SenCat: Redefining human cell senescence
RNA-seq (gene expression) · human · 232 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
- 232 samples: 50 conditions, mostly with about 4 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.
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 GSE285826_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 PRJNA1206155 and SRA study SRP552543. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 223 samples.
How we read this: 50 replicated groups read from 232 sample titles; they account for 223 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.
- GSM8690061 BJ_P_1 BJ_P
- GSM8690062 BJ_P_2 BJ_P
- GSM8690063 BJ_P_3 BJ_P
- GSM8690064 BJ_P_4 BJ_P
- GSM8690065 BJ_ETIS_1 BJ_ETIS
- GSM8690066 BJ_ETIS_2 BJ_ETIS
- GSM8690067 BJ_ETIS_3 BJ_ETIS
- GSM8690068 BJ_ETIS_4 BJ_ETIS
- GSM8690069 BJ_IRIS_1 BJ_IRIS
- GSM8690070 BJ_IRIS_2 BJ_IRIS
- GSM8690071 BJ_IRIS_3 BJ_IRIS
- GSM8690072 BJ_IRIS_4 BJ_IRIS
- GSM8690073 BJ_OSIS_1 BJ_OSIS
- GSM8690074 BJ_OSIS_2 BJ_OSIS
- GSM8690075 BJ_OSIS_3 BJ_OSIS
- GSM8690076 BJ_OSIS_4 BJ_OSIS
- GSM8690077 BJ_EV_1 BJ_EV
- GSM8690078 BJ_EV_2 BJ_EV
- GSM8690079 BJ_EV_3 BJ_EV
- GSM8690080 BJ_EV_4 BJ_EV
- GSM8690081 BJ_OIS_1 BJ_OIS
- GSM8690082 BJ_OIS_2 BJ_OIS
- GSM8690083 BJ_OIS_3 BJ_OIS
- GSM8690084 BJ_OIS_4 BJ_OIS
- GSM8690085 BMMSC_P_1 BMMSC_P
- GSM8690086 BMMSC_P_2 BMMSC_P
- GSM8690087 BMMSC_P_3 BMMSC_P
- GSM8690088 BMMSC_P_4 BMMSC_P
- GSM8690089 BMMSC_ETIS_1 BMMSC_ETIS
- GSM8690095 BMMSC_ETIS_2 BMMSC_ETIS
- GSM8690099 BMMSC_ETIS_3 BMMSC_ETIS
- GSM8690103 BMMSC_ETIS_4 BMMSC_ETIS
- GSM8690105 BMMSC_IRIS_1 BMMSC_IRIS
- GSM8690106 BMMSC_IRIS_2 BMMSC_IRIS
- GSM8690107 BMMSC_IRIS_3 BMMSC_IRIS
- GSM8690108 BMMSC_IRIS_4 BMMSC_IRIS
- GSM8690109 HCAEC_P_1 HCAEC_P
- GSM8690110 HCAEC_P_2 HCAEC_P
- GSM8690111 HCAEC_P_3 HCAEC_P
- GSM8690112 HCAEC_P_4 HCAEC_P
+ 192 more — browse all 232 samples with per-sample file links →
- GSE245949 Failed reprogramming of transformed cells due to induction of apoptosis and senescence impairs tumor progression in lung cancer 24 samples shares senescence
- GSE295888 SIRT7 regulates NUCKS1 chromatin binding to elicit metabolic and inflammatory gene expression in senescence and liver aging. 30 samples shares senescence
- GSE304030 oHSV-mediated JAG1 blockade induces glioma senescence-associated secretory phenotype to increase macrophage activation and sensitize to cetuximab-mediated senolysis 24 samples shares senescence
- GSE249561 Histone Lactylation Antagonizes Senescence and Skeletal Muscle Aging by Modulating Aging-Related Pathways [RNA-Seq II] 20 samples shares senescence
- GSE270175 Oxysterol Sensing through GPR183 Triggers Endothelial Senescence and Cardio-renal Injuries via Altering Circadian Signaling in Hypertension 18 samples shares senescence
- GSE259308 A p53-CCF circuit integrates genome integrity with senescence phenotypes 81 samples shares senescence
- GSE226006 Histone Lactylation Antagonizes Senescence and Skeletal Muscle Aging by Modulating Aging-Related Pathways [CUT&Tag] 32 samples shares senescence
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.