DrugMap: A quantitative pan-cancer analysis of cysteine ligandability (RNA-Seq)
Single-cell RNA-seq · human · 69 samples
- What it is
- Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
- Organism
- Homo sapiens (human)
- Samples
- 69 samples: 21 conditions, mostly in triplicateGroups 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.
- Paper
- DrugMap: A quantitative pan-cancer analysis of cysteine ligandabilityCell · 2024
Read the full summary ↓
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 GSE256466_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: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.
Also filed as BioProject PRJNA1079745 and SRA study SRP491378. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 60 samples.
How we read this: 21 replicated groups read from 69 sample titles; they account for 60 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.
- GSM8098898 HMCB_siControl_F10
- GSM8098899 HMCB_siControl_G10
- GSM8098900 HMCB_siControl_H10
- GSM8098901 HMCB_siSOX10_C10
- GSM8098902 HMCB_siSOX10_D10
- GSM8098903 HMCB_siSOX10_E10
- GSM8098904 Mewo_siControl_A01 Mewo_siControl_A
- GSM8098905 Mewo_siControl_A03 Mewo_siControl_A
- GSM8098906 Mewo_siSOX10_A05 Mewo_siSOX10_A
- GSM8098907 Mewo_siSOX10_A06 Mewo_siSOX10_A
- GSM8098908 SK_MEL2_siControl_B01 SK_MEL2_siControl_B
- GSM8098909 SK_MEL2_siControl_B03 SK_MEL2_siControl_B
- GSM8098910 SK_MEL2_siSOX10_B04 SK_MEL2_siSOX10_B
- GSM8098911 SK_MEL2_siSOX10_B05 SK_MEL2_siSOX10_B
- GSM8098912 SK_MEL2_siSOX10_B06 SK_MEL2_siSOX10_B
- GSM8098913 SK_MEL5_siControl_D01 SK_MEL5_siControl_D
- GSM8098914 SK_MEL5_siControl_D03 SK_MEL5_siControl_D
- GSM8098915 SK_MEL5_siSOX10_D04 SK_MEL5_siSOX10_D
- GSM8098916 SK_MEL5_siSOX10_D05 SK_MEL5_siSOX10_D
- GSM8098917 SK_MEL28_siControl_C01 SK_MEL28_siControl_C
- GSM8098918 SK_MEL28_siControl_C03 SK_MEL28_siControl_C
- GSM8098919 SK_MEL28_siSOX10_C04 SK_MEL28_siSOX10_C
- GSM8098920 SK_MEL28_siSOX10_C05 SK_MEL28_siSOX10_C
- GSM8098921 SK_MEL28_siSOX10_C06 SK_MEL28_siSOX10_C
- GSM8098922 U62_siControl_H01 U62_siControl_H
- GSM8098923 U62_siControl_H03 U62_siControl_H
- GSM8098924 U62_siSOX10_H04 U62_siSOX10_H
- GSM8098925 U62_siSOX10_H06 U62_siSOX10_H
- GSM8098926 U257_siControl_E01 U257_siControl_E
- GSM8098927 U257_siControl_E03 U257_siControl_E
- GSM8098928 U257_siSOX10_E04 U257_siSOX10_E
- GSM8098929 U257_siSOX10_E05 U257_siSOX10_E
- GSM8098930 U257_siSOX10_E06 U257_siSOX10_E
- GSM8098931 C_DMSO_10_1
- GSM8098932 C_DMSO_11_1
- GSM8098933 C_DMSO_12_1
- GSM8098934 C_13_F10_16 C_13_F10
- GSM8098935 C_13_F10_17 C_13_F10
- GSM8098936 C_13_F10_18 C_13_F10
- GSM8098937 C_16_H03_13 C_16_H03
+ 29 more — browse all 69 samples with per-sample file links →
- GSE256467 DrugMap: A quantitative pan-cancer analysis of cysteine ligandability 71 samples shares ligandability, quantitative, pan-cancer, cysteine, drugmap
- GSE68086 RNA-seq of tumor-educated platelets enables blood-based pan-cancer, multiclass and molecular pathway cancer diagnostics 285 samples shares pan-cancer, rna-seq
- GSE320255 Pan-Cancer Analysis Reveals Mutation-Driven TE Dysregulation as a Contributor to Immune Suppression [RNA-seq] 14 samples shares pan-cancer, rna-seq
- GSE318881 RNA-Seq quantitative analysis of the regulatory effects of plasma exosomes from CML patients on CML cell transcriptomes 13 samples shares quantitative, rna-seq
- GSE324767 RNA-Seq quantitative analysis of the regulatory effects of circRBM6 and circMLLT1 on nasopharyngeal carcinoma cell transcriptomes 8 samples shares quantitative, rna-seq
- GSE272124 Pan-cancer analysis combining RNA-seq reveals that ZNF25 contributes to glioma progression mainly through modulating PI3K-AKT signaling pathway 6 samples shares pan-cancer, rna-seq
- GSE223145 siqRNA-seq is a spike-in-independent technique for quantitative mapping of mRNA landscape 50 samples shares quantitative, rna-seq
- GSE318329 RNA-Seq quantitative analysis of the regulation of imatinib-resistant cell-derived exosomes on CML cell transcriptomes 4 samples shares quantitative, 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.