https://github.com/hms-dbmi/dseqr
single-cell and bulk RNA-seq analyses from counts → pathways → drug candidates.
https://github.com/hms-dbmi/dseqr
bulk-rna-seq drug-repurposing rna-seq single-cell-rna-seq
Last synced: 3 months ago
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single-cell and bulk RNA-seq analyses from counts → pathways → drug candidates.
- Host: GitHub
- URL: https://github.com/hms-dbmi/dseqr
- Owner: hms-dbmi
- License: other
- Created: 2019-04-22T17:22:32.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2024-10-21T22:21:46.000Z (over 1 year ago)
- Last Synced: 2025-09-04T21:48:54.982Z (5 months ago)
- Topics: bulk-rna-seq, drug-repurposing, rna-seq, single-cell-rna-seq
- Language: R
- Homepage: https://docs.dseqr.com
- Size: 36.9 MB
- Stars: 20
- Watchers: 7
- Forks: 5
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: NEWS.md
- License: LICENSE
- Citation: CITATION.cff
Awesome Lists containing this project
- awesome-expression-browser - dseqr - seq data. Project website available at https://docs.dseqr.com/ (Software list)
README
[](https://github.com/hms-dbmi/dseqr/actions/workflows/ci.yml)
[](https://zenodo.org/badge/latestdoi/182834359)
## Dseqr
#### **End-to-End RNA-Seq Analysis**
Dseqr is a web application that helps you run 10X single-cell and bulk RNA-seq analyses from fastq → pathways → drug candidates.
💡 [Read the Docs and Open Dseqr →](https://docs.dseqr.com)
### Local setup
```R
# install
install.packages('remotes')
remotes::install_github('hms-dbmi/dseqr')
# initialize and run new project
library(dseqr)
project_name <- 'example'
# directory to store application and project files in
data_dir <- './dseqr'
run_dseqr(project_name, data_dir)
```
To enable bulk fastq.gz import, first build a `kallisto` index for quantification. To do so run:
```R
# default as used by run_dseqr
indices_dir <- file.path(data_dir, '.indices_dir')
rkal::build_kallisto_index(indices_dir)
```
### scRNA-seq fastqs
`dseqr` can directly import `cellranger` formatted count matrices. If you are starting
from fastq files, first install `kb-python`:
```console
# install kallisto|bustools wrapper (required)
pip install kb-python
```
Then run pseudo-quantification:
```R
# download pre-built index (mouse or human)
dseqr::download_kb_index(indices_dir, species = 'human')
# run pseudo-quantification
data_dir <- 'path/to/folder/with/fastqs'
dseqr::run_kb_scseq(indices_dir, data_dir, species = 'human')
# clean intermediate files produced by kb
dseqr::clean_kb_scseq(data_dir)
```
The resulting `cellranger` formatted count matrix files will be in the `data_dir`
subdirectory `bus_output/counts_unfiltered/cellranger`.
### Prefer docker?
```bash
# pull image
docker pull alexvpickering/dseqr
# run at http://0.0.0.0:3838/ and keep data on exit
docker run -v /full/path/to/data_dir:/srv/dseqr \
-p 3838:3838 \
alexvpickering/dseqr R -e 'library(dseqr); run_dseqr("example", "/srv/dseqr")'
```
### Host it
To spin up your own AWS infrastructure to host `dseqr`, see [dseqr.aws →](https://github.com/hms-dbmi/dseqr.aws)