Transcriptome analysis of TNFα or necroptosis-induced stimulation in the NCI-H441 cell line with RIPK3 knocked down by siRNA.
RNA-seq (gene expression) · human · 9 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
- 9 samples: 9 samples, each with a distinct title — no replicated groupsGroups 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 GSE270211_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 PRJNA1125417 and SRA study SRP514759. Searching any of these in the dataset finder brings you back here.
Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.
How we read this: Read from 9 sample titles: 9 distinct titles with little repetition. 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.
- GSM8336875 siRNA NC_no stimulation
- GSM8336876 siRNA NC_TNFα
- GSM8336877 siRNA NC_TZB
- GSM8336878 siRNA RIPK3 #1_no stimulation
- GSM8336879 siRNA RIPK3 #1_TNFα
- GSM8336880 siRNA RIPK3 #1_TZB
- GSM8336881 siRNA RIPK3 #2_no stimulation
- GSM8336882 siRNA RIPK3 #2_TNFα
- GSM8336883 siRNA RIPK3 #2_TZB
- GSE319877 Transcriptomic profiling of human RPTECs following TNFα and IFNγ stimulation at early and late time points 20 samples shares tnf, stimulation
- GSE259401 Effects of depletion of EPAS1 and knocking down TNFSF10 in 786-O cell lines. 8 samples shares tnf, line
- GSE51221 Transcriptional response to TNF-alpha in a panel of 17 human melanoma cell lines 34 samples shares tnf, line
- GSE270661 Characterisation of the regulatory landscape in Crohn’s disease reveals miRNA-associated alterations that shape anti-TNF response (CIBER-120) 119 samples shares tnf
- GSE339038 RNA-Seq of CD3+ T cells flow sorted from MLR assays with TNF/TNFR inhibitory compound combination treatments 64 samples shares tnf
- GSE266348 TNF-mediated cell death: an actionable target for immunotherapy in T-ALL 50 samples shares tnf
- GSE272019 Opposing Regulation of TNF Responses and IL-1b+ Macrophages by PGE2-cAMP Signaling and IFN-g [RNA-seq] 48 samples shares tnf
- GSE317789 Single-cell and spatial profiling reveal cDC2A-CXCL13+ CD8+ T-epithelial cell crosstalk and cytotoxicity through TNFRSF9 in cutaneous and mucosal lichen planus 42 samples shares tnf
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.