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 →
# 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:
# default as used by run_dseqr
indices_dir <- file.path(data_dir, ".indices_dir")
rkal::build_kallisto_index(indices_dir)dseqr can directly import cellranger formatted count matrices. If you are starting
from fastq files, first install kb-python:
# install kallisto|bustools wrapper (required)
pip install kb-pythonThen run pseudo-quantification:
# 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.
# 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")"To spin up your own AWS infrastructure to host dseqr, see dseqr.aws →