Authoring Books with R Markdown
1
Saccharomyces cerevisiae dataset
2
NGS Pipeline: Saccharomyces cerevisiae samples
2.1
Fastq files
2.2
Mandatory arguments:
2.3
Optional arguments:
3
input reads and output
4
Phred offset
5
thresholds for mapping
6
unique mapping and multi-mapping
7
indel detection
8
read trimming
9
distance and orientation of paired end reads
10
number of CPU threads
11
read group
12
read order
13
color space reads
14
dynamic programming
15
detect structural variants
16
gene annotation
17
others
17.1
Mandatory arguments:
17.2
Optional arguments:
17.3
input reads and output
17.4
Phred offset
17.5
thresholds for mapping
17.6
unique mapping and multi-mapping
17.7
indel detection
17.8
read trimming
17.9
distance and orientation of paired end reads
17.10
number of CPU threads
17.11
read group
17.12
read order
17.13
color space reads
17.14
dynamic programming
17.15
detect structural variants
17.16
gene annotation
17.17
others
18
Multi-mapping reads
19
Fractional counting
20
Learning the BAM format
20.1
Introduction
20.2
Installing SAMtools
20.3
Basic usage
20.4
The SAM/BAM format
20.5
Sorting
20.6
Extracting entries mapping to a specific loci
20.7
Indexing
20.8
stats
20.9
BAM flags
20.10
Counting
20.11
Filtering unmapped reads
20.12
Converting a BAM file to a SAM file
20.13
Stats
20.14
samtools calmd/fillmd
20.15
Coverage
20.16
20.17
Creating the counts file
21
RNAseq diferential & exploratory analysis
21.1
Differential Expression Testing
21.2
DESeq2 workflow
21.3
Visualizing RNA-seq results
21.4
Components of the PCA analysis - Variance explained
21.5
HeatMaps
21.6
Saving results
21.7
Review DeSeq2 workflow
21.8
Plotting single genes
22
Data Wrangling and Analyses with Tidyverse”
22.1
What is dplyr?
22.1.1
Loading .csv files in tidy style
22.1.2
Taking a quick look at data frames
22.1.3
Selecting columns and filtering rows
22.2
Challenge 1
22.3
Solution 1
22.4
Alternative solution 1
22.5
Challenge 2
22.6
Solution
22.6.1
Pipes
22.7
Exercise 1: Pipe and filter
22.8
Solution Ex 1
22.8.1
Mutate
22.9
Exercise 2
22.10
Solution Ex2
22.10.1
group_by() and summarize() functions
22.11
Challenge 3
22.12
Solution 3
22.12.1
Reshaping data frames
22.12.2
Resources
23
Data visualization with ggplot2
23.0.1
Learning Objectives
23.1
Plotting with
ggplot2
23.1.1
Challenge (optional)
23.2
Building your plots iteratively
23.2.1
Challenge
23.3
Boxplot
23.3.1
Challenges
23.4
Integrating the pipe operator with ggplot2
23.5
Faceting
23.6
ggplot2
themes
23.6.1
Challenge
23.7
Customization
23.7.1
Challenge
23.8
Arranging plots
23.9
Exporting plots
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RNA-genomics
10
number of CPU threads
-T
Number of CPU threads used, 1 by default.