Modern Statistics for Modern Biology (Paperback)
暫譯: 現代生物學的現代統計學 (平裝本)

Holmes, Susan, Huber, Wolfgang

Modern Statistics for Modern Biology (Paperback)
  • 出版商: Cambridge
  • 出版日期: 2019-03-21
  • 售價: $1,235
  • 語言: 英文
  • 頁數: 402
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1108705294
  • ISBN-13: 9781108705295
  • 相關分類: 機率統計學 Probability-and-statistics

    • 下單後立即進貨 (約5~7天)

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商品描述

If you are a biologist and want to get the best out of the powerful methods of modern computational statistics, this is your book. You can visualize and analyze your own data, apply unsupervised and supervised learning, integrate datasets, apply hypothesis testing, and make publication-quality figures using the power of R/Bioconductor and ggplot2. This book will teach you 'cooking from scratch', from raw data to beautiful illuminating output, as you learn to write your own scripts in the R language and to use advanced statistics packages from CRAN and Bioconductor. It covers a broad range of basic and advanced topics important in the analysis of high-throughput biological data, including principal component analysis and multidimensional scaling, clustering, multiple testing, unsupervised and supervised learning, resampling, the pitfalls of experimental design, and power simulations using Monte Carlo, and it even reaches networks, trees, spatial statistics, image data, and microbial ecology. Using a minimum of mathematical notation, it builds understanding from well-chosen examples, simulation, visualization, and above all hands-on interaction with data and code.

商品描述(中文翻譯)

如果您是一位生物學家，並希望充分利用現代計算統計的強大方法，那麼這本書就是為您而寫。您可以視覺化和分析自己的數據，應用無監督學習和監督學習，整合數據集，進行假設檢驗，並使用 R/Bioconductor 和 ggplot2 的強大功能製作出版品質的圖形。本書將教您「從零開始烹飪」，從原始數據到美麗的啟發性輸出，您將學會在 R 語言中編寫自己的腳本，並使用來自 CRAN 和 Bioconductor 的高級統計套件。它涵蓋了在高通量生物數據分析中重要的廣泛基本和高級主題，包括主成分分析和多維尺度分析、聚類、多重檢驗、無監督學習和監督學習、重抽樣、實驗設計的陷阱，以及使用蒙地卡羅方法的效能模擬，甚至還涉及網絡、樹狀結構、空間統計、影像數據和微生物生態學。這本書使用最少的數學符號，通過精心挑選的範例、模擬、視覺化，最重要的是與數據和代碼的實際互動來建立理解。

作者簡介

Susan Holmes is Professor of Statistics at Stanford University, California. She specializes in exploring and visualizing multidomain biological data, using computational statistics to draw inferences in microbiology, immunology and cancer biology. She has published over 100 research papers, and has been a key developer of software for the multivariate analyses of complex heterogeneous data. She was the Breiman Lecturer at NIPS 2016, has been named a Fields Institute fellow, and is currently a fellow at the Center for the Advances Study of the Behavioral Sciences.
Wolfgang Huber is Research Group Leader and Senior Scientist at the European Molecular Biological Laboratory, where he develops computational methods for new biotechnologies and applies them to biological discovery. He has published over 150 research papers in functional genomics, cancer and statistical methods. He is a founding member of the open-source bioinformatics software collaboration Bioconductor and has co-authored two books on Bioconductor.

作者簡介(中文翻譯)

蘇珊·霍姆斯是加州史丹佛大學的統計學教授。她專注於探索和視覺化多領域的生物數據，利用計算統計學在微生物學、免疫學和癌症生物學中進行推斷。她已發表超過100篇研究論文，並且是複雜異質數據多變量分析軟體的主要開發者。她曾是2016年NIPS的布雷曼講者，並被任命為菲爾茲研究所的研究員，目前是行為科學高級研究中心的研究員。

沃爾夫岡·胡伯是歐洲分子生物實驗室的研究小組負責人和高級科學家，他開發新生物技術的計算方法並將其應用於生物發現。他在功能基因組學、癌症和統計方法方面發表了超過150篇研究論文。他是開源生物信息學軟體合作項目Bioconductor的創始成員，並共同撰寫了兩本關於Bioconductor的書籍。

目錄大綱

1. Generative models for discrete data
2. Statistical modeling
3. High-quality graphics in R
4. Mixture models
5. Clustering
6. Testing
7. Multivariate analysis
8. High-throughput count data
9. Multivariate methods for heterogeneous data
10. Networks and trees
11. Image data
12. Supervised learning
13. Design of high-throughput experiments and their analyses
Statistical concordance

目錄大綱(中文翻譯)

1. Generative models for discrete data

2. Statistical modeling

3. High-quality graphics in R

4. Mixture models

5. Clustering

6. Testing

7. Multivariate analysis

8. High-throughput count data

9. Multivariate methods for heterogeneous data

10. Networks and trees

11. Image data

12. Supervised learning

13. Design of high-throughput experiments and their analyses

Statistical concordance

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