README.md

In eudoraleer/Ursa: An Automated Multi-omics Package for Single-cell Analysis

Ursa: an automated multi-omics package for single-cell analysis

Ursa is an R package consisting of seven single-cell omics automated analysis workflows. One-liner command for each omics to run a full post-quantification analysis for the omics.

Six single-cell (sc) omics and one bulk omics include:

1. scRNA-sequencing (sc)
2. scATAC-sequencing (sc)
3. scImmune profiling (sc)
4. scCNV (sc)
5. CyTOF (sc)
6. Flow cytometry (sc)
7. Spatial transcriptomics (bulk)

Installation

Ursa can be installed in R via the command:

install.packages("devtools")
devtools::install_github("eudoraleer/Ursa")

Please download the example sample files and their meta files from the following link with respect to the omics you will be running. Original file sources can be found below. Multiple samples are supported if information in the meta data is corrected provided.

Running single-cell analysis with Ursa

1. scRNA-sequencing*

(1) Download example dataset from original source 10X or from the following link

The following input file(s) from the example data are needed in the input directory before running the analysis: - filtered gene matrix .h5 file: Feature / cell matrix HDF5 (filtered) - sample meta file (in .csv format) with the following file content:

(2) Set the downloaded file folder as working directory in R/RStudio:

(3) Run the analysis with the following commands:

library("Ursa")
scRNASEQPip(project_name = 'My_scRNASeq', pheno_file = 'Ursa_scRNA_Seq_Metadata_Example.csv')

(4) Example output files for project My_scRNASeq: link

2. scATAC-sequencing*

(1) Download example dataset from original source 10X or from the following link

For this omics, running this workflow on a computer with memory >=16GB is recommended due to large input file size The following input file(s) from the example data are needed in the input directory before running the analysis: - filtered peak matrix .h5 file: Peak by cell matrix HDF5 (filtered) - fragment file and its index file: Fragments (TSV), Fragments index (TBI) - single cell file: Per Barcode metrics (CSV, optional) - sample meta file (in .csv format) with the following file content:

(2) Set the downloaded file folder as working directory in R/RStudio:

(3) Run the analysis with the following commands:

library("Ursa")
scATACPip(project_name = 'My_scATAC', pheno_file = 'Ursa_scATAC_Seq_Metadata_Example.csv')

(4) Example output files for project My_scATAC: link

3. scImmune profiling*

Download example dataset from original source 10X or from the following link

The following input file(s) from the example data are needed in the input directory before running the analysis: - BCR or/and TCR contig CSV file: VDJ Ig - All contig annotations (CSV) - filtered gene matrix .h5 file (optional, only for multi-modal analysis): Gene Expression - Feature / cell matrix .h5 file (filtered) - sample meta file (in .csv format) with the following file content:

(2) Set the downloaded file folder as working directory in R/RStudio:

(3) Run the analysis with the following commands:

library("Ursa")
scImmunePip(project_name = 'My_scImmune', pheno_file = 'Ursa_scImmune_Profiling_Metadata_Example.csv')

(4) Example output files for project My_scImmune: link

4. scCNV*

Download example dataset from original source 10X or from the following link

The following input file(s) from the example data are needed in the input directory before running the analysis: - mappable regions BED file: Mappable regions (BED) - CNV calls: CNV calls (BED) - per cell summary metrics: Per-cell summary metrics (CSV) - sample meta file (in .csv format) with the following file content:

(2) Set the downloaded file folder as working directory in R/RStudio:

(3) Run the analysis with the following commands:

library("Ursa")
scCNVPip(project_name = 'My_scCNV', pheno_file = 'Ursa_scCNV_Metadata_Example.csv')

(4) Example output files for project My_scCNV: link

5. CyTOF

Download example dataset from original source Nowicka, M., et al. (2017) or from the following link

The following input file(s) from the example data are needed in the input directory before running the analysis: - .fcs input files - sample meta file (in .csv format) with the following file content:

(2) Set the downloaded file folder as working directory in R/RStudio:

(3) Run the analysis with the following commands:

library("Ursa")
CyTOFPip(project_name = 'My_CyTOF', pheno_file = 'Ursa_CyTOF_Metadata_Example.csv')

(4) Example output files for project My_CyTOF: link

6. Flow Cytometry

Download example dataset from original source Dillon Hammill,2021 or from the following link

The following input file(s) from the example data are needed in the input directory before running the analysis: - .fcs input files - sample meta file (in .csv format) with the following file content:

(2) Set the downloaded file folder as working directory in R/RStudio:

(3) Run the analysis with the following commands:

library("Ursa")
FlowPip(project_name = 'My_Flow', pheno_file = 'Ursa_Flow_Cytometry_Metadata_Example.csv')

(4) Example output files for project My_Flow: link

7. Spatial Transcriptomics

Download example dataset from original source 10X or from the following link

The following input file(s) from the example data are needed in the input directory before running the analysis: - filtered gene matrix .h5 file: Feature / barcode matrix HDF5 (filtered) - spatial imaging data: Spatial imaging data (please make sure the imaging data for each sample is placed in their corresponding folder with the .h5 file, with imaging data folder named 'spatial') - sample meta file (in .csv format) with the following file content:

(2) Set the downloaded file folder as working directory in R/RStudio:

(3) Run the analysis with the following commands:

library("Ursa")
SpatialPip(project_name = 'My_Spatial', pheno_file = 'Ursa_Spatial_Metadata_Example.csv')

(4) Example output files for project My_Spatial: link

*Registration is needed for downloading the data for the first time on 10X website. Subsequent download would no longer require any registration.

eudoraleer/Ursa documentation built on Nov. 26, 2022, 10:35 p.m.