RNA-seq profiling of breast metastases from different sites
RNA-seq (gene expression) · human · 42 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
- 42 samples: 42 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 GSE245414_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 PRJNA1028299 and SRA study SRP466422. 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 42 sample titles: 42 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.
- GSM7842221 B-Met-001-1T-R
- GSM7842222 B-Met-003-1T-R
- GSM7842223 B-Met-008-1T-R
- GSM7842224 B-Met-025-1T-R
- GSM7842225 B-Met-026-1T-R
- GSM7842226 B-Met-031-1T-R
- GSM7842227 B-Met-049-1T-R
- GSM7842228 B-Met-052-1T-R
- GSM7842229 B-Met-053-1T-R
- GSM7842230 B-Met-055-1T-R
- GSM7842231 B-Met-076-1T-R
- GSM7842232 B-Met-086-1T-R
- GSM7842233 B-Met-088-1T-R
- GSM7842234 B-Met-108-1T-R
- GSM7842235 B-Met-110-1T-R
- GSM7842236 B-Met-113-1T-R
- GSM7842237 B-Met-117-1T-R
- GSM7842238 B-Met-123-1T-R
- GSM7842239 B-Met-128-1T-R
- GSM7842240 B-Met-132-1T-R
- GSM7842241 B-Met-136-1T-R
- GSM7842242 B-Met-137-1T-R
- GSM7842243 B-Met-138-1T-R
- GSM7842244 B-Met-139-1T-R
- GSM7842245 B-Met-144-1T-R
- GSM7842246 B-Met-150-1T-R
- GSM7842247 B-Met-155-1T-R
- GSM7842248 B-Met-158L-1T-R
- GSM7842249 B-Met-160-1T-R
- GSM7842250 B-Met-104-1T-R
- GSM7842251 B-Met-176-1T-R
- GSM7842252 B-Met-180-1T-R
- GSM7842253 B-Met-003-1O-R
- GSM7842254 B-Met-163-1T-R
- GSM7842255 B-Met-191-1T-R
- GSM7842256 B-Met-195-1T-R
- GSM7842257 B-Met-207K-1T-R
- GSM7842258 B-Met-211-1T-R
- GSM7842259 B-Met-212-1T-R
- GSM7842260 B-Met-214-1T-R
+ 2 more — browse all 42 samples with per-sample file links →
- GSE319641 RNA-Seq profiling of pre- and on-treatment tumor samples from breast cancer patients enrolled in the NeoTRIP trial. 401 samples shares rna-seq, breast
- GSE261713 Linking single cell genomes and transcriptomes at scale to decode breast cancer progression [scRNA-seq] 35 samples shares rna-seq, breast
- GSE303346 Single-Cell RNA-Seq Reveals Immunosuppressive Effects of Triple-Negative Breast Cancer–Derived Exosomes on NK Cell Responses 16 samples shares rna-seq, breast
- GSE310797 Single cell and Spatial RNA-seq in Oral Squamous Cell Carcinoma with Different Stage 19 samples shares different, rna-seq
- GSE323973 Expression profiling by high throughput sequencing of breast cancer 158 samples shares breast
- GSE327707 Sorted bulk RNA-seq profiling of human peripheral blood mononuclear cell populations across diverse in vitro immune stimuli 150 samples shares rna-seq
- GSE310058 Single-nucleus transcriptomic profiling of human lung tissue in chronic obstructive pulmonary disease (COPD) [snRNA-seq] 149 samples shares rna-seq
- GSE327167 Single-cell RNA-seq profiling of pleural mesothelioma and comparator pleural specimens 113 samples shares rna-seq
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.