- GSM8644665 AF1 AF
- GSM8644666 AF2 AF
- GSM8644667 AF3 AF
- GSM8644668 AF4 AF
- GSM8644669 AF5 AF
- GSM8644670 AF6 AF
- GSM8644671 AF7 AF
- GSM8644672 AF8 AF
- GSM8644673 AF9 AF
- GSM8644674 AF10 AF
- GSM8644675 AF11 AF
- GSM8644676 AF12 AF
- GSM8644677 AF13 AF
- GSM8644678 AF14 AF
- GSM8644679 AF15 AF
- GSM8644680 AF16 AF
- GSM8644681 AF17 AF
- GSM8644682 AF18 AF
- GSM8644683 AF19 AF
- GSM8644684 AF20 AF
- GSM8644685 C1 C
- GSM8644686 C2 C
- GSM8644687 C3 C
- GSM8644688 C4 C
- GSM8644689 C5 C
- GSM8644690 C6 C
- GSM8644691 C7 C
- GSM8644692 C8 C
- GSM8644693 C9 C
- GSM8644694 C10 C
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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.