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)

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.

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:

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

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:

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

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:

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

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:

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

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:

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

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:

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

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:

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