Preface
I Introduction
1
Introduction of R
1.1
Setup
1.2
Basic systax
1.2.1
Install packages
1.2.2
Install
1.3
Data type
1.4
Data structures
1.5
Read from and write into files
1.5.1
Read Random Rows from A Huge File
1.5.2
Reference
1.6
Functions
1.7
Control structure
1.8
Reference
2
Plot in R
2.1
Read the data
2.2
Line plot
2.3
Basic line plot
2.4
Color the dots based on the
sex
information
2.5
2.6
Draw a circle in R
2.7
Network
2.7.1
3
Correlation
3.1
Visulization of pair-wise correlation in R
3.2
Correlation and p-values of all combinations of all rows of two matrices
3.2.1
Prepare the data
3.2.2
Another way to do this is to use
corr.test
3.2.3
Read data
3.2.4
Calculate the correlation (Advance)
3.2.5
Reference
3.3
Different ways to visulize correlation
3.3.1
Display correlation using
chart.Correlation
in
PerformanceAnalytics
3.4
Reference
II Statistics
4
Standard deviation vs Standard error
4.1
Reference
5
T test
5.1
Calculate t-statistic step by step in R
5.1.1
5.2
Calculate the standard deviation in R
5.3
Reference
6
Linear model
6.1
6.2
Extract male data
6.3
Reference
7
MRG
7.1
Read the data
8
Multiple linear regression
8.1
8.2
9
Bayesian newwork in R
9.1
Practical experiments
9.1.1
Analysis of the MAGIC population in Scutari et al., Genetics (2014)
9.1.2
Reference
10
The rationale for using negative binomial distribution to model read count in RNA-seq data
10.1
Negative binomial in R
10.1.1
Real data
10.2
Reference
10.3
Batch effect
10.4
Reference
11
Multiple test corretion
12
Working with missing values
12.1
12.2
Summaryize the missing values
12.2.1
Summarize the missing values using
mice
package
12.2.2
Summarize the missing values using
VIM
packages
12.3
Delete columns/rows with more that x% missing
12.4
Imputing the data
12.4.1
Imputing the data with row-wise mean
12.4.2
Imputing the data with row-wise mean using
mice
12.5
Reference
13
Data wrangling
13.1
13.2
Joining Data in R with dplyr
13.2.1
Whats Covered
13.3
Reference
III Machine learning
14
PCA
14.1
Read data
14.2
Head data
14.3
Summary of the data
14.4
Check the distribution of the data
14.5
Eigendecomposition - Computing Eigenvectors and Eigenvalues
14.5.1
Covariance Matrix
14.6
PCA with two variables
14.7
PCA on
wine
data
14.7.1
Read the
wine
data
14.7.2
Description of each column
14.7.3
Example
14.8
References
15
Random forest
15.1
Practical experiments
15.1.1
Random forest for prediction of iris
16
QTL analysis
16.1
Reference
References
17
Doing Bayesian statistics in R
17.1
Reference
18
Transformation of data
18.1
Log transformation
18.1.1
The rationale of using log2 transformation
18.1.2
Reference
19
Goodness of fit in R
19.1
Reference
20
Overdispersion
20.1
Recognising (and testing for) overdispersion
20.2
Refefernce
21
Package management
22
Permutation and combination
22.1
Reference
23
Partial least square analysis
23.1
Prepare the data
23.2
Principal Component Analysis (PCA)
23.3
Partial least-squares: PLS and PLS-DA
23.4
Reference
24
Scale
24.1
Reference
25
Web scraping
25.1
Download Content from NCBI Databases using
RISmed
25.1.1
Search author name
25.1.2
Search keyword
26
Linear mixed model in R
26.1
Data from an Oats Field Trial
26.2
Data visulization
26.3
Mixed linear model
Notes of R for Bioinformatics
11
Multiple test corretion