Microbial Dynamics and Pulmonary Immune Responses in COVID-19 Secondary Bacterial Pneumonia
RNA-seq (gene expression) · human · 56 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
- 56 samples: 56 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.
- Paper
- Microbial dynamics and pulmonary immune responses in COVID-19 secondary bacterial pneumoniaNature communications · 2024
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 GSE246795_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 PRJNA1034622. 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 56 sample titles: 56 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.
- GSM7877165 MVIR1-HS1-D3ETA1-MNGS1
- GSM7877166 MVIR1-HS2-D2ETA1-MNGS1
- GSM7877167 MVIR1-HS47-D19ETA1-MNGS1
- GSM7877168 MVIR1-HS51-D1ETA1-MNGS1
- GSM7877169 MVIR1-HS55-D9ETA1-MNGS1
- GSM7877170 MVIR1-HS72-D14ETA1-MNGS1
- GSM7877171 MVIR1-HS78-D4ETA1-MNGS1
- GSM7877172 MVIR1-HS94-D20ETA1-MNGS1
- GSM7877173 MVIR1-HS97-D8ETA1-MNGS1
- GSM7877174 MVIR1-HS114-D3ETA1-MNGS1
- GSM7877175 MVIR1-HS115-D16ETA1-MNGS1
- GSM7877176 MVIR1-HS145-D1ETA1-MNGS1
- GSM7877177 MVIR1-HS154-D15ETA1-MNGS1
- GSM7877178 MVIR1-HS156-D0ETA1-MNGS1
- GSM7877179 MVIR1-HS158-D6ETA1-MNGS1
- GSM7877180 MVIR1-HS161-D10ETA1-MNGS1
- GSM7877181 MVIR1-HS168-D0ETA1-MNGS1
- GSM7877182 MVIR1-HS172-D8ETA1-MNGS1
- GSM7877183 MVIR1-HS185-D1ETA1-MNGS1
- GSM7877184 MVIR1-HS196-D19ETA1-MNGS1
- GSM7877185 MVIR1-HS204-D0ETA1-MNGS1
- GSM7877186 MVIR1-HS223-D3ETA1-MNGS1
- GSM7877187 MVIR1-HS231-D4ETA1-MNGS1
- GSM7877188 MVIR1-HS233-D3ETA1-MNGS1
- GSM7877189 MVIR1-HS236-D7ETA1-MNGS1
- GSM7877190 MVIR1-HS238-DN1ETA1-MNGS1
- GSM7877191 MVIR1-HS243-D6ETA1-MNGS1
- GSM7877192 MVIR1-HS244-D5ETA1-MNGS1
- GSM7877193 MVIR1-HS250-D23ETA1-MNGS1
- GSM7877194 MVIR1-HS253-D3ETA1-MNGS1
- GSM7877195 MVIR1-HS254-D14ETA1-MNGS1
- GSM7877196 MVIR1-HS271-D9ETA1-MNGS1
- GSM7877197 MVIR1-HS290-D9ETA1-MNGS1
- GSM7877198 MVIR1-HS292-D15ETA1-MNGS2
- GSM7877199 MVIR1-HS320-D10ETA1-MNGS1
- GSM7877200 MVIR1-HS357-D10ETA1-MNGS2
- GSM7877201 MVIR1-HS410-D4ETA1-MNGS1
- GSM7877202 MVIR1-HS419-D3ETA1-MNGS1
- GSM7877203 MVIR1-HS427-D7ETA1-MNGS1
- GSM7877204 MVIR1-HS441-D6ETA1-MNGS1
+ 16 more — browse all 56 samples with per-sample file links →
- GSE212861 Dynamics of gene expression profiling by RNA-Seq and identification of high-risk patients for severe COVID-19 [RNA-Seq] 93 samples shares covid-19, dynamics
- GSE237180 Distinct pulmonary and systemic effects of dexamethasone in severe COVID-19. 45 samples shares covid-19, pulmonary
- GSE236030 Distinct pulmonary and systemic effects of dexamethasone in severe COVID-19 24 samples shares covid-19, pulmonary
- GSE212866 Dynamics of gene expression profiling by microarrays and identification of high-risk patients for severe COVID-19 230 samples shares covid-19, dynamics
- GSE288527 Human giant GTPase GVIN1 forms an antimicrobial coatomer around the intracellular bacterial pathogen Burkholderia thailandensis 40 samples shares microbial, bacterial
- GSE264443 Dynamic gene expression analysis reveals distinct severity phases of immune and cellular dysregulation in COVID-19 189 samples shares covid-19
- GSE336577 Integrated Transcriptomic and Immune Analysis Reveals Distinct Mitochondrial and Immune Signature in COVID-19 ARDS Requiring Invasive Mechanical Ventilation 94 samples shares covid-19
- GSE290188 Minimal impact of ivermectin on immune response and transcriptional profiles in naïve adults with mild COVID-19 72 samples shares covid-19
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.