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https://github.com/rcannood/SCORPIUS

Linear trajectory inference for single-cell RNA-seq
https://github.com/rcannood/SCORPIUS

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Linear trajectory inference for single-cell RNA-seq

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README 

        
---

output:

  github_document:

    html_preview: false

editor_options: 

  chunk_output_type: console

bibliography: library.bib

---



```{r setup1, include=FALSE}

knitr::opts_chunk$set(fig.path = "man/figures/README_", warning = FALSE, message = FALSE, error = FALSE, echo = TRUE)



submission_to_cran <- TRUE

library(tidyverse)

```



# SCORPIUS



[![R-CMD-check](https://github.com/rcannood/SCORPIUS/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/rcannood/SCORPIUS/actions/workflows/R-CMD-check.yaml)

[![CRAN_Status_Badge](https://www.r-pkg.org/badges/version/SCORPIUS)](https://cran.r-project.org/package=SCORPIUS)

[![Codecov test coverage](https://codecov.io/gh/rcannood/SCORPIUS/branch/master/graph/badge.svg)](https://app.codecov.io/gh/rcannood/SCORPIUS?branch=master)



SCORPIUS an unsupervised approach for inferring linear developmental chronologies from single-cell

RNA sequencing data. In comparison to similar approaches, it has three main advantages:



* **It accurately reconstructs linear dynamic processes.** 

  The performance was evaluated using a quantitative evaluation pipeline and

  ten single-cell RNA sequencing datasets. 

  

* **It automatically identifies marker genes, speeding up knowledge discovery.**

  

* **It is fully unsupervised.** Prior knowledge of the relevant marker genes or 

  cellular states of individual cells is not required.

  

News: 



* See `news(package = "SCORPIUS")` for a full list of changes to the package.



* Our preprint is on [bioRxiv](https://biorxiv.org/content/early/2016/10/07/079509)

  [@Cannoodt2016].



* Check out our [review](https://dx.doi.org/10.1038/s41587-019-0071-9) on Trajectory Inference methods

  [@Saelens2019].

  

## Installing SCORPIUS



You can install:



* the latest released version from CRAN with



    ```R

    install.packages("SCORPIUS")

    ```



* the latest development version from GitHub with



    ```R

    devtools::install_github("rcannood/SCORPIUS", build_vignettes = TRUE)

    ```



If you encounter a bug, please file a minimal reproducible example on the [issues](https://github.com/rcannood/SCORPIUS/issues) page. 



## Learning SCORPIUS



To get started, read the introductory example below, or read one of the vignettes containing more elaborate examples:



```{r vignettes, results='asis', echo=FALSE}

walk(

  list.files("vignettes", pattern = "*.Rmd"),

  function(file) {

    title <- 

      read_lines(paste0("vignettes/", file)) %>% 

      keep(~grepl("^title: ", .)) %>% 

      gsub("title: \"(.*)\"", "\\1", .)

    vignette_name <- gsub("\\.Rmd", "", file)

    cat(

      "* ", title, ":  \n",

      "`vignette(\"", vignette_name, "\", package=\"SCORPIUS\")`\n", 

      sep = ""

    )

  }

)

```



## Introductory example



```{r, echo=F}

set.seed(1)

```



This section describes the main workflow of SCORPIUS without going in depth in the R code. For a more detailed explanation, see the vignettes listed below.



To start using SCORPIUS, simply write:

```{r libraries, message=FALSE}

library(SCORPIUS)

```



The `ginhoux` dataset [@Schlitzer2015] contains 248 dendritic cell progenitors in one of three cellular cellular states: MDP, CDP or PreDC. Note that this is a reduced version of the dataset, for packaging reasons. See ?ginhoux for more info.

```{r load_ginhoux}

data(ginhoux)

expression <- ginhoux$expression

group_name <- ginhoux$sample_info$group_name

```



With the following code, SCORPIUS reduces the dimensionality of the dataset and provides a visual overview of the dataset. 

In this plot, cells that are similar in terms of expression values will be placed closer together than cells with dissimilar expression values.



```{r reduce_dimensionality}

space <- reduce_dimensionality(expression, "spearman")

draw_trajectory_plot(space, group_name, contour = TRUE)

```



To infer and visualise a trajectory through these cells, run:

```{r infer_trajectory}

traj <- infer_trajectory(space)

draw_trajectory_plot(space, group_name, traj$path, contour = TRUE)

```



To identify candidate marker genes, run:

```{r find_tafs, message=F, warning=F}

# warning: setting num_permutations to 10 requires a long time (~30min) to run!

# set it to 0 and define a manual cutoff for the genes (e.g. top 200) for a much shorter execution time.

gimp <- gene_importances(

  expression, 

  traj$time, 

  num_permutations = 10, 

  num_threads = 8, 

  ntree = 10000,

  ntree_perm = 1000

) 

```



To select the most important genes and scale its expression, run:

```{r calculate_pvalue, message=F, warning=F}

gimp$qvalue <- p.adjust(gimp$pvalue, "BH", length(gimp$pvalue))

gene_sel <- gimp$gene[gimp$qvalue < .05]

expr_sel <- scale_quantile(expression[,gene_sel])

```



```{r echo=F}

# reverse the trajectory. This does not change the results in any way,

# other than the heatmap being ordered more logically.

# traj <- reverse_trajectory(traj)

```



To visualise the expression of the selected genes, use the `draw_trajectory_heatmap` function.

```{r visualise_tafs, fig.keep='first'}

draw_trajectory_heatmap(expr_sel, traj$time, group_name)

```



Finally, these genes can also be grouped into modules as follows: 

```{r moduled_tafs, fig.keep='first'}

modules <- extract_modules(scale_quantile(expr_sel), traj$time, verbose = F)

draw_trajectory_heatmap(expr_sel, traj$time, group_name, modules)

```



## References