Glimma: getting greater graphics for your genes

第十届中国R会议【直播】

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圆桌讨论

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）

大数据时代下的统计学思维—以文本挖掘为例

Building Better Connected World with Artificial Intelligence Tec

李航

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Statistical learning with genomic big-data

刘军

4183

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

王永雄

4953

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

Charity Law

4498

Rsubread: an efficient toolkit for mapping and counting short se

汽车消费的数字化决策

机器学习在滴滴

摩拜单车的数据科学实践

A smooth simultaneous confidence band for correlation curve

张园园

3782

Simultaneous conficence bands for mean and variance function bas

蔡利

3259

Prediction Interval for Autoregressive Time Series via Oracally

顾莉洁

3780

On a vector double autoregressive model

张兴发

3073

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

卓越质量管理中的大数据分析

PM 2.5 数据的时空特征及统计建模

不可或缺的优质地理大数据

基于室内定位数据（IPS）的时空行为分析

地理数据与商业网点选址实战

Identification of disease-causing single nucleotide variants in

癌症转录组大数据的可视化与再挖掘

消化道肿瘤基因组学研究进展

临床医生眼中的医疗大数据研究：需求和挑战

R Usage in Pharmaceutical Industry

手机数据与经济活动测度

词汇、概念、数字：文本探勘技术于中国近代观念史研究中的应用与实践

邱伟云

3963

群像的描绘与类型的分析：用数字工具挖掘《德意志人物志》

王涛

3804

network diffusion: Simulate and Visualize Network Diffusion

计算与人文：作为新领域的“数字人文”

Elastic Stack 与机器学习

Evaluating Fast Algorithms for Convolutional Neural Networks on

卢丽强

7617

Pluto: A Distributed Heterogeneous Deep Learning Framework

王思宇

5719

Exploring Heterogeneous Algorithms for Accelerating Deep Convolu

嵌入式上的深度学习初探

再抽样法分析夫妻般配与家庭工资不平等

法律的定量分析及其实践

Latent Variable Modeling for Cognitive Assessment Through Second

刘京辰

4511

中文文本分析方便工具包chinese.misc介绍

大数据教育平台的建设与探索

大数据教育学科建设的关键因素

微启的旋转门：大数据教育界与工业界的生态进化

赵鹏

3517

跟踪 R 社区动态 - R Weekly 的背后

覃文锋

3419

Learning R Internals and C++ via Rcpp

任乾

3424

Persistent Reproducible Reporting with Docker and R

肖楠

3725

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

Interaction+: “让可视化动起来”的既有网页交互

数控机床大数据分析

人工智能颠覆客服行业的实践

机器学习在营销管理中的应用

从统计学生到互金数据科学家之路

“AI+慢性病管理”使精准医疗成为可能

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

bandit算法与推荐系统

腾讯社交广告实践中智能出价新模式：oCPA

王流斌

5647

Learning theory for deep nets

林绍波

4283

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

3335

从文本分析看小说中人物的复杂关系：以琅琊榜为例

周静

5014

R语言中的深度学习: 用Mxnet进行车型识别

条件GAN用于车型设计和判别

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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