Day 1.jpg
本周的Data Workshop又開始了，這次將圍繞著以R語言為工具，進(jìn)行RNA-seq和ScRNA-seq的分析。今天主要回顧了R introduction的內(nèi)容允乐，溫習(xí)了接下來將要用到的一些commands，然后對(duì)RNA-seq的流程進(jìn)行了系列介紹削咆。
1. Introduction to R (Dr. Rocio T Martinez-Nunez）
1.1 Objects
- Assign to objects(vectors, tables, values, functions)
1.2 Commenting your code
- Just add (#) before what you want to comment
1.3 system(): communicates with the shell in your computer
system("ls -F/")
1.4 cmd as a group of commands
cmd <- paste("gunzip -c", fastq.files, "| head")
cmd # to view cmds & runs
system(cmd[1]) # Run the first command of cmd
1.5 Some R tips
1.5.1 ask for help
# in R: ? + function
?system
#in shell : (-h)
system("trim_galore -h")
1.5.2 Tab: look for the list of word match in R.
1.5.3 Arrow keys: up row-the last thing you type in.
1.5.4 Pines %>% in R or | in shell
install. packages("tidyverse") # install packages
library("tidyverse") # load packages
download.file("website", "path and name. csv") # download file
surveys <- read_csv("path and name. csv") # open file
str( surveys) # inspect the data: an overview of an object's structure and its elements
dim( surveys) # size: row numbers and column numbers
head( surveys) # check the top(first six lines) of the data frame
surveys_new <- surveys %>% # pipes
filter(weight < 5) %>% # filter
select(species_id, sex, weight) # select
str(surveys_new) # inspect the data: an overview of an object's structure and its elements
dim(surveys_new) # size: row numbers and column numbers
head(surveys_new) # check the top(first six lines) of the data frame
- Only works when install tidyverse.
- %>% : shortcut keys in PC: ctrl + shift + M
- %>% means then, (the things we want pipe) on the left, and (the things we want to pine into) on the right.
1.6 Some R functions we will be using:
# create command cmd that includes trim_galore and its flags with the object we apply it to
cmd <- paste("trim_galore --length 21 --output_dir trimgalore, fastq.files)
# run only the first line of the commands
system(cmd[1])
# create vector with the power of 1, 2 and 3:
sapply(1:3, function(x) x^2)
#[1] 1, 4, 9
- system(): communicates with the shell.
- dir.create(): create directories.
- list.files(): list the files in your working directory.
- paste(): concatenates vectors after converting into character.
- data.frame(): generates a data frame.
- sapply(): applies a function to an object and returns a simplified object.
1.7 Loops: vectorization & sapply
for (year in c(2010, 2011, 2012, 2013, 2014, 2015)){
print(paste("The year is", year))
}
2. Introduction to RNA-seq data analysis (Dr. Alessandra Vigilante)
2.1 What is NGS
- Next-generation sequencing (NGS), also known as high-throughput sequencing, is the term used to describe a number of different modern sequencing technologies, such as RNA-seq, ScRNA-seq, ChIP-seq et al.
2.2 Eight stages in RNA-seq Analysis
2.2.1 Define the question of interest (RNA-seq data can tell us)
- Relative expression levels within a biological sample
- Gene expression differences between biological samples
- Quantify alternative transcript levels
- Confirm annotated 5′ and 3′ ends of genes
- Map exon/intron boundaries
2.2.2 Get the data(data formats)
- Raw data: Fastq
- Aligned data: SAM, BAM, CRAM
- Genome annotation: GFF
- Intervals: BED
- Variants: VCF, BCF
2.2.3 Clean the data(quality control)
- FastQC: trimmomatic, cutadapt
- The ShortRead package in R/Bioconductor using the qa() and report () functions
2.2.4 Map the data
- Chanllenges: large costs in memory; introns; updates of reference genomes, tools and softwares.
- Mapping srategies: de novo assembly, align to transcriptome, align to genome.
- Tools: Bowtie 2, TopHat 2, STAR
- Pseudo-alignment: Kallisto - faster and more accurate
- If you have SAM files you have to transform them to BAM
- You can visualise your BAM files in IGV
- Use either your BAM file or the transcript abundance file (from Kallisto) to
generate a Count Table - Perform differential expression analysis and downstream analyses
2.2.5 Explore the data
2.2.6 Fit statistical models
2.2.7 Make your analysis reproducible
RNA-seq workflow in the workshop
3. Learning experience
- 今天第一個(gè)到workshop牍疏，一切準(zhǔn)備很充分，全天學(xué)習(xí)很投入拨齐。
- 今天課程比較雜鳞陨，遇到的很多新的問題和挑戰(zhàn)，需要好好消化瞻惋。
- 今天認(rèn)識(shí)了Guys Campus的口腔醫(yī)學(xué)華人博士厦滤，聊得很開心援岩，KCL的口腔醫(yī)學(xué)已經(jīng)世界排名第二啦，進(jìn)一步了解了國外博士的生活和學(xué)習(xí)風(fēng)貌掏导，值得學(xué)習(xí)他們的新技術(shù)新方法窄俏。
- 今天還認(rèn)識(shí)了Denmark Campus的生信大牛，樂于助人還給我們講述他的學(xué)習(xí)歷程碘菜，希望接下來可以繼續(xù)向他們請(qǐng)教，互幫互助限寞。
本次筆記借鑒了KCL Workshop的學(xué)習(xí)資料及課件忍啸，請(qǐng)勿轉(zhuǎn)載，如需引用請(qǐng)注明履植。
