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https://github.com/soedinglab/merlot

Reconstruct the lineage topology of a scRNA-seq differentiation dataset.
https://github.com/soedinglab/merlot

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Reconstruct the lineage topology of a scRNA-seq differentiation dataset.

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README 

        
# MERLot: A MEthod for Reconstructing Lineage tree Topologies using scRNA-seq data



MERLot is a tool that can reconstruct the lineage tree topology that explains the emergence of different cell types from a progenitor population. MERLot is an R package than can reconstruct complex lineage tree topologies using coordinates for cells in a given manifold(like diffusion maps) as input.



## Get ready



## 1) Python Dependencies:

MERLoT consists of 1 part written in Python, which is distributed with the R package for which the following packages need to be installed. Take into account that MERLoT uses python 3.



* scipy

* pandas

* python3-tk

* numpy

* cython



In case you install packages via pip you can simply do:

`sudo pip3 install scipy pandas python3-tk numpy cython`



* csgraph_mod (modified version of csgraph) which you can install from here: https://github.com/soedinglab/csgraph_mod



**NOTE:**

In case you don’t have a standard python3 installation, e.g you installed it using anaconda, when using the package you will need to set the location of your working python3 binary in the _python_url_ variable in the _ScaffoldTree()_ function. By default it is set to “/usr/bin/python3” (See the Vignette Section, ScaffoldTree() function, for more information).



## 2) R Dependencies:

MERLoT depends on certain R packages in order to work properly. Most of the packages can be installed either via CRAN (with the install.packages() function) or via Bioconductor.



* car

* rgl

* rpgraph

* igraph

* fields



with cran:

`install.packages(c("car", "rgl", "igraph", "fields"))`



The Destiny package for creating diffusion maps was one of the dinmensionality reduction techniques we used in order to reconstruct lineage tree topologies in a low dimensional manifold.



The destiny package as well as how to install it and use it can be found [here](https://www.helmholtz-muenchen.de/icb/research/groups/quantitative-single-cell-dynamics/software/destiny/index.html)



Optional packages:

* energy (needed for finding differentially expressed genes)

* VGAM



Rpgraph can be installed following the instructions from the [developer's site](https://github.com/Albluca/rpgraph/wiki).



The steps can be summarized in:

`install.packages(pkgs = "rJava", repos="http://rforge.net", type = 'source')`



For rJava **you have to have Java installed** in your system. You can install **default-jre, open jdk**



`install.packages("devtools")`

`library(devtools)`

`install.packages(c("bigpca", "irlba", "nsprcomp", "plotly","fields", "igraph", "rgl", "tictoc"))`



You might be required to installed the following system libraries: libudunits2-dev, mesa-common-dev, libglu1-mesa-dev, and zlib1g-dev.



## 3) Download The Rpackage files 

Download an archive from github (for example the [zip](https://github.com/soedinglab/merlot/archive/master.zip) file) and unpack it, or pull the repository directly.



## 4) Install MERLoT

Install from source:



`install.packages("/path/to/merlot/directory/",  types="source", repos = NULL)`



Install from github:



`library(devtools)`



`install_github("soedinglab/merlot")`