Glimma: getting greater graphics for your genes

第十届中国R会议【直播】

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中产阶级如何利用量化投资工具完成财富进阶

统计转移学习（及其在统计过程控制的应用）

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Statistical learning with genomic big-data

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Data Science, machine learning, precision medicine, and all that

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Adaptive False Discovery Rate regression with application in int

条件随机场及其在生物信息学中的应用

Glimma: getting greater graphics for your genes

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Rsubread: an efficient toolkit for mapping and counting short se

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A smooth simultaneous confidence band for correlation curve

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5478

Simultaneous conficence bands for mean and variance function bas

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顾莉洁

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On a vector double autoregressive model

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Banded Spatio-Temporal Autoregressions with with Application to

基于车辆GPS 数据的交通大数据应用

Branding with social media: User gratifications, usage patterns,

质保数据建模与分析

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PM 2.5 数据的时空特征及统计建模

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地理数据与商业网点选址实战

Identification of disease-causing single nucleotide variants in

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邱伟云

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network diffusion: Simulate and Visualize Network Diffusion

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Elastic Stack 与机器学习

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卢丽强

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Pluto: A Distributed Heterogeneous Deep Learning Framework

王思宇

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Exploring Heterogeneous Algorithms for Accelerating Deep Convolu

嵌入式上的深度学习初探

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刘京辰

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中文文本分析方便工具包chinese.misc介绍

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跟踪 R 社区动态 - R Weekly 的背后

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Learning R Internals and C++ via Rcpp

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Persistent Reproducible Reporting with Docker and R

肖楠

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利用css对shiny页面优化及利用htmlwidgets包创建HTML控件

SAS统计图表：一键式的图表生成术

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数控机床大数据分析

人工智能颠覆客服行业的实践

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心理学在助老机器人研发中的应用

心理学研究规范化及在R语言的实现

R语言在加强心理学可重复性中的作用

基于R与Rstudio的心理统计教学模式探索

基于社会媒体大数据的心理学研究

NLP在金融报告自动化的实践

面向社交媒体的商业大数据挖掘

自然语言处理在医疗智能辅助中的应用

智能时代的量化资产管理

从语言智能到法务智能

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腾讯社交广告实践中智能出价新模式：oCPA

王流斌

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Learning theory for deep nets

林绍波

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Triple Generative Adversarial Networks

Kaggle 数据挖掘比赛经验分享

On equivalence of likelihood maximization of stochastic block mo

张忠元

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从文本分析看小说中人物的复杂关系：以琅琊榜为例

周静

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R语言中的深度学习: 用Mxnet进行车型识别

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增强学习打麻将

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RNA-sequencing is a popular technology used by scientists to study changes in gene expression levels across tens of thousands of genes simultaneously. Representing gene expression levels, the counts in each sample are typically analysed by categorising samples into groups of interest, and obtaining gene-wise summary statistics in the form of log-fold changes, t-statistics, p-values, and the like. The data and its results can be explored by plotting one summary statistic against another and highlighting genes that are significant or of interest. The new Bioconductor package, Glimma, generates interactive graphics for plots typically found in the limma package with the enhanced feature of connecting many levels of information within the analysis on a single html page using d3.js. A Glimma-style mean-difference plot, or the more generic xy-plot, allows one to click on the points to bring up a new plot of sample-wise expression levels that is displayed alongside the original plot. This feature enables researchers to interrogate the data more intensely than ever before without the need to repeat the work for every gene under examination. The plots include options to search and select for genes of interest, and zoom in and out for better resolution. Unlike the traditional multi-dimensional scaling (MDS) plot, Glimma’s MDS plot shows several dimensions and group combinations on the same page. The functions within Glimma are tailored to integrate smoothly with objects native to limma, edgeR and DESeq2, and can be extended for use with microarray, single-cell and methylation data analyses.

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