Neuronal Activity-Dependent Gene Dysregulation inC9orf72i3Neuronal Models of ALS/FTD Pathogenesis
RNA-seq (gene expression) · human · 78 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
- 78 samples: 14 conditions, mostly in triplicateGroups 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.
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 GSE275765_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 PRJNA1152951 and SRA study SRP528906. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 42 samples.
How we read this: 14 replicated groups read from 78 sample titles; they account for 42 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.
- GSM8484188 C9_Line1_1_0 C9_Line1_1
- GSM8484189 C9_Line1_1_2 C9_Line1_1
- GSM8484190 C9_Line1_1_6 C9_Line1_1
- GSM8484191 C9_Line1_2_0 C9_Line1_2
- GSM8484192 C9_Line1_2_2 C9_Line1_2
- GSM8484193 C9_Line1_2_6 C9_Line1_2
- GSM8484194 C9_Line2_1_0 C9_Line2_1
- GSM8484195 C9_Line2_1_2 C9_Line2_1
- GSM8484196 C9_Line2_1_6 C9_Line2_1
- GSM8484197 C9_Line2_2_0 C9_Line2_2
- GSM8484198 C9_Line2_2_2 C9_Line2_2
- GSM8484199 C9_Line2_2_6 C9_Line2_2
- GSM8484200 C9_Line3_1_0 C9_Line3_1
- GSM8484201 C9_Line3_1_2 C9_Line3_1
- GSM8484202 C9_Line3_1_6 C9_Line3_1
- GSM8484203 C9_Line3_2_0 C9_Line3_2
- GSM8484204 C9_Line3_2_2 C9_Line3_2
- GSM8484205 C9_Line3_2_6 C9_Line3_2
- GSM8484206 C9_Line3_3_0 C9_Line3_3
- GSM8484207 C9_Line3_3_2 C9_Line3_3
- GSM8484208 C9_Line3_3_6 C9_Line3_3
- GSM8484209 WT_Line1_1_0 WT_Line1_1
- GSM8484210 WT_Line1_1_2 WT_Line1_1
- GSM8484211 WT_Line1_1_6 WT_Line1_1
- GSM8484212 WT_Line1_2_0 WT_Line1_2
- GSM8484213 WT_Line1_2_2 WT_Line1_2
- GSM8484214 WT_Line1_2_6 WT_Line1_2
- GSM8484215 WT_Line2_1_0 WT_Line2_1
- GSM8484216 WT_Line2_1_2 WT_Line2_1
- GSM8484217 WT_Line2_1_6 WT_Line2_1
- GSM8484218 WT_Line2_2_0 WT_Line2_2
- GSM8484219 WT_Line2_2_2 WT_Line2_2
- GSM8484220 WT_Line2_2_6 WT_Line2_2
- GSM8484221 WT_Line3_1_0 WT_Line3_1
- GSM8484222 WT_Line3_1_2 WT_Line3_1
- GSM8484223 WT_Line3_1_6 WT_Line3_1
- GSM8484224 WT_Line3_2_0 WT_Line3_2
- GSM8484225 WT_Line3_2_2 WT_Line3_2
- GSM8484226 WT_Line3_2_6 WT_Line3_2
- GSM8484227 WT_Line3_3_0 WT_Line3_3
+ 38 more — browse all 78 samples with per-sample file links →
- GSE310131 Long-read proteogenomic atlas of human neuronal differentiation reveals isoform diversity informing neurodevelopmental risk mechanisms 15 samples shares als, neuronal
- GSE253723 Reverse translational discovery reveals dysregulation of CD4+ and CD8+ resident memory T, myeloid, and stromal cells in steroid-refractory, checkpoint inhibitor colitis 12 samples shares als, dysregulation
- GSE305971 Integrative single-cell analysis reveals malignant archetypes and proliferative hierarchies in multiple myeloma 2105 samples shares als
- GSE314342 Genome-scale perturb-seq in primary human CD4+ T cells reveals genes regulating T cell programs and human immune traits. 577 samples shares als
- GSE49711 RNA-Seq reveals an unprecedented complexity of the neuroblastoma transcriptome and is suitable for clinical endpoint prediction [RNA-Seq] 498 samples shares als
- GSE274680 Inflammation and lymphocyte activation during longitudinal progression to rheumatoid arthritis in at-risk individuals 243 samples shares als
- GSE263733 Single-cell transcriptome analysis reveals subtype-specific clonal evolution and microenvironmental changes in liver metastasis of pancreatic adenocarcinoma and their clinical implications 218 samples shares als
- GSE328048 Single-cell transcriptome reveals keratinocyte subclusters that contribute to altered differentiation and inflammatory responses in atopic dermatitis 116 samples shares als
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.