Bioinformatics

Notes for Bioinformatics study

Basic for Linux

First thing is to learn how to enter the server: ssh space name@ip direction,and enter the keyword.

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Enter Linux

Some Commands

pwd Print Work Directory:showing the direction of current path

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Enter Linux

mkdir Making Directory:creating a catalog

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Enter Linux

ls check how many documents or catalogs

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Enter Linux

rm cd rm : to remove documents rmdir : to remove catalogs rm-r : to remove unempty catalog cd space name of catalog : enter this catalog

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miniconda instal

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using this command to set up “bash Miniconda3-latest-Linux-x86_64.sh”

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to active: source ~/.bashrc

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if something wrong here: try

rm ~/.condarc conda config –add channels https://mirrors.bfsu.edu.cn/anaconda/cloud/bioconda/ conda config –add channels https://mirrors.bfsu.edu.cn/anaconda/cloud/conda-forge/ conda config –add channels https://mirrors.bfsu.edu.cn/anaconda/pkgs/free/ conda config –add channels https://mirrors.bfsu.edu.cn/anaconda/pkgs/main/ conda config –set show_channel_urls yes

installing the package: conda install fastqc -y

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removing the package: conda remove fastqc -y

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could use : packagename space –help to check wether download or not

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check the environment

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enter new environment conda activate rna-seq

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boxplot(iris$Sepal.Length~iris$Species,col = c("lightblue","lightyellow","lightpink"))

save(X,file=“test.RData”)

#a <- read.table(file = "huahua.txt")

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

test <- iris[c(1:2,51:52,101:102),]
mutate(test, new = Sepal.Length * Sepal.Width)

##     Sepal.Length Sepal.Width Petal.Length Petal.Width    Species   new
## 1            5.1         3.5          1.4         0.2     setosa 17.85
## 2            4.9         3.0          1.4         0.2     setosa 14.70
## 51           7.0         3.2          4.7         1.4 versicolor 22.40
## 52           6.4         3.2          4.5         1.5 versicolor 20.48
## 101          6.3         3.3          6.0         2.5  virginica 20.79
## 102          5.8         2.7          5.1         1.9  virginica 15.66

select(test,1)

##     Sepal.Length
## 1            5.1
## 2            4.9
## 51           7.0
## 52           6.4
## 101          6.3
## 102          5.8

select(test, Petal.Length, Petal.Width)

##     Petal.Length Petal.Width
## 1            1.4         0.2
## 2            1.4         0.2
## 51           4.7         1.4
## 52           4.5         1.5
## 101          6.0         2.5
## 102          5.1         1.9

filter(test, Species == "setosa")

##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1          5.1         3.5          1.4         0.2  setosa
## 2          4.9         3.0          1.4         0.2  setosa

arrange(test, Sepal.Length)

##   Sepal.Length Sepal.Width Petal.Length Petal.Width    Species
## 1          4.9         3.0          1.4         0.2     setosa
## 2          5.1         3.5          1.4         0.2     setosa
## 3          5.8         2.7          5.1         1.9  virginica
## 4          6.3         3.3          6.0         2.5  virginica
## 5          6.4         3.2          4.5         1.5 versicolor
## 6          7.0         3.2          4.7         1.4 versicolor

summarise(test, mean(Sepal.Length), sd(Sepal.Length))

##   mean(Sepal.Length) sd(Sepal.Length)
## 1           5.916667        0.8084965

test %>% 
  group_by(Species) %>% 
  summarise(mean(Sepal.Length), sd(Sepal.Length))

## # A tibble: 3 x 3
##   Species    `mean(Sepal.Length)` `sd(Sepal.Length)`
##   <fct>                     <dbl>              <dbl>
## 1 setosa                     5                 0.141
## 2 versicolor                 6.7               0.424
## 3 virginica                  6.05              0.354

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