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dseqr

single-cell and bulk RNA-seq analyses from counts → pathways → drug candidates.

https://github.com/hms-dbmi/dseqr

Science Score: 62.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
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    Found .zenodo.json file
  • ○
    DOI references
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    Academic publication links
    Links to: zenodo.org
  • ○
    Academic email domains
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    Institutional organization owner
    Organization hms-dbmi has institutional domain (dbmi.hms.harvard.edu)
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (11.4%) to scientific vocabulary

Keywords

bulk-rna-seq drug-repurposing rna-seq single-cell-rna-seq
Last synced: 8 months ago · JSON representation ·

Repository

single-cell and bulk RNA-seq analyses from counts → pathways → drug candidates.

Basic Info
  • Host: GitHub
  • Owner: hms-dbmi
  • License: other
  • Language: R
  • Default Branch: master
  • Homepage: https://docs.dseqr.com
  • Size: 36.9 MB
Statistics
  • Stars: 20
  • Watchers: 7
  • Forks: 4
  • Open Issues: 0
  • Releases: 2
Topics
bulk-rna-seq drug-repurposing rna-seq single-cell-rna-seq
Created about 7 years ago · Last pushed over 1 year ago
Metadata Files
Readme Changelog License Citation

README.md

CI DOI <!-- badges: end -->

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 →

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'

rundseqr(projectname, 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

indicesdir <- file.path(datadir, '.indices_dir')

rkal::buildkallistoindex(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::downloadkbindex(indices_dir, species = 'human')

run pseudo-quantification

datadir <- 'path/to/folder/with/fastqs' dseqr::runkbscseq(indicesdir, data_dir, species = 'human')

clean intermediate files produced by kb

dseqr::cleankbscseq(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/datadir:/srv/dseqr \ -p 3838:3838 \ alexvpickering/dseqr R -e 'library(dseqr); rundseqr("example", "/srv/dseqr")' ```

Host it

To spin up your own AWS infrastructure to host dseqr, see dseqr.aws →

Owner

  • Name: Harvard Medical School - Department of Biomedical Informatics
  • Login: hms-dbmi
  • Kind: organization
  • Location: Boston

Citation (CITATION.cff) 

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- family-names: "Pickering"
  given-names: "Alex"
  orcid: "https://orcid.org/0000-0002-0002-6759"
title: "dseqr"
version: 0.35.0
date-released: 2023-01-18
url: "https://github.com/hms-dbmi/dseqr"

GitHub Events

Total
  • Delete event: 1
  • Push event: 1
  • Pull request event: 2
Last Year
  • Delete event: 1
  • Push event: 1
  • Pull request event: 2

Dependencies

DESCRIPTION cran
  • R >= 4.1.0 depends
  • AnnotationDbi >= 1.48.0 imports
  • Biobase >= 2.46.0 imports
  • BiocManager * imports
  • DT >= 0.11 imports
  • DropletUtils >= 1.6.1 imports
  • FNN * imports
  • GO.db >= 3.10.0 imports
  • HDF5Array * imports
  • MASS * imports
  • Matrix * imports
  • Matrix.utils >= 0.9.7 imports
  • S4Vectors * imports
  • Seurat * imports
  • SeuratObject * imports
  • SingleCellExperiment * imports
  • SingleR >= 1.1.6 imports
  • SummarizedExperiment * imports
  • TSP * imports
  • batchelor >= 1.3.9 imports
  • callr * imports
  • celldex * imports
  • cowplot * imports
  • crossmeta * imports
  • data.table >= 1.12.8 imports
  • digest * imports
  • dplyr >= 0.8.3 imports
  • dseqr.data * imports
  • dtangle >= 2.0.9 imports
  • edgeR >= 3.28.0 imports
  • fs * imports
  • ggplot2 >= 3.2.1 imports
  • harmony >= 0.1.0 imports
  • hdf5r >= 1.3.1 imports
  • htmltools * imports
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  • igraph >= 1.2.4.2 imports
  • jsonlite * imports
  • limma >= 3.40.2 imports
  • magrittr >= 1.5 imports
  • methods * imports
  • picker >= 0.2.6 imports
  • plotly * imports
  • presto * imports
  • qs * imports
  • repel * imports
  • rhandsontable * imports
  • rintrojs >= 0.2.2 imports
  • rkal >= 0.1.12 imports
  • rlang >= 0.4.3 imports
  • scDblFinder * imports
  • scales * imports
  • scater >= 1.15.12 imports
  • scran >= 1.14.5 imports
  • shiny >= 1.4.0 imports
  • shinyBS >= 0.61 imports
  • shinyWidgets >= 0.5.0 imports
  • shinydlplot >= 0.1.0 imports
  • shinyjs >= 1.1 imports
  • shinypanel >= 0.1.0 imports
  • stats * imports
  • stringr * imports
  • symphony >= 0.1.0 imports
  • tibble >= 2.1.3 imports
  • tidyr * imports
  • utils * imports
  • scuttle * suggests
  • shinytest2 * suggests
  • testthat >= 3.0.0 suggests
.github/workflows/ci.yml actions
  • actions/checkout v2 composite
  • docker/build-push-action v2 composite
  • docker/build-push-action v3 composite
  • docker/login-action v2 composite
  • docker/setup-buildx-action v2 composite
Dockerfile docker
  • common latest build
  • rocker/r-ver 4.2.0 build