Transcriptomic profiling of irradiated HEK293T and NHLF cells to assess cell-type specific responses to γ-ray exposure
RNA-seq (gene expression) · human · 12 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
- 12 samples: 3 × HEK_Ct vs 3 × HEK_r16 vs 3 × NHLF_Ct vs 3 × NHLF_r16Groups 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 GSE294574_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 PRJNA1250524 and SRA study SRP578502. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 12 samples.
How we read this: 4 replicated groups read from 12 sample titles; they account for 12 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.
- GSM8911586 HEK_Ctr_1 HEK_Ct
- GSM8911587 HEK_Ctr_2 HEK_Ct
- GSM8911588 HEK_Ctr_3 HEK_Ct
- GSM8911589 HEK_r16_1 HEK_r16
- GSM8911590 HEK_r16_2 HEK_r16
- GSM8911591 HEK_r16_3 HEK_r16
- GSM8911592 NHLF_Ctr_1 NHLF_Ct
- GSM8911593 NHLF_Ctr_2 NHLF_Ct
- GSM8911594 NHLF_Ctr_3 NHLF_Ct
- GSM8911595 NHLF_r16_1 NHLF_r16
- GSM8911596 NHLF_r16_2 NHLF_r16
- GSM8911597 NHLF_r16_3 NHLF_r16
- GSE270586 The specificity of gene activation was revealed by RNA-seq in the HEK293T cells transfected with the dCasMINI-VPR or dhpCasMINI-VPR system targeting IL1RN [RNA-seq] 18 samples shares hek293t, specific
- GSE219070 RNA-seq for HEK293T: a high-throughput method for R-loop mapping and quantitative assessment 2 samples shares hek293t, assess
- GSE86473 Single cell transcriptomics defines human islet cell signatures and reveals cell-type-specific expression changes in type 2 diabetes 662 samples shares cell-type, specific
- GSE86469 Single cell transcriptomics defines human islet cell signatures and reveals cell-type-specific expression changes in type 2 diabetes [single cell] 638 samples shares cell-type, specific
- GSE221156 Dissecting cell-type specific pathophysiological changes in human type-2 diabetic pancreatic islets using single-cell transcriptomic data 54 samples shares cell-type, specific
- GSE281106 Single-cell RNA-seq of peripheral blood links cell-type-specific regulation of splicing to autoimmune and inflammatory diseases [scRNAseq_KR_SGI] 352 samples shares cell-type, specific
- GSE280974 Single-cell RNA-seq of peripheral blood links cell-type-specific regulation of splicing to autoimmune and inflammatory diseases [scRNAseq_JP_RIK] 302 samples shares cell-type, specific
- GSE260603 Dissecting depression symptoms: multi-omics clustering uncovers immune-related subgroups and cell-type specific dysregulation 273 samples shares cell-type, specific
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.