The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.

AnanseSeurat package

The AnanseSeurat package takes pre-processed clustered single cell objects of scRNAseq and scATACseq or a multiome combination, and generates files for gene regulatory network (GRN) analysis. It is part of the scANANSE pipeline. https://doi.org/10.12688/f1000research.130530.1

Installation

AnanseSeurat can be installed from CRAN using

install.packages('AnanseSeurat')

Or to install the developmental branch from github:

library(devtools) # Tools to Make Developing R Packages Easier # Tools to Make Developing R Packages Easier
Sys.unsetenv("GITHUB_PAT")
remotes::install_github("JGASmits/AnanseSeurat@main")

Usage

library("AnanseSeurat")
rds_file <- './scANANSE/preprocessed_PDMC.Rds'
pbmc <- readRDS(rds_file)

Next you can output the data from your single cell object, the file format, config file and sample file are all ready to automate GRN analysis using anansnake. https://github.com/vanheeringen-lab/anansnake

export_CPM_scANANSE(
  pbmc,
  min_cells = 25,
  output_dir = './scANANSE/analysis',
  cluster_id = 'predicted.id',
  RNA_count_assay = 'RNA'
)

export_ATAC_scANANSE(
  pbmc,
  min_cells = 25,
  output_dir = './scANANSE/analysis',
  cluster_id = 'predicted.id',
  ATAC_peak_assay = 'peaks'
)

# Specify additional contrasts:
contrasts <-  c('B-naive_B-memory',
                'B-memory_B-naive',
                'B-naive_CD14-Mono',
                'CD14-Mono_B-naive')

config_scANANSE(
  pbmc,
  min_cells = 25,
  output_dir = './scANANSE/analysis',
  cluster_id = 'predicted.id',
  additional_contrasts = contrasts
)

DEGS_scANANSE(
  pbmc,
  min_cells = 25,
  output_dir = './scANANSE/analysis',
  cluster_id = 'predicted.id',
  additional_contrasts = contrasts
)

install and run anansnake

Follow the instructions its respective github page, https://github.com/vanheeringen-lab/anansnake After activating the conda environment, use the generated files to run GRN analysis using your single cell cluster data:

anansnake \
--configfile scANANSE/analysis/config.yaml \
--resources mem_mb=48_000 --cores 12

import ANANSE results back to your single cell object

After running Anansnake, you can import the TF influence scores back into your single cell object of choice

pbmc <- import_seurat_scANANSE(pbmc,
                               cluster_id = 'predicted.id',
                               anansnake_inf_dir = "./scANANSE/analysis/influence")
TF_influence <- per_cluster_df(pbmc,
                               cluster_id = 'predicted.id',
                               assay = 'influence')

Citation

scANANSE gene regulatory network and motif analysis of single-cell clusters [version 1; peer review: awaiting peer review] Jos G.A. Smits, Julian A. Arts, Siebren Frölich, Rebecca R. Snabel, Branco M.H. Heuts, Joost H.A. Martens, Simon J van Heeringen, Huiqing Zhou F1000Research 2023, 12:243 (https://doi.org/10.12688/f1000research.130530.1)

Thanks to:

• Julian A. Arts and his Pycharm equivalent of this package https://github.com/Arts-of-coding/AnanseScanpy
• Siebren Frohlich and his anansnake implementation https://github.com/vanheeringen-lab/anansnake
• Rebecca R. Snabel for her implementation of the motif expression correlation analysis
• Branco Heuts for testing

Credits

The hex sticker is generated using the hexSticker package.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.