@@ -1,12 +1,14 @@

 Package: PanomiR

 Title: Inferring miRNAs targeting of pathway groups

-Version: 0.99.6

+Version: 0.99.7

 Authors@R: c(

     person("Pourya", "Naderi", email = "pouryany@gmail.com",

            role = c("aut", "cre")),

-    person("Winston", "Hide", email = "whide@bidmc.harvard.edu",

+    person("Yue Yang (Alan)", "Teo", email = "yueyang.teo@epfl.ch",

            role = c("aut")),

     person("Ilya", "Sytchev", email = "isytchev@hsph.harvard.edu",

+           role = c("aut")),

+    person("Winston", "Hide", email = "whide@bidmc.harvard.edu",

            role = c("aut")))

 Description: PanomiR is a package to detect miRNAs that target groups of

     pathways from gene expression data. This package provides functionality 

@@ -19,6 +21,7 @@ Encoding: UTF-8

 RoxygenNote: 7.1.2

 Suggests:

     testthat (>= 3.0.0),

+    BiocStyle,

     knitr,

     rmarkdown

 Config/testthat/edition: 3

@@ -1,8 +1,9 @@

 ---

-title: "PanomiR Package Walkthrough"

+title: "PanomiR Package Vignette"

+author: "Pourya Naderi"

+package: PanomiR

 output: 

-  rmarkdown::html_vignette:

-    toc: true

+  BiocStyle::html_document

 bibliography: references.bib  

 vignette: >

   %\VignetteIndexEntry{PanomiR}

@@ -17,13 +18,13 @@ knitr::opts_chunk$set(

 )

 ```

-## Introduction

+# Introduction

 PanomiR is a package for pathway and microRNA Analysis of gene expression data.

 This document provides details about how to install and utilize various 

 functionality in PanomiR.

-## Installation

+# Installation

 PanomiR can be accessed via Bioconductor. To install, start R (version >= 4.1.0)

 and run the following code.

@@ -41,7 +42,7 @@ You can also install the latest development version of PanomiR using GitHub.

 devtools::install_github("pouryany/PanomiR")

 ```

-## Overview

+# Overview

 PanomiR is a pipeline to prioritize disease-associated miRNAs based on activity 

 of disease-associated pathways. The input datasets for PanomiR are (a) a gene 

@@ -57,7 +58,7 @@ Individual steps of the workflow can be used in isolation to carry out different

 analyses. The following sections outline each step and material needed to

 execute PanomiR. 

-## 1. Pathway summarization

+# Pathway summarization

 PanomiR can generate pathway activity profiles given a gene expression dataset

 and a list of pathways.Pathway summaries are numbers that represent the overall

@@ -103,7 +104,7 @@ head(summaries)[,1:2]

-## 2. Differential Pathway activation

+# Differential Pathway activation

 Once you generate the pathway activity profiles, as discussed in the last

 section, there are several analysis that you can perform. We have bundled some 

@@ -132,7 +133,7 @@ de.paths <- output0$DEP

 head(de.paths,3)

 ```

-## 3. Finding clusters of pathways

+# Finding clusters of pathways

 PanomiR provides a function to find groups coordinated differentially 

 activated pathways based on a pathway co-expression network (PCxN) previously

@@ -177,7 +178,7 @@ head(pathwayClustsLIHC$Clustering)

 ```

-## 4. Prioritizing miRNAs per cluster of pathways.

+# Prioritizing miRNAs per cluster of pathways.

 PanomiR identifies miRNAs that target clusters of pathways, as defined in the 

 last section. In order to this, you would need a reference table of

@@ -187,7 +188,7 @@ This section provides an overview of prioritization process. Readers interested

 in knowing more about the technical details of PanomiR are refered to

 accompaniying publication (Work under preparation).

-### Enrichment reference

+## Enrichment reference

 Here, we provide a preprocessed small example table of miRNA-pathway enrichment

 in `miniTestsPanomiR$miniEnrich` object. This table contains enrichment analysis

 results using Fisher's Exact Test between MSigDB pathways and TargetScan miRNA

@@ -197,7 +198,7 @@ example table is contains only a full subset of the full pairwise enrichment.

 You can refer to [section 5](#geneset) of this manual on how to create full 

 tables and how to customize them to your specific gene expression data.

-### Generating targeting scores

+## Generating targeting scores

 PanomiR generates a score for individual miRNAs targeting a group of pathways.

 These scores are generated based on the reference enrichment table.

 We are interested in knowing to what extent each miRNA targets pathway clusters

@@ -207,7 +208,7 @@ The significance of observed scores from a given group of pathways (clusters

 in this case) is contrasted against the null distribution to generate a

 targeting p-value. These p-values are used to rank miRNAs per cluster.

-### Sampling parameter

+## Sampling parameter

 The above described process requires repeated sampling to empirically obtain the

 null distribution. The argument `sampRate` denotes the number of repeats in the

 process. Note that in the example below, we use a sampling rate of 50, the

@@ -240,7 +241,7 @@ head(output2$Cluster1)

-## 5. miRNA-Pathway enrichment tables 

+# miRNA-Pathway enrichment tables 

 PanomiR best performs on tissue/experiment-customized datasets. In order to do

 this, you need to create a customized enrichment table. You can simply do so by

@@ -287,7 +288,7 @@ tempEnrich <-miRNAPathwayEnrichment(targetScan_03[1:30],msigdb_c2[1:30])

 head(reportEnrichment(tempEnrich))

 ```

-## 6. Customized genesets and recommendations {#geneset}

+# Customized genesets and recommendations {#geneset}

 PanomiR can integrate genesets and pathways from external sources including

 those annotated in MSigDB. In order to do so, you need to provide a 

@@ -329,10 +330,10 @@ newPathGeneTable      <- tableFromGSC(yourGeneSetCollection)

 ```

-## Session info

+# Session info

 ```{r sessionInfo}

 sessionInfo()

 ```

-## References

\ No newline at end of file

+# References

\ No newline at end of file