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https://github.com/hmatsu1226/SCODE

SCODE : an efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation
https://github.com/hmatsu1226/SCODE

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SCODE : an efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation

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# SCODE



SCODE : an efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation.



## Reference



https://academic.oup.com/bioinformatics/article/33/15/2314/3100331



## Requirements



SCODE is written with R, and use MASS library to calculate pseudo inverse matrix.



## Download



```

git clone https://github.com/hmatsu1226/SCODE

cd SCODE

```

Or download from "Download ZIP" button and unzip it.



## Running SCODE

Optimize linear ODE and infer regulatory network from time course data.



##### Usage

```

Rscript SCODE.R       

```



* Input_file1 : G x C matrix of expression data

* Input_file2 : Time point data (e.g. pseudo-time data)

* Output_dir : Result files are outputted in this directory

* G : The number of transcription factors

* D : The number of z

* C : The number of cells

* I : The number of iterations of optimization



##### Example of running SCODE

```

Rscript SCODE.R data/exp_train.txt data/time_train.txt out 100 4 356 100

```



##### Format of Input_file1

The Input_file1 is the G x C matrix of expression data (separated with 'TAB').

Each row corresponds to each gene, and each column corresponds to each cell.



##### Example of Input_file1

```

1.24	1.21	1.28	...

0.0 	0.19	0.0	...

.

.

.

```



##### Format of Input_file2

The Input_file2 contains the time point data (pseudo-time) of each cell.



* Col1 : Information of a cell (e.g. index of a cell, experimental time point)

* Col2 : Time parameter (e.g. pseudo-time) (normalized from 0.0 to 1.0)



##### Example of Input_file2

```

0	0.065

0	0.037

0	0.007

.

.

.

72	0.873

72	0.964

```



## Output files of SCODE

SCODE outputs some files as below, and the files are named to correspond with the names of the variables in the paper.



#### A.txt

G x G matrix, which corresponds to infered regulatory network.

Aij represents the regulatory relationship from TF j to TF i.



#### B.txt

D x D diagonal matrix, which corresponds to the optimized parameters of ODE of z.



#### W.txt

G x D matrix, which corresponds to W of linear regression.



#### RSS.txt

The residual sum of squares of linear regression.



## Running SCODE several times

We recommend runnning SCODE several times and averaging the result (A) to obtain reliable relationships.

```

ruby run_R.rb        

```



* R : The number of traials

* Output_dir : Result files of each trial is outputted in the directory



The averaged A (meanA.txt) is outputted in the Output_dir.










# SCODE implemented by Julia

## Requirements

SCODE.jl is written with Julia(Version 0.5.0), and use DataFrames package.

The runtimes of SCODE.jl is smaller than that of SCODE.R



## Running SCODE.jl

```

julia SCODE.jl       

```



## Running SCODE.jl several times

```

ruby run_julia.rb        

```









# Downstream analysis



## Calculation of RSS (RSS.R)

To choose appropriate size of z, we recommend to calculate RSS of independent test data.



#### Usage

```

Rscript RSS.R       

```

* Input_file1 : G x C matrix of expression data

* Input_file2 : Time point data (e.g. pseudo-time data)

* Input_dir : The directory that W.txt and B.txt are saved (Output_dir of SCODE)

* Output_file : RSS for this data

* G : The number of transcription factors

* D : The number of z

* C : The number of cells



#### Example of running RSS.R

```

Rscript RSS.R data/exp_test.txt data/time_test.txt out out/RSS_test.txt 100 4 100

```



## Reconstruction of expression dynamics (Reconstruct_dynamics.R)

Calculate the dynamics from optimized linear ODE.



#### Usage

```

Rscript Reconstruct_dynamics.R    

```

* Input_file1 : Initial value of x

* Input_file2 : A.txt

* Output_file : (G+1) x 101 matrix of reconstructed expression data

* G : The number of transcription factors



#### Example of running Reconstruct_dynamics.R

```

Rscript Reconstruct_dynamics.R data/init.txt out/A.txt out/dynamics.txt 100

```



##### Format of Input_file1

The Input_file1 is the initial values of x (separated with 'TAB').

Each row corresponds to each gene.

* Col1 : Index of a gene

* Col2 : Initial value



##### Example of Input_file1

```

0	1.253

1	1.266

2	1.548

.

.

.

```



##### Format of Output_file

The Output_file is the (G+1) x 101 matrix of reconstructed expression dynamics (separated with 'TAB').

The first column corresponds to time parameter (from 0.0 to 1.0 with 0.01 interval).

Each row corresponds to each gene, and each column corresponds to each time point.



##### Example of Output_file

```

0	0.01	0.02	...

1.253	1.241	1.233	...

1.266 	1.053	0.937	...

.

.

```



# Dataset

We validated SCODE with three time couse scRNA-Seq data.

We extracted top 100 variable TFs.



## data

scRNA-Seq data derived from PrE cells differentiated from mES cells (in preparation).



## data2

scRNA-Seq data obtained to examine direct reprogramming from MEF cells to myocytes.

Treutlein, Barbara, et al. "Dissecting direct reprogramming from fibroblast to neuron using single-cell RNA-seq." Nature (2016).



## data3

scRNA-Seq data derived from DE cells differentiated from hES cells.

Chu, Li-Fang, et al. "Single-cell RNA-seq reveals novel regulators of human embryonic stem cell differentiation to definitive endoderm." Genome biology 17.1 (2016): 173.



## Rererence TF-TF network

Reference TF-TF networks are extracted from http://www.regulatorynetworks.org .

The first column corresponds to target TF.

The second column corresponds to regulator TF.



## License

Copyright (c) 2016 Hirotaka Matsumoto

Released under the MIT license