Single cell spatial transcriptome and RNA-seq analysis in human lung tissues with mycobacterial infection: RNA-seq analysis
Single-cell RNA-seq · human · 37 samples
- What it is
- Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
- Organism
- Homo sapiens (human)
- Samples
- 37 samples: 5 conditions, mostly in duplicateGroups 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 GSE276060_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 PRJNA1154622 and SRA study SRP529698. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 19 samples.
How we read this: 5 replicated groups read from 37 sample titles; they account for 19 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.
- GSM8490465 18-070 18
- GSM8490466 18-071-15
- GSM8490467 18-072-9 18-072
- GSM8490468 18-072-11 18-072
- GSM8490469 18-073-7 18-073
- GSM8490470 18-073-14 18-073
- GSM8490471 18-074-4 18-074
- GSM8490472 18-074-16 18-074
- GSM8490473 18-075 18
- GSM8490474 18-076 18
- GSM8490475 18-077 18
- GSM8490476 18-078 18
- GSM8490477 18-079 18
- GSM8490478 18-080 18
- GSM8490479 18-081 18
- GSM8490480 18-82 18
- GSM8490481 Lu2748-9
- GSM8490482 Lu2757-14
- GSM8490483 Lu2760-7
- GSM8490484 Lu2774-7
- GSM8490485 tonsil-11 tonsil
- GSM8490486 tonsil-17 tonsil
- GSM8490487 tonsil-18 tonsil
- GSM8490488 tonsil-21 tonsil
- GSM8490489 Lu2778-7
- GSM8490490 24-064_RN
- GSM8490491 24-065_RN
- GSM8490492 24-066_Aspg
- GSM8490493 24-067_Aspg
- GSM8490494 24-068_Aspg
- GSM8490495 24-069_GPA
- GSM8490496 24-070_GPA
- GSM8490497 24-071_GPA
- GSM8490498 24-072_SA
- GSM8490499 24-073_SA
- GSM8490500 24-074_SA
- GSM8490501 24-075_AsP
- GSE308103 Single-nucleus RNA-sequencing (snRNA-seq) analysis of normal lung tissues, precursor lung lesions and invasive lung adenocarcinomas 75 samples shares rna-seq, lung
- GSE339456 Integrated bulk and spatial transcriptomic analysis identifies progression-associated molecular signatures in biopsy-proven hypertensive nephropathy [RNA-seq] 35 samples shares spatial, rna-seq
- GSE310058 Single-nucleus transcriptomic profiling of human lung tissue in chronic obstructive pulmonary disease (COPD) [snRNA-seq] 149 samples shares rna-seq, lung
- GSE272409 Molecular and spatial analysis unveils the functional basis of tertiary lymphoid structures in Sjogren’s syndrome[scRNA-seq] 13 samples shares spatial, rna-seq
- GSE348445 Identification and Characterization of Adrenergic and Mesenchymal Cancer Cell States in Neuroblastoma Using Spatial-Omics [RNA-Seq] 33 samples shares spatial, rna-seq
- GSE278688 Integrated Single-Cell and Spatial Transcriptomics Uncover Distinct Cellular Subtypes Involved in Neural Invasion in PDAC [scRNA-Seq] 29 samples shares spatial, rna-seq
- GSE319969 Spatial and Bulk Transcriptomic Profiling Defines the Molecular Evolution of Cutaneous Squamous Cell Carcinoma and Reveals Stage-Specific Biomarkers of Clinical Relevance [RNA-Seq] 24 samples shares spatial, rna-seq
- GSE271370 Spatial transcriptomics unveils the in situ cellular and molecular hallmarks of the lung in fatal COVID-19 23 samples shares spatial, lung
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.