From 1b34e29c63dbf29b871fe1ba42e378460d0882d7 Mon Sep 17 00:00:00 2001
From: Aryan Kamal <aryan.kamal@embl.de>
Date: Mon, 13 Dec 2021 10:53:22 +0100
Subject: [PATCH] re-write README.md

---
 .Rproj.user/shared/notebooks/paths |  2 --
 README.md                          | 34 ++++--------------------------
 vignettes/quickStart.Rmd           | 31 ++++++---------------------
 3 files changed, 10 insertions(+), 57 deletions(-)

diff --git a/.Rproj.user/shared/notebooks/paths b/.Rproj.user/shared/notebooks/paths
index edb4ca0..f8ad49b 100644
--- a/.Rproj.user/shared/notebooks/paths
+++ b/.Rproj.user/shared/notebooks/paths
@@ -1,8 +1,6 @@
 /Users/aryan/Desktop/EMBL/GRaNPA/GRaNPA/NAMESPACE="3457577B"
 /Users/aryan/Desktop/EMBL/GRaNPA/GRaNPA/NEWS.md="AF7ECC67"
 /Users/aryan/Desktop/EMBL/GRaNPA/GRaNPA/News.md="DDECFD77"
-/Users/aryan/Desktop/EMBL/GRaNPA/GRaNPA/R/OP_boxplot.R="43F211D3"
-/Users/aryan/Desktop/EMBL/GRaNPA/GRaNPA/R/OP_density.R="1151DA06"
 /Users/aryan/Desktop/EMBL/GRaNPA/GRaNPA/R/plot_GRaNPA_TF_importance.R="7F119776"
 /Users/aryan/Desktop/EMBL/GRaNPA/GRaNPA/R/plot_GRaNPA_boxplot.R="DB0BB038"
 /Users/aryan/Desktop/EMBL/GRaNPA/GRaNPA/R/plot_GRaNPA_density.R="BB01A8D9"
diff --git a/README.md b/README.md
index c3db310..6646c95 100644
--- a/README.md
+++ b/README.md
@@ -1,43 +1,17 @@
-### Gene Regulatory Network Inference including Enhancers: Reconstruction and evaluation of data-driven, cell type specific gene regulatory networks including enhancers using chromatin accessibility and RNAseq data
+### GRaNIE and GRaNPA: Reconstruction and unbiased evaluation of cell-type specific enhancer-mediated gene regulatory networks
 
-*GRaNIE* (**G**ene **R**egul**a**tory **N**etwork **I**nference including **E**nhancers) is currently under active development. If you have questions, please do not hesitate to contact us (see below).
+*GRaNPA* (**G**ene **R**egul**a**tory **N**etwork **P**erformace **A**nalysis) is currently under active development. If you have questions, please do not hesitate to contact us (see below).
 
 ### Summary
 
 *Towards a data-driven cell-type specific regulatory network including enhancers*
 
 
-Genetic variants associated with diseases often affect non-coding regions, thus likely having a regulatory role. To
-understand the effects of genetic variants in these regulatory regions, identifying genes that are modulated by specific
-regulatory elements (REs) is crucial. The effect of gene regulatory elements, such as enhancers, is often cell-type
-specific, likely because the combinations of transcription factors (TFs) that are regulating a given enhancer have cell-type-specific activity. This TF activity can be quantified with existing tools such as *diffTF* and captures differences in
-binding of a TF in open chromatin regions. Collectively, this forms an enhancer-mediated gene regulatory network (*eGRN*) with cell-type and
-data-specific TF-RE and RE-gene links.
-Here, we reconstruct such a *eGRN* using bulk RNAseq and open chromatin (e.g., using ATACseq or ChIPseq for open
-chromatin marks) and optionally TF activity data. Our network contains different types of links, connecting TFs to
-regulatory elements, the latter of which is connected to genes in the vicinity or within the same chromatin domain
-(TAD). We use a statistical framework to assign empirical FDRs and weights to all links using a permutation-based
-approach.
-Since no widely accepted ground-truth dataset for assessing the constructed *eGRN* exists, we propose a novel
-evaluation algorithm which is not using a ground-truth network and instead assesses a *eGRN* based on its
-performance in predicting differential expression response. For this, we used a random forest regression model and
-evaluate how well the *eGRN* links predict differential expression values based on differential TF activity. Overall, our
-*eGRNs* consistently perform significantly better than corresponding randomized versions, showing that they capture
-reliable links between TFs and their target genes. Our framework also allows us to benchmark and compare different
-*eGRN* reconstruction algorithms.
-Finally, we run our *eGRN* construction and evaluation pipeline on diverse datasets such as naive CD4-positive T cells
-or an AML cohort and identified a set of cell-type specific TFs with crucial roles to predict differential gene expression
-based on differential TF activity. The resulting core subnetwork has higher predictive power and enables a deeper
-understanding of the underlying regulatory programs
+Among the biggest challenges in the post-GWAS (genome-wide association studies) era is the interpretation of disease-associated genetic variants in non-coding genomic regions. Enhancers have emerged as key players in mediating the effect of genetic variants on complex traits and diseases. Their activity is regulated by a combination of transcription factors (TFs), epigenetic changes and genetic variants. Several approaches exist to link enhancers to their target genes, and others that infer TF-gene connections. However, we currently lack a framework that systematically integrates enhancers into TF-gene regulatory networks. Furthermore, we lack an unbiased way of assessing whether inferred regulatory interactions are biologically meaningful. Here we present two methods, implemented as user-friendly R-packages, for building and evaluating enhancer-mediated gene regulatory networks (eGRNs) called GRaNIE (Gene Regulatory Network Inference including Enhancers - https://git.embl.de/grp-zaugg/GRaNIE) and GRaNPA (Gene Regulatory Network Performance Analysis - https://git.embl.de/grp-zaugg/GRaNPA), respectively. GRaNIE jointly infers TF-enhancer, enhancer-gene and TF-gene interactions by integrating open chromatin data such as ATAC-Seq or H3K27ac with RNA-seq across a set of samples (e.g. individuals), and optionally also Hi-C data. GRaNPA is a general framework for evaluating the biological relevance of TF-gene GRNs by assessing their performance for predicting cell-type specific differential expression. We demonstrate the power of our tool-suite by investigating gene regulatory mechanisms in macrophages that underlie their response to infection, and their involvement in common genetic diseases including autoimmune diseases
 
 
 ### Get help and contact us
 
-If you have questions or comments, feel free to contact us. We will be happy to answer any questions related to this project as well as questions related to the software implementation. For method-related questions, contact Judith B. Zaugg (judith.zaugg@embl.de). For technical questions, contact Christian Arnold (christian.arnold@embl.de).
+If you have questions or comments, feel free to contact us. We will be happy to answer any questions related to this project as well as questions related to the software implementation. For method-related questions, contact Judith B. Zaugg (judith.zaugg@embl.de). For technical questions, contact Aryan Kamal (aryan.kamal@embl.de).
 
-If you have questions, doubts, ideas or problems, please use the [Gitlab Issue Tracker](https://git.embl.de/grp-zaugg/GRaNIE/issues). We will respond in a timely manner.
 
-### Contributions
-
-- Originally developed by Armando Reyes-Palomares and subsequently modified by Giovanni Palla
-- Christian Arnold then took over and added many improvements and extensions and converted it to a proper R package with the help of various people from the Zaugg Lab
diff --git a/vignettes/quickStart.Rmd b/vignettes/quickStart.Rmd
index 9b440e9..bf5b67b 100644
--- a/vignettes/quickStart.Rmd
+++ b/vignettes/quickStart.Rmd
@@ -1,6 +1,6 @@
 ---
-title: "Get Started with the *GRaNIE* packages from the Zaugg Lab"
-author: "Christian Arnold, Judith Zaugg"
+title: "Get Started with the *GRaNPA* packages from the Zaugg Lab"
+author: "Aryan Kamal, Judith Zaugg"
 date: "`r doc_date()`"
 package: "`r BiocStyle::pkg_ver('GRaNPA')`"
 vignette: >
@@ -11,12 +11,12 @@ output:
   BiocStyle::html_document
 ---
 
-**This vignette gives you a 1 minute summary of how to install and use our *GRaNIE* packages. For more details, check out the other Vignettes.** 
+**This vignette gives you a 1 minute summary of how to install and use our *GRaNPA* packages. For more details, check out the other Vignettes.** 
 
 ## Install the packages
 
-### Stable version: *GRaNIE*
-Installing our *GRaNIE* packages is easy. Just execute the following line into *R*:
+### Stable version: *GRaNPA*
+Installing our *GRaNPA* packages is easy. Just execute the following line into *R*:
 
 ```{r, eval = FALSE}
 library(devtools)
@@ -29,29 +29,10 @@ If you receive an error message, make sure you installed the devtools package. I
 install.packages("devtools")
 ```
 
-### Development version: *GRaNIEdev*
-
-To install the development version of our *GRN* package, simply do:
-```{r, eval = FALSE}
-devtools::install_gitlab("grp-zaugg/GRaNPA", host = "git.embl.de", force = TRUE)
-```
-
-
-### Data package: *GRaNIEData*
-
-Similarly, to install the *GRaNIEData* package, simply do:
+To install the GRN evaluation pipeline, *GRaNPA* package, simply do:
 ```{r, eval = FALSE}
 devtools::install_gitlab("grp-zaugg/GRaNPA", host = "git.embl.de", force = TRUE)
 ```
 
 
-## What do the different packages do?
-
-The stable version ***GRaNIE*** package is the best choice for most users, it contains features that have been tested more thoroughly and that we provide support for.
-
-The development version ***GRaNIEdev*** contains new features that may or may not be tested so far and we can only provide limited support for it. This is the version we actively work on, and every once in a while after finishing a new feature, we integrate it into the stable version in the *GRaNIE* package.
-
-The ***GRaNIEdata*** package provides example data that can be used to test the package and run the Vignettes.
-
-
 ## Bug Reports, Feature Requests and Contact Information
-- 
GitLab