Updated description, readme and copyright holders

fbertran · Mar 1, 2019 · d537b74 · d537b74
1 parent d9ca8ba
commit d537b74
Show file tree

Hide file tree

Showing 18 changed files with 89 additions and 25 deletions.
diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,7 +1,7 @@
 Package: networkABC
 Type: Package
 Title: Network Reverse Engineering with Approximate Bayesian Computation
-Version: 0.5-2
+Version: 0.5-3
 Date: 2019-02-25
 Depends: R (>= 3.0.0)
 Imports: RColorBrewer, network, sna
@@ -10,10 +10,12 @@ Authors@R: c(
 	person(given = "Frederic", family= "Bertrand", role = c("cre", "aut"), email = "frederic.bertrand@math.unistra.fr", comment = c(ORCID = "0000-0002-0837-8281")),
 	person(given = "Myriam", family= "Maumy-Bertrand", role = c("aut"), email = "myriam.maumy-bertrand@math.unistra.fr", comment = c(ORCID = "0000-0002-4615-1512")),
 	person(given = "Khadija", family= "Musayeva", role = c("ctb"), email = "khadija@musayeva.net"),
-	person(given = "Nicolas", family= "Jung", role = c("ctb"), email = "nicolas.jung@unistra.fr"))
-Author: Frederic Bertrand [cre, aut] (<https://orcid.org/0000-0002-0837-8281>), Myriam Maumy-Bertrand [aut] (<https://orcid.org/0000-0002-4615-1512>), Khadija Musayeva [ctb], Nicolas Jung [ctb]
+	person(given = "Nicolas", family= "Jung", role = c("ctb"), email = "nicolas.jung@unistra.fr"),
+	person(family = "Université de Strasbourg", role = c("cph")),
+	person(family = "CNRS", role = c("cph")))
+Author: Frederic Bertrand [cre, aut] (<https://orcid.org/0000-0002-0837-8281>), Myriam Maumy-Bertrand [aut] (<https://orcid.org/0000-0002-4615-1512>), Khadija Musayeva [ctb], Nicolas Jung [ctb], Université de Strasbourg [cph], CNRS [cph]
 Maintainer: Frederic Bertrand <frederic.bertrand@math.unistra.fr>
-Description: An inference tool based on approximate Bayesian computation to decipher network data and assess the strength of their inferred links.
+Description: We developed an inference tool based on approximate Bayesian computation to decipher network data and assess the strength of the inferred links between network's actors. It is a new multi-level approximate Bayesian computation (ABC) approach. At the first level, the method captures the global properties of the network, such as scale-freeness and clustering coefficients, whereas the second level is targeted to capture local properties, including the probability of each couple of genes being linked. Up to now, Approximate Bayesian Computation (ABC) algorithms have been scarcely used in that setting and, due to the computational overhead, their application was limited to a small number of genes. On the contrary, our algorithm was made to cope with that issue and has low computational cost. It can be used, for instance, for elucidating gene regulatory network, which is an important step towards understanding the normal cell physiology and complex pathological phenotype. Reverse-engineering consists in using gene expressions over time or over different experimental conditions to discover the structure of the gene network in a targeted cellular process. The fact that gene expression data are usually noisy, highly correlated, and have high dimensionality explains the need for specific statistical methods to reverse engineer the underlying network. 
 NeedsCompilation: Yes
 License: GPL-3
 Encoding: UTF-8

diff --git a/NEWS.md b/NEWS.md
@@ -1,3 +1,8 @@
+# networkABC 0.5-3
+
+* Detailed description of the package
+* Added copyright holders
+
 # networkABC 0.5-2
 
 * Added citation for package

diff --git a/README.Rmd b/README.Rmd
@@ -20,7 +20,23 @@ knitr::opts_chunk$set(
 ```
 # networkABC
 
-The goal of networkABC is to provide an inference tool based on approximate Bayesian computation to decipher network data and assess the strength of their inferred links.
+The goal of `networkABC` is to provide an inference tool based on approximate Bayesian computation to decipher network data and assess the strength of their inferred links.
+
+
+We developed an inference tool based on approximate Bayesian computation to decipher network data and assess the strength of the inferred links between network's actors. 
+
+
+It is a new multi-level approximate Bayesian computation (ABC) approach. 
+
+* At the first level, the method captures the global properties of the network, such as scale-freeness and clustering coefficients, 
+* whereas the second level is targeted to capture local properties, including the probability of each couple of genes being linked. 
+
+
+Up to now, Approximate Bayesian Computation (ABC) algorithms have been scarcely used in that setting and, due to the computational overhead, their application was limited to a small number of genes. On the contrary, our algorithm was made to cope with that issue and has low computational cost. 
+
+
+It can be used, for instance, for elucidating gene regulatory network, which is an important step towards understanding the normal cell physiology and complex pathological phenotype. Reverse-engineering consists in using gene expressions over time or over different experimental conditions to discover the structure of the gene network in a targeted cellular process. The fact that gene expression data are usually noisy, highly correlated, and have high dimensionality explains the need for specific statistical methods to reverse engineer the underlying network. 
+
 
 This website and these examples were created by F. Bertrand and M. Maumy-Bertrand.
 

diff --git a/README.md b/README.md
@@ -11,7 +11,23 @@ output: github_document
 
 # networkABC
 
-The goal of networkABC is to provide an inference tool based on approximate Bayesian computation to decipher network data and assess the strength of their inferred links.
+The goal of `networkABC` is to provide an inference tool based on approximate Bayesian computation to decipher network data and assess the strength of their inferred links.
+
+
+We developed an inference tool based on approximate Bayesian computation to decipher network data and assess the strength of the inferred links between network's actors. 
+
+
+It is a new multi-level approximate Bayesian computation (ABC) approach. 
+
+* At the first level, the method captures the global properties of the network, such as scale-freeness and clustering coefficients, 
+* whereas the second level is targeted to capture local properties, including the probability of each couple of genes being linked. 
+
+
+Up to now, Approximate Bayesian Computation (ABC) algorithms have been scarcely used in that setting and, due to the computational overhead, their application was limited to a small number of genes. On the contrary, our algorithm was made to cope with that issue and has low computational cost. 
+
+
+It can be used, for instance, for elucidating gene regulatory network, which is an important step towards understanding the normal cell physiology and complex pathological phenotype. Reverse-engineering consists in using gene expressions over time or over different experimental conditions to discover the structure of the gene network in a targeted cellular process. The fact that gene expression data are usually noisy, highly correlated, and have high dimensionality explains the need for specific statistical methods to reverse engineer the underlying network. 
+
 
 This website and these examples were created by F. Bertrand and M. Maumy-Bertrand.
 

diff --git a/docs/articles/index.html b/docs/articles/index.html
diff --git a/docs/articles/vignette.html b/docs/articles/vignette.html
diff --git a/docs/authors.html b/docs/authors.html
diff --git a/docs/index.html b/docs/index.html
diff --git a/docs/news/index.html b/docs/news/index.html
diff --git a/docs/reference/abc.html b/docs/reference/abc.html
diff --git a/docs/reference/index.html b/docs/reference/index.html
diff --git a/docs/reference/netsimul.html b/docs/reference/netsimul.html
diff --git a/docs/reference/networkABC.html b/docs/reference/networkABC.html
diff --git a/docs/reference/network_gen.html b/docs/reference/network_gen.html
diff --git a/docs/reference/resabc.html b/docs/reference/resabc.html
diff --git a/docs/reference/showHp.html b/docs/reference/showHp.html