Commit 3a32c960 authored by Christian Arnold's avatar Christian Arnold
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Small bugfixes

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......@@ -22,6 +22,6 @@ Contributions
---------------
- Originally developed by Armando Reyes-Palomares and subsequently modified by Giovanni Palla
- Christian Arnold then made an R package out of it with additional improvements and extensions with the help of various people from the Zaugg Lab
- Christian Arnold then made an R package out of it with additional improvements and extensions with the help of various people from the Zaugg Lab (Rim Moussa in particular)
- supervised by Judith Zaugg
- many people from the Zaugg Lab helped to develop the package
Package: GRaNIEdev
Title: GRaNIE: Reconstruction cell type specific gene regulatory networks using chromatin accessibility and RNA-seq data
Version: 0.13.9
Title: GRaNIE: Reconstruction cell type specific gene regulatory networks including enhancers using chromatin accessibility and RNA-seq data
Version: 0.13.10
Encoding: UTF-8
Authors@R: c(person("Christian", "Arnold", email =
"christian.arnold@embl.de", role = c("cre","aut")),
person("Judith", "Zaugg", email =
"judith.zaugg@embl.de", role = "aut"))
person("Rim", "Moussa", email =
"rim.moussa01@gmail.com", role = "aut"))
Description: 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 celltype 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 a gene regulatory network (GRN) with cell-type and data-specific TF-RE and RE-gene links. Here, we reconstruct such a GRN 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.
Imports:
futile.logger,
......
<!--
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-->
# GRaNIE Changelog and News
## GRNdev 0.9 - 0.11 (2021-06-24)
## GRaNIEdev 0.9 - 0.11 (2021-06-24)
### Major changes
......@@ -19,12 +19,12 @@
- changed the object structure slightly and moved some gene and peak annotation data (such as mean, CV) to the appropriate annotation slot
## GRNdev 0.8 (2021-05-07)
## GRaNIEdev 0.8 (2021-05-07)
### Major changes
- improved PCA plotting, PCA plots are now produced for both raw and normalized data
- new filters for the function *filterGRNAndConnectGenes* (*peak_gene.maxDistance*) as well as more flexibility how to adjust the peak-gene raw p-values for multiple testing (including the possibility to use IHW - experimental)
- new filters for the function *filterGRaNIEAndConnectGenes* (*peak_gene.maxDistance*) as well as more flexibility how to adjust the peak-gene raw p-values for multiple testing (including the possibility to use IHW - experimental)
- new function *plotDiagnosticPlots_TFPeaks* for plotting (this function was previously called only internally, but is now properly exported), in analogy to *plotDiagnosticPlots_peakGene*
### Bug fixes
......@@ -37,7 +37,7 @@
- some functions have been added / renamed to make the workflow more clear and streamlined, see Vignette for details
- some default parameters changed
## GRNdev 0.7 (2021-03-12)
## GRaNIEdev 0.7 (2021-03-12)
### Major changes
......@@ -55,7 +55,7 @@
<!--
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-->
## GRNdev 0.6 (2021-02-09)
## GRaNIEdev 0.6 (2021-02-09)
### Major changes
......@@ -71,6 +71,6 @@
<!--
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-->
## GRNdev 0.5 (2021-02-02)
## GRaNIEdev 0.5 (2021-02-02)
first published package version
#' GRN: Reconstruction cell type specific gene regulatory networks using chromatin accessibility and RNA-seq data (general package information)
#' *GRaNIE* (**G**ene **R**egul**a**tory **N**etwork **I**nference including **E**nhancers): Reconstruction and evaluation of data-driven, cell type specific gene regulatory networks including enhancers using chromatin accessibility and RNAseq data (general package information)
#'
#' 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 celltype 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 a gene regulatory network (GRN) with cell-type and data-specific TF-RE and RE-gene links. Here, we reconstruct such a GRN 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.
#' 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 celltype 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 a 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.
#'
#' @section Package functions:
#' See the Vignettes for a workflow example and more generally \url{https://grp-zaugg.embl-community.io/grn/articles/} for all project-related information.
#' See the Vignettes for a workflow example and more generally \url{https://grp-zaugg.embl-community.io/GRaNIE/articles/} for all project-related information.
#'
#' @section GRN object:
#' The GRN* packages work with GRN objects. See \code{\linkS4class{GRN}} for details.
#' The *GRaNIE* package works with *GRN* objects. See \code{\linkS4class{GRN}} for details.
#'
#' @section Contact Information:
#' Please check out \url{https://grp-zaugg.embl-community.io/grn} for how to get in contact with us.
#' Please check out \url{https://grp-zaugg.embl-community.io/GRaNIE} for how to get in contact with us.
#'
#' @docType package
#' @keywords GRN, GRN-package
#' @name GRN
#' @keywords GRaNIE, GRaNIE-package
#' @name GRaNIE
NULL
......@@ -2841,6 +2841,11 @@ filterGRNAndConnectGenes <- function(GRN,
futile.logger::flog.info(paste0("\n\n", .getPermStr(permutationCur)))
permIndex = as.character(permutationCur)
if (is.null(GRN@connections$peak_genes[[permIndex]])) {
message = "No peak-gene connections found. Run the function addConnections_peak_gene first"
.checkAndLogWarningsAndErrors(NULL, message, isWarning = FALSE)
}
peakGeneCorrelations = GRN@connections$peak_genes[[permIndex]] %>%
dplyr::mutate(gene.ENSEMBL = as.character(.data$gene.ENSEMBL)) %>%
......
### Reconstruction and evaluation of a cell type specific gene regulatory networks using chromatin accessibility and RNAseq data
### 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
This project is currently under active development. If you have questions, please do not hesitate to contact us (see below).
*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).
### Summary
*Towards a data-driven cell-type specific regulatory network*
*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 a gene regulatory network (GRN) with cell-type and
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 GRN using bulk RNAseq and open chromatin (e.g., using ATACseq or ChIPseq for open
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 GRN exists, we propose a novel
evaluation algorithm which is not using a ground-truth network and instead assesses a GRN based on its
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 GRN links predict differential expression values based on differential TF activity. Overall, our
GRNs consistently perform significantly better than corresponding randomized versions, showing that they capture
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
GRN reconstruction algorithms.
Finally, we run our GRN construction and evaluation pipeline on diverse datasets such as naive CD4-positive T cells
*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
......@@ -35,7 +35,7 @@ understanding of the underlying regulatory programs
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, doubts, ideas or problems, please use the [Gitlab Issue Tracker](https://git.embl.de/grp-zaugg/grn/issues). We will respond in a timely manner.
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
......
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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/GRN.R
\docType{package}
\name{GRN}
\alias{GRN}
\title{GRN: Reconstruction cell type specific gene regulatory networks using chromatin accessibility and RNA-seq data (general package information)}
\name{GRaNIE}
\alias{GRaNIE}
\title{*GRaNIE* (**G**ene **R**egul**a**tory **N**etwork **I**nference including **E**nhancers): Reconstruction and evaluation of data-driven, cell type specific gene regulatory networks including enhancers using chromatin accessibility and RNAseq data (general package information)}
\description{
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 celltype 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 a gene regulatory network (GRN) with cell-type and data-specific TF-RE and RE-gene links. Here, we reconstruct such a GRN 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.
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 celltype 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 a 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.
}
\section{Package functions}{
See the Vignettes for a workflow example and more generally \url{https://grp-zaugg.embl-community.io/grn/articles/} for all project-related information.
See the Vignettes for a workflow example and more generally \url{https://grp-zaugg.embl-community.io/GRaNIE/articles/} for all project-related information.
}
\section{GRN object}{
The GRN* packages work with GRN objects. See \code{\linkS4class{GRN}} for details.
The *GRaNIE* package works with *GRN* objects. See \code{\linkS4class{GRN}} for details.
}
\section{Contact Information}{
Please check out \url{https://grp-zaugg.embl-community.io/grn} for how to get in contact with us.
Please check out \url{https://grp-zaugg.embl-community.io/GRaNIE} for how to get in contact with us.
}
\keyword{GRN,}
\keyword{GRN-package}
\keyword{GRaNIE,}
\keyword{GRaNIE-package}
......@@ -2,7 +2,7 @@
title: "Introduction and Methodological Details"
author: "Christian Arnold, Judith Zaugg"
date: "`r doc_date()`"
package: "`r BiocStyle::pkg_ver('GRaNIE')`"
package: "`r BiocStyle::pkg_ver('GRaNIEdev')`"
abstract: >
This vignette introduces the `GRaNIE` package and explains the main features, methods and necessary background.
......@@ -25,7 +25,7 @@ output:
<!-- </div> -->
<!-- <br/><br/> -->
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 celltype 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 a gene regulatory network (GRN) with cell-type and data-specific TF-RE and RE-gene links. Here, we reconstruct such a GRN 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.
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 celltype 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 a gene regulatory network (GRN) with cell-type and data-specific TF-RE and RE-gene links. Here, we reconstruct such a GRN 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.
In summary, we present a framework to reconstruct predictive enhancer-mediated regulatory network models that are based on integrating of expression and chromatin accessibility/activity pattern across individuals, and provide a comprehensive resource of cell-type specific gene regulatory networks for particular cell types.
......
......@@ -2,7 +2,7 @@
title: "Get Started with the *GRaNIE* packages from the Zaugg Lab"
author: "Christian Arnold, Judith Zaugg"
date: "`r doc_date()`"
package: "`r BiocStyle::pkg_ver('GRaNIE')`"
package: "`r BiocStyle::pkg_ver('GRaNIEdev')`"
vignette: >
%\VignetteIndexEntry{Get Started}
%\VignetteEngine{knitr::rmarkdown}
......
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