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Identification of potential non-invasive biomarkers for diagnosing patients with methamphetamine use disorder

GSE227204 Homo sapiens (human) RNA-seq (gene expression) 62 samples Submitted 2023/05/31 Platform GPL18573
At a glance

RNA-seq (gene expression) · human · 62 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
62 samples
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.
Summary (written by the submitters)
To date, diagnosis of methamphetamine use disorder (MUD) is based primarily on the patient’s self-reports of drug-seeking behaviour and interviews with psychiatrists in the absence of objective biomarkers. As an effective clinical method for diagnosis of MUD is still not available, the development of potential biomarkers for more accurate diagnosis is imperative.
Read the full summary ↓
Transcription profiling and discovery of biomarkers in the treatment and recovery periods of MUD patients can be used as scientific basis to provide a greater understanding of the disease and to facilitate decisions on whether to continue or not. In this study, RNA sequencing analysis was performed to profile transcripts from hair follicle cells of healthy controls and patients with MUD, and biomarkers were discovered at each transition states to be used to diagnose MUD. This study will present an important information to diagnose MUD using non-invasive biomarkers for human addicts and it will help to develop better pharmacological treatment of MUD in the future.
This dataset
Open GSE227204 in finder → Find more human RNA-seq datasets →
Download · which file do I need?

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.

1 · Just have a look

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.

Series matrix ↓
2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE227204_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓
3 · Start from the raw reads

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.

Raw reads on ENA ↗

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 PRJNA944127 and SRA study SRP426982. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 37240016) ↗
Samples in this study

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+ 62 more — browse all 62 samples with per-sample file links →

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