RNAseq of RPE-hTert cells expressing different levels of predictive reporter construct
RNA-seq (gene expression) · human · 50 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
- 50 samples: 10 conditions, mostly with about 5 replicates eachGroups 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
- Titration of RAS alters senescent state and influences tumour initiationNature · 2024
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 GSE222337_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 PRJNA919036 and SRA study SRP416197. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 50 samples.
How we read this: 10 replicated groups read from 50 sample titles; they account for 50 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.
- GSM6921190 L_d0_rep1 L_d0
- GSM6921191 L_d0_rep2 L_d0
- GSM6921192 L_d0_rep3 L_d0
- GSM6921193 L_d0_rep4 L_d0
- GSM6921194 L_d0_rep5 L_d0
- GSM6921195 L_d6_rep1 L_d6
- GSM6921196 L_d6_rep2 L_d6
- GSM6921197 L_d6_rep3 L_d6
- GSM6921198 L_d6_rep4 L_d6
- GSM6921199 L_d6_rep5 L_d6
- GSM6921200 M_d0_rep1 M_d0
- GSM6921201 M_d0_rep2 M_d0
- GSM6921202 M_d0_rep3 M_d0
- GSM6921203 M_d0_rep4 M_d0
- GSM6921204 M_d0_rep5 M_d0
- GSM6921205 M_d6_rep1 M_d6
- GSM6921206 M_d6_rep2 M_d6
- GSM6921207 M_d6_rep3 M_d6
- GSM6921208 M_d6_rep4 M_d6
- GSM6921209 M_d6_rep5 M_d6
- GSM6921210 N_d0_rep1 N_d0
- GSM6921211 N_d0_rep2 N_d0
- GSM6921212 N_d0_rep3 N_d0
- GSM6921213 N_d0_rep4 N_d0
- GSM6921214 N_d0_rep5 N_d0
- GSM6921215 N_d6_rep1 N_d6
- GSM6921216 N_d6_rep2 N_d6
- GSM6921217 N_d6_rep3 N_d6
- GSM6921218 N_d6_rep4 N_d6
- GSM6921219 N_d6_rep5 N_d6
- GSM6921220 S_d0_rep1 S_d0
- GSM6921221 S_d0_rep2 S_d0
- GSM6921222 S_d0_rep3 S_d0
- GSM6921223 S_d0_rep4 S_d0
- GSM6921224 S_d0_rep5 S_d0
- GSM6921225 S_d6_rep1 S_d6
- GSM6921226 S_d6_rep2 S_d6
- GSM6921227 S_d6_rep3 S_d6
- GSM6921228 S_d6_rep4 S_d6
- GSM6921229 S_d6_rep5 S_d6
+ 10 more — browse all 50 samples with per-sample file links →
- GSE320212 Identifying Senescence and Immune Biomarkers Predictive of Benefit to Combined CDK4/6 and PD-1 Blockade in Dedifferentiated Liposarcoma: Single-Cell RNA-seq of Serial Tumor Biopsies 23 samples shares predictive, different
- GSE226482 Single cell RNA sequencing of spinal motor neurons and interneurons differentiated from isogenic FUS and TARDBP human induced pluripotent stem cell lines harbouring ALS-causative mutations 1415 samples shares different
- GSE271689 Enhancing Immunotherapy Outcomes: Spatial Multi-Omics Predictive Models for Non-Small Cell Lung Cancer 586 samples shares predictive
- GSE278723 Differential Roles of Astrocytic CSF1 in Alzheimer's Disease and Cerebral Amyloid Angiopathy: Insights from Transcriptomic Analysis 248 samples shares different
- GSE306664 Revealing differential type I and type II IFN responses in different immune cells in various human diseases by a new IFNs transcriptomic atlas 131 samples shares different
- GSE328048 Single-cell transcriptome reveals keratinocyte subclusters that contribute to altered differentiation and inflammatory responses in atopic dermatitis 116 samples shares different
- GSE315928 Differential T cell clonal dynamics underlie outcomes to frontline chemoimmunotherapy in advanced gastric cancer [paired scRNA/TCR-seq] 94 samples shares different
- GSE268136 MYCN-driven de-differentiation profile identifies a subgroup of aggressive retinoblastoma II 82 samples shares different
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.