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scROSHI

Robust Supervised Hierarchical Identification of Single Cells

Identifying cell types based on expression profiles is a pillar of single cell analysis. 'scROSHI' identifies cell types based on expression profiles of single cell analysis by utilizing previously obtained cell type specific gene sets. It takes into account the hierarchical nature of cell type relationship and does not require training or annotated data. A detailed description of the method can be found at: Prummer, Bertolini, Bosshard, Barkmann, Yates, Boeva, The Tumor Profiler Consortium, Stekhoven, and Singer (2022) <doi:10.1101/2022.04.05.487176>.

README

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# scROSHI

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scROSHI identifies cell types based on expression profiles of single
cell analysis by utilizing previously obtained cell type specific gene
sets. It takes into account the hierarchical nature of cell type
relationship and does not require training or annotated data. A detailed
description of the method can be found at: Prummer et al. 2022.
“scROSHI - robust supervised hierarchical identification of single
cells”. bioRxiv.
<https://www.biorxiv.org/content/10.1101/2022.04.05.487176v1>

## Installation

You can install the development version from
[GitHub](https://github.com/) (required R version \>= 3.6) with:

``` r
# install.packages("devtools")
devtools::install_github("ETH-NEXUS/scROSHI")
```

## Example

This is a basic example for the scROSHI function

scROSHI requires three input objects:

*sce_data*

A SingleCellExperiment object containing the expression profiles of the
single cell analysis

*celltype_lists*

Marker gene list for all cell types. It can be provided as a list of
genes with cell types as names or as a path to a file containing the
marker genes. Supported file formats are .gmt or .gmx files.

*type_config*

Config file to define major cell types and hierarchical subtypes. It
should be provided as a two-column data.frame where the first column are
the major cell types and the second column are the subtypes. If several
subtypes exists they should be separated by comma.

``` r
library(scROSHI)
data("test_sce_data")
data("config")
data("marker_list")

results <- scROSHI(sce_data = test_sce_data,
                  celltype_lists = marker_list,
                  type_config = config)
table(results$celltype_final)
#> 
#>                      B.cells                B.cells.naive 
#>                            4                          180 
#>            B.cells.precursor              Dendritic.cells 
#>                           40                           40 
#>                    Monocytes                     NK.cells 
#>                          233                          219 
#>                 Plasma.cells Plasmacytoid.dendritic.cells 
#>                           13                           11 
#>                      T.cells                  T.cells.CD4 
#>                           60                          415 
#>                  T.cells.CD8                    uncertain 
#>                           76                           25
```

Versions across snapshots

VersionRepositoryFileSize
1.0.0.0 rolling linux/jammy R-4.5 scROSHI_1.0.0.0.tar.gz 681.0 KiB
1.0.0.0 rolling linux/noble R-4.5 scROSHI_1.0.0.0.tar.gz 681.0 KiB
1.0.0.0 rolling source/ R- scROSHI_1.0.0.0.tar.gz 681.0 KiB
1.0.0.0 latest linux/jammy R-4.5 scROSHI_1.0.0.0.tar.gz 681.0 KiB
1.0.0.0 latest linux/noble R-4.5 scROSHI_1.0.0.0.tar.gz 681.0 KiB
1.0.0.0 latest source/ R- scROSHI_1.0.0.0.tar.gz 681.0 KiB
1.0.0.0 2026-04-26 source/ R- scROSHI_1.0.0.0.tar.gz 681.0 KiB
1.0.0.0 2026-04-23 source/ R- scROSHI_1.0.0.0.tar.gz 681.0 KiB
1.0.0.0 2025-04-20 source/ R- scROSHI_1.0.0.0.tar.gz 681.0 KiB

Dependencies (latest)

Imports