dcats

This is a read-only mirror of the git repos at https://bioconductor.org

https://github.com/bioc/dcats

Repository

This is a read-only mirror of the git repos at https://bioconductor.org

README.Rmd

---
output: github_document
---



```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# DCATS




This R package contains methods to detect the differential composition
abundances between multiple conditions in singel-cell experiments.

The **latest** version of the `DCATS` package is 0.99.7. It is under the MIT license.

## Installation

### From R

The **latest** `DCATS` package can be conveniently installed using the
[`devtools`](https://www.rstudio.com/products/rpackages/devtools/)
package thus:

```{r, eval=FALSE}
## install dependencies
install.packages(c("MCMCpack", "matrixStats", "robustbase", "aod", "e1071"))
## dependencies for vignette
install.packages(c("SeuratObject", "Seurat", "robustbase", "aod", "e1071"))
devtools::install_github('satijalab/seurat-data')
```

``` r
# install.packages("devtools")
devtools::install_github("holab-hku/DCATS", build_vignettes = TRUE)
```

You can also install `DCATS` without building the vignette:

```
devtools::install_github("holab-hku/DCATS")
```

### From Biocounductor (required R >= 4.3.0)

```{r, eval=FALSE}
if (!requireNamespace("BiocManager"))
install.packages("BiocManager")
BiocManager::install("DCTAS")
```


#### For development

Download this repository to your local machine and open it in Rstudio as
a project, and build it by install and restart.

## Getting started

The best place to start are the vignettes. Inside an R session,
load `DCATS` and then browse the vignette about the usage guidance of `DCATS`:

``` r
library(DCATS)
browseVignettes("DCATS")
```

The tutorial demonstrating how to use DCATS after using [`Seurat`](https://satijalab.org/seurat/index.html) pipeline to process data can be found in [`Integrate DCATS with Seurat pipeline`](https://htmlpreview.github.io/?https://github.com/linxy29/DCATS_anlysis/blob/master/vignette/Integrate_with_seurat.html).

### Example

This is a basic example which shows you how to estimate a similarity matrix from KNN graph and do the differential abundance test using this similarity matrix.

```{r, results="hide",message=FALSE,warning=FALSE}
library(DCATS)
data("simulation")
knn_mat = knn_simMat(simulation$knnGraphs, simulation$labels)
sim_count = rbind(simulation$numb_cond1, simulation$numb_cond2)
sim_design = data.frame(condition = c("c1", "c1", "c2"))
knn_mat[colnames(sim_count),]
res = dcats_GLM(as.matrix(sim_count), sim_design, similarity_mat = knn_mat)
print(res$LRT_pvals)
```

Owner

Citation (CITATION.cff)

# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!

cff-version: 1.2.0
title: DCTAS
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: Xinyi
    family-names: Lin
    email: linxy29@connect.hku.hk
    affiliation: The University of Hong Kong
    orcid: 'https://orcid.org/0000-0002-7780-2461'
  - given-names: Chuen
    family-names: Chau
  - given-names: Kun
    family-names: Ma
  - given-names: 'Yuanhua '
    family-names: Huang
  - given-names: 'Joshua '
    family-names: Ho
    name-particle: 'W.K. '
repository-code: 'https://github.com/holab-hku/DCATS/tree/master'
abstract: >-
  This is a repository for an R package named DCATS that
  contains methods to detect the differential composition
  abundances between multiple conditions in singel-cell
  experiments.
license: MIT
commit: 63bc93c
version: 0.99.6
date-released: '2023-05-25'

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