index.Rmd

---
title: "May Institute 2019: Statistics for quantitative mass spsectrometry"
author: "Meena Choi and Ting Huang"
date: "`r Sys.Date()`"
site: bookdown::bookdown_site
documentclass: book
biblio-style: apalike
link-citations: yes
---

# Before we start {-}

- Download the data files here: https://drive.google.com/file/d/1buDYh8UA0z_5MKzoG8QhQTr9hY4XA_N-/view?usp=sharing


## Suggested reading {-}

* [Points of Significance : Statistics for Biologists](https://www.nature.com/collections/qghhqm/pointsofsignificance)
* [Susan Holmes and Wolfgang Huber, Modern Statistics for Modern Biology. Cambridge University Press, 2019](https://www.huber.embl.de/msmb/introduction.html)

## Schedule {-}


| Day | Time            | Content                                                                                            |
|-----|-----------------|----------------------------------------------------------------------------------------------------|
| 1   | 9:00 - 10:30am   | Keynote : Introduction and  [`MSstats`](https://bioconductor.org/packages/release/bioc/html/MSstats.html) (OV)                                                           |
|     | 10:30 - 11:00am   | Refreshments                                                                                     |
|     | 11:00 - 12:30pm   | MSstats-Introduction to data and preprocessing (MC)                                              |
|     | 12:30 - 1:30pm | Lunch break                                                                                         |
|     | 1:30 - 3:00pm  | MSstats-Normalization, summarization, and missing value (MC)                                        |
|     | 3:00 - 3:30pm   | Refreshments                                                                                       |
|     | 3:30 - 5:00pm   | MSstats-Differential abundance and visualization (MC)                                              |
| 2   | 9:00 - 10:30am  | Lecture: Multiple testing, limma, [`MSstatsTMT`](https://bioconductor.org/packages/release/bioc/html/MSstatsTMT.html), Unsupervised and supervised classification (OV)                                                                              |
|     | 10:30 - 11:00am | Refreshments                                                                                       |
|     | 11:00 - 12:30pm | MSstatsTMT-Introduction to data and preprocessing (TH)                                             |
|     | 12:30 - 13:30pm | Lunch break                                                                                        |
|     | 13:30 - 3:00pm  | MSstatsTMT-Summarization, normalization and group comparison (TH)                                  |
|     | 3:00 - 3:30pm   | Refreshments                                                                                       |
|     | 3:30 - 5:00pm   | Classification (TH)                                                                                |
| 3   | 9:00 - 10:30am  | Keynote (NB)
                              |
|     | 10:30 - 11:00am | Refreshments                                                                                       |
|     | 11:00 - 12:30pm | MassIVE and MassIVE.quant (NB)                  
                              |
|     | 12:30 - 13:30pm | Wrap-up                                                                                            |


Meena Choi (MC), Ting Huang (TH), Olga Vitek (OV), Nuno Bandeira (NB)


## License {-}


This material, unless otherwise stated, is made available under the
[Creative Commons Attribution
license](https://creativecommons.org/licenses/by/4.0/).

You are free to:

* **Share** - copy and redistribute the material in any medium or format
* **Adapt** - remix, transform, and build upon the material for any
  purpose, even commercially.

The licensor cannot revoke these freedoms as long as you follow the license terms.

Under the following terms:

* **Attribution** - You must give appropriate credit, provide a link
  to the license, and indicate if changes were made. You may do so in
  any reasonable manner, but not in any way that suggests the licensor
  endorses you or your use.

No additional restrictions - You may not apply legal terms or
technological measures that legally restrict others from doing
anything the license permits.