This is a guide on how to conduct Meta-Analyses in R. […] Welcome to the online version of “Doing Meta-Analysis with R: A Hands-On Guide”. This book serves as an accessible introduction into how meta-analyses can be conducted in R. Essential steps for meta-analysis are covered, including pooling of outcome measures, forest plots, heterogeneity diagnostics, subgroup analyses, meta-regression, methods to control for publication bias, risk of bias assessments and plotting tools. Advanced, but highly relevant topics such as network meta-analysis, multi-/three-level meta-analyses, Bayesian … Read more →

Chen Peng MetaNet is a comprehensive network analysis package, especially in various biological omics. Some functions of MetaNet are dependent with pcutils, so you also need to install pcutils. The stable version can be installed from CRAN: The latest development version can be found in https://github.com/Asa12138/MetaNet: For data manipulation, we recommend to use dplyr. MetaNet is a comprehensive network analysis R package for omics data: Support for integrated analysis for multi-omics data. Calculate correlation network quickly, accelerate lots of analysis by parallel computing. Handle … Read more →

This containes all the supplementary materials for the paper named GraphR: a probabilistic modeling framework for genomic networks incorporating sample heterogeneity. […] Network modeling are widely used in biomedical research, aiming to estimate and visualize complicated dependency structures in various fields and at different level. Graphically, networks compromise a set of variables (nodes) and relationships among nodes which are referred as edges. Under the assumption that: (1) edges represent partial correlation between nodes; (2) nodes follow Gaussian distribution, leading to a … Read more →

These are the course notes for the Machine Learning and Neural Networks module (MATH3431) at Durham University. […] Welcome to the material for the first half of the Machine Learning and Neural Networks module (MATH3431) at Durham University. These pages consist of relevant lecture notes will be updated as the course progresses. I would recommend that you use the html version of these notes (they have been designed for use in this way), however, there is also a pdf version of these notes. In this first half of the module (Michaelmas Term), we will be focusing on “Machine Learning” rather … Read more →

This is a textbook for the course J381M at UT-Austin. […] Welcome to the J381M Textbook! In this course, we will learn how to use R for Computational Communication Research and Data Science, focusing on skills such as data wrangling, basic statistics, data visualization, data collection, NLP, network analysis, and machine learning. This is a survey course that is meant to give you a taste of data science. In truth, many of these topics are rich enough to warrant full courses. This textbook is best paired with the J381M course materials, including lectures, readings, and course assignments. … Read more →

This is a note on financial network, risk contagion and systemic risk. […] The book is a collection of notes of academic literatures on financial network, risk contagion and systemic … Read more →

This containes all the supplementary materials for the paper named SpaceX: Gene Co-expression Network Estimation for Spatial Transcriptomics. […] The spatial transcriptomics method depicts the positioning of a single cell on a spatially structured tissue. Knowledge about gene expressions and the spatial distribution of mRNA allows us to uncover cellular and subcellular heterogeneity in tissues, tumors, and immune cells. Spatial transcriptomics provides a unique opportunity to decipher both the cellular and subcellular architecture in both tissues and individual cells along with detection of … Read more →

This is an introduction to the Epistemic Network Analysis web tool. […] This is the website for Epistemic Network Analysis Web Tool User Guide. This user guide demonstrates how to conduct an Epistemic Network Analysis (ENA) using the ENA web tool. Topics covered in this user guide include how to format data, upload data, construct an ENA model, perform statistical analysis, understand ENA visualizations, and interpret ENA model. Before you dive into this user guide, please keep in mind that this user guide is designed with a focus on utilizing the web tool itself, instead of discussing the … Read more →

Introduction to Computational Social Science […] This seminar is intended as a theoretical and methodological introduction to computational social science. Each week covers substantive and theoretical material and is associated with a technical lab. You will need to bring your laptops to each class. In the technical labs you will learn how to analyze network data in R. This e-book contains all of the technical labs in the order that we cover them. Should you forget anything we learned, you will be able to return to this e-book to cover the material again on your … Read more →

Methods for Network Analysis […] This 4-5 credit hour seminar is intended as a theoretical and methodological introduction to social network analysis. Though network analysis is an interdisciplinary endeavor, its roots can be found in classical anthropology and sociology. Network analysis focuses on patterns of relations between actors. Both relations and actors can be defined in many ways, depending on the substantive area of inquiry. For example, network analysis has been used to study the structure of affective links between persons, flows of commodities between organizations, shared … Read more →

This is a course handbook written by Bodong Chen for his SNA course at UMN. […] This site is built for a course titled CI 8371 - Applied Social Network Analysis in Education, taught by Prof. Bodong Chen at the University of Minnesota. Content on this site will be actively built and refined throughout the Spring 2021 semester. While the course is titled Social Network Analysis in Education, this course is not limited to social networks or to education. We will broadly examine social, information, and artificial networks in a variety of learning contexts including schools, workplace, and … Read more →

Documentation and guidelines for using the ProteoMill platform. […] The analysis of protein enrichment and knowledge of protein-protein interactions have become essential tools in many proteomics studies. We developed ProteoMill, an analysis platform with the purpose to enable researchers to quickly gain insights about the molecular events in their data. ProteoMill is unique in that it is a free, open-source, always up-to-date and easily accessible web tool that renders a complete pipeline from data upload to differential expression-, enrichment- and network analysis. A common requirement … Read more →

This is a minimal example of using the bookdown package to write a book. The output format for this example is bookdown::gitbook. […] Rod photoreceptor death is a form of retinal degeneration that rewires the retina, which eventually leads to blindness. We use genetic and physiological methods to study how the retina encodes the visual world differently. Using spike train consistency and information theoretic metrics, we find that cone mediated visual performance is maintained during retinal degeneration. Next, we find genetic targets that may recover the neural networks in RP patients. … Read more →

Official Guidance：Understanding Surge […] Surge is a networking tool on iOS and macOS platforms with four core capabilities. Takeover: You can take over the network connection sent by the device. Surge supports both proxy service and virtual NIC takeover. Processing: You can modify the network requests and responses that have been taken over. This includes URL redirection, local file mapping, custom modification using JavaScript, and many other methods. Forwarding: You can forward the taken over network requests to other proxy servers. This can be global forwarding or with a flexible rule … Read more →

Data Analysis and Processing with R based on IBIS data […] Over the course of my time working with the Carolina Insitute for Developmental Disabilities (CIDD) and the Infant Brain Imaging Study (IBIS) network, I have seen a great interest in learning how to do basic statistical analyses and data processing among the trainees. Specially, there is an interest in learning how to use R, due to its popularity across the sciences and its zero financial cost. As a statistican in training, I feel it is a great benefit for scientists to learn R. It is vital for scientists to understand the … Read more →

Seeing through the developping lens: […] Through this project, we aim to decipher post-transcriptional regulation network in the developping lens. In the past decades, post-transcriptional gene regulation (PTGR) was shown to be of particular importance in the developping lens. Indeed, the alteration of PTGR network can result in abnormal development of the lens, of the eye. For example, mutations in RNA binding proteins such as Celf1, Stau2, Tdrd7 has been associated to eye’s defects in animal models. mutation in RNA binding protein Tdrd7 was associated with juvenile cataract in human and … Read more →

A guide to visualise networks of Twitter interactions in R using the twinetverse. […] The goal of the twinetverse is to provide everything one might need to analyset and visualise Twitter interactions, from data collection to visualisation. The following pages will walk you trough the packages contained within the twinetverse, from collecting twitter data to building various types of networks to visualising them. The ’verse focuses on ease of use and interactivity. The source code for this book can be found on Github. You can suggest edits to this book by highlighting a section of text and … Read more →

This document focuses on structural equation modeling. It is conceptually based, and tries to generalize beyond the standard SEM treatment. It includes special emphasis on the lavaan package. Topics include: graphical models, including path analysis, bayesian networks, and network analysis, mediation, moderation, latent variable models, including principal components analysis and ‘factor analysis’, measurement models, structural equation models, mixture models, growth curves, item response theory, Bayesian nonparametric techniques, latent dirichlet allocation, and more. Read more →