其他
看到了一本有意思的书籍：《现代生物学所需要的现代统计学》，名字是我自己翻译的。
主要是因为太多小伙伴在咱们《生信技能树》后台咨询过想不错生物学知识和统计学知识，恰好这个《Modern Statistics for Modern Biology》把二者涵盖了，在线阅读链接：https://www.huber.embl.de/msmb/index.html
全书还配套代码哦：
source("https://www.huber.embl.de/msmb/install_packages.R")
Data
Zipped data directory，压缩包自己下载，https://www.huber.embl.de/msmb/data.tar.gz
Code
Rfiles folder，链接是：https://www.huber.embl.de/msmb/code/
章节目录：
Home Book supplements Physical Copy Introduction 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 Acknowledgements References
确实非常详细，图表代码丰富，比如第8节是高通量测序数据表达量矩阵处理：
Goals of this chapter Some core concepts Count data Modeling count data A basic analysis Critique of default choices and possible modifications Multi-factor designs and linear models Generalized linear models Two-factor analysis of the pasilla data Further statistical concepts Summary of this chapter Further reading Exercises
使用了一个R包《pasilla》里面的果蝇的表达量矩阵和分组信息：
fn = system.file("extdata", "pasilla_gene_counts.tsv",
package = "pasilla", mustWork = TRUE)
counts = as.matrix(read.csv(fn, sep = "\t", row.names = "gene_id"))
annotationFile = system.file("extdata",
"pasilla_sample_annotation.csv",
package = "pasilla", mustWork = TRUE)
pasillaSampleAnno = readr::read_csv(annotationFile)
pasillaSampleAnno
然后根据分组，构建好比较信息，使用DESeq2包如下所示代码即可差异分析 ：
library("dplyr")
pasillaSampleAnno = mutate(pasillaSampleAnno,
condition = factor(condition, levels = c("untreated", "treated")),
type = factor(sub("-.*", "", type), levels = c("single", "paired")))
library("DESeq2")
pasilla = DESeqDataSetFromMatrix(
countData = counts,
colData = pasillaSampleAnno[mt, ],
design = ~ condition)
class(pasilla)
pasilla = DESeq(pasilla)
res = results(pasilla)
res[order(res$padj), ] %>% head
是不是超级方便啊！
生物学背景也可以看公开课
因为绝大部分转生物信息学工程师的小伙伴都是有至少4年的生物学背景，生物大分子，中心法则都没有问题，但是也有部分计算机背景学生转过来，会不停的问我该如何补充生物学背景，这里推荐慕课(https://www.icourse163.org/)的两个课程
复旦大学的基因组学：https://www.icourse163.org/course/FUDAN-1002839009#/info 四川大学的细胞生物学：https://www.icourse163.org/course/SCU-46011 其它课程请自行搜索，按需学习，争取掌握生信基础100讲：https://mp.weixin.qq.com/s/Gr_0H4-GaTYkgUkbNHcMcg
ngs课程还有更多
不容错过的B站免费NGS数据处理视频课程，目前，已经组建了微信交流群的有下面这些：