Assessment of the Anti-Tumor Effects of Poly(I:C) Combined with QS-21 on Melanoma Cells(B16F10)
RNA-seq (gene expression) · mouse · 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
- Mus musculus (mouse)
- Samples
- 12 samples: 3 × V vs 3 × P vs 3 × Q vs 3 × AGroups 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 GSE287957_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 PRJNA1215363 and SRA study SRP559787. 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.
- GSM8756330 V1 V
- GSM8756331 V2 V
- GSM8756332 V3 V
- GSM8756333 P1 P
- GSM8756334 P2 P
- GSM8756335 P3 P
- GSM8756336 Q1 Q
- GSM8756337 Q2 Q
- GSM8756338 Q3 Q
- GSM8756339 A1 A
- GSM8756340 A2 A
- GSM8756341 A3 A
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- GSE329033 Metabolic inhibition of glutamate-cysteine ligase increases dendritic cell-mediated anti-tumor immunity in melanoma 20 samples shares anti-tumor, melanoma
- GSE300712 RAS(ON) multi-selective inhibition drives anti-tumor immunity in preclinical models of NRAS-mutant melanoma 12 samples shares anti-tumor, melanoma
- GSE342584 Tumor-targeted IL-10 combined with IL-2 amplifies anti-tumor immunity 3 samples shares anti-tumor, combined
- GSE219143 RNA-seq of B16F10 cancer cells treated with different conditioned media from cancer cell-induced TAM 12 samples shares b16f10
- GSE318796 RNA-seq analysis of B16F10 cells following p300/CBO knockout 12 samples shares b16f10
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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.