Cascade

Cascade R package

https://github.com/fbertran/cascade

Repository

Cascade R package

README.Rmd

```{r setup, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%",
  dpi=300,fig.width=7,
  fig.keep="all"
)
```

# Cascade 

# Cascade, Selection, Reverse-Engineering and Prediction in Cascade Networks
## Frédéric Bertrand and Myriam Maumy-Bertrand


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Cascade is a modeling tool allowing gene selection, reverse engineering, and prediction in cascade networks. Jung, N., Bertrand, F., Bahram, S., Vallat, L., and Maumy-Bertrand, M. (2014) .


The package was presented at the [User2014!](https://user2014.r-project.org/) conference. Jung, N., Bertrand, F., Bahram, S., Vallat, L., and Maumy-Bertrand, M. (2014). "Cascade: a R-package to study, predict and simulate the diffusion of a signal through a temporal genenetwork", *book of abstracts*, User2014!, Los Angeles, page 153, .


![Reverse-engineered network.](man/figures/figure_article_def.png)

![Simulation of an intervention on a gene of the network.](man/figures/figure_network_pred2.png)

This website and these examples were created by F. Bertrand and M. Maumy-Bertrand.

## Installation

You can install the released version of Cascade from [CRAN](https://CRAN.R-project.org) with:

```{r, eval = FALSE}
install.packages("Cascade")
```

You can install the development version of Cascade from [github](https://github.com) with:

```{r, eval = FALSE}
devtools::install_github("fbertran/Cascade")
```

## Examples

### Data management
Import Cascade Data (repeated measurements on several subjects) from the CascadeData package and turn them into a micro array object. The second line makes sure the CascadeData package is installed.
```{r microarrayclass}
library(Cascade)
if(!require(CascadeData)){install.packages("CascadeData")}
data(micro_US)
micro_US<-as.micro_array(micro_US,time=c(60,90,210,390),subject=6)
```

Get a summay and plots of the data:
```{r plotmicroarrayclass}
summary(micro_US)
```

### Gene selection
There are several functions to carry out gene selection before the inference. They are detailed in the two vignettes of the package. 

### Data simulation
Let's simulate some cascade data and then do some reverse engineering.

We first design the F matrix
```{r createF}
T<-4
F<-array(0,c(T-1,T-1,T*(T-1)/2))

for(i in 1:(T*(T-1)/2)){diag(F[,,i])<-1}
F[,,2]<-F[,,2]*0.2
F[2,1,2]<-1
F[3,2,2]<-1
F[,,4]<-F[,,2]*0.3
F[3,1,4]<-1
F[,,5]<-F[,,2]
```

We set the seed to make the results reproducible and draw a scale free random network.
```{r randomN}
set.seed(1)
Net<-Cascade::network_random(
  nb=100,
  time_label=rep(1:4,each=25),
  exp=1,
  init=1,
  regul=round(rexp(100,1))+1,
  min_expr=0.1,
  max_expr=2,
  casc.level=0.4
)
Net@F<-F
```

We simulate gene expression according to the network that was previously drawn
```{r genesimul}
M <- Cascade::gene_expr_simulation(
  network=Net,
  time_label=rep(1:4,each=25),
  subject=5,
  level_peak=200)
```

Get a summay and plots of the simulated data:
```{r summarysimuldata}
summary(M)
```

```{r plotsimuldata}
plot(M)
```

### Network inference
We infer the new network using subjectwise leave one out cross-validation (all measurement from the same subject are removed from the dataset)
```{r netinf}
Net_inf_C <- Cascade::inference(M, cv.subjects=TRUE)
```

Heatmap of the coefficients of the Omega matrix of the network
```{r heatresults}
stats::heatmap(Net_inf_C@network, Rowv=NA, Colv=NA, scale="none", revC=TRUE)
```

###Post inferrence network analysis
We switch to data that were derived from the inferrence of a real biological network and try to detect the optimal cutoff value: the best cutoff value for a network to fit a scale free network.

```{r cutoff, cache=TRUE}
data("network")
set.seed(1)
cutoff(network)
```

Analyze the network with a cutoff set to the previouly found 0.14 optimal value.

```{r analyzenet, warning=FALSE}
analyze_network(network,nv=0.14)
```

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