RNA-seq-cheatsheet.md

Bulk RNA-seq Cheatsheet

The tables below consist of valuable functions or commands that will help you through this module.

Each table represents a different library/tool and its corresponding commands.

Please note that these tables are not intended to tell you all the information you need to know about each command.

The hyperlinks found in each piece of code will take you to the documentation for further information on the usage of each command.

Base R

Read the Base R package documentation here.

Library/Package	Piece of Code	What it's called	What it does
Base R	list.files()	List files	Produces a character vector of files or directories in the specified directory
Base R	names()	Names	Gets or sets the names of an object
Base R	colnames()	Column names	Gets or sets the column names of a matrix or data frame
Base R	all.equal()	All equal	Checks if two R objects are nearly equal
Base R	attr()	Object Attributes	Gets or sets the attributes of an object
Base R	rowSums()	Row Sums	Returns the sum of the rows in a numeric matrix-like object (i.e.. a matrix, data.frame, etc.)
Base R	relevel()	Relevel	Reorders the levels of a factor as specified
Base R	summary()	Object Summary	Returns a result summary of an object
Base R	as.data.frame()	Data Frame	Checks if an object is a data.frame, and transforms the object into one, if possible

DESeq2

Read the DESeq2 package documentation here, and the package vignette by Love, Anders, and Huber here.

Library/Package	Piece of Code	What it's called	What it does
DESeq2	vst()	Variance Stabilizing Transformation	Applies variance stabilizing transformation to data (log2-like scale)
DESeq2	DESeqDataSet()	DESeqDataSet constructor that can take a SummarizedExperiment	Creates a DESeqDataSet object
DESeq2	DESeqDataSetFromMatrix()	DESeqDataSet constructor	Creates a DESeqDataSet object from a matrix of count data
DESeq2	DESeq()	Differential Expression Analysis Based on the Negative Binomial Distribution	Estimates size factors, estimates dispersion, and performs negative binomial fitting and Wald statistics as steps in the default DESeq2 differential expression analysis
DESeq2	plotPCA()	PCA plot	Produces a principal component analysis plot for transformed data. It can be used to visually inspect the data, which might allow an analyst to identify batch effects.
DESeq2	counts()	Counts	Returns count matrix from DESeqDataSet object
DESeq2	results()	Results	Returns the results table from a DESeq2 analysis
DESeq2	assay()	Assay	Returns matrix from the assay slot of a DESeqDataSet object

FastQC and fastp

Read the FastQC documentation here and the fastp documentation here.

Library/Package	Piece of Code	What it's called	What it does
fastp	fastp	FASTQ preprocessor	Preprocesses FASTQ files through adapter trimming, quality filtering, length filtering, and a number of additional options
FastQC	fastqc	FASTQC (Quality Control)	Performs quality control checks on raw sequence data and outputs a QC(quality control) report

ggplot2

Read the ggplot2 package documentation here. A vignette on the usage of the ggplot2 package can be found here.

Library/Package	Piece of Code	What it's called	What it does
ggplot2	ggsave()	GG Save	Saves the last plot in working directory
ggplot2	last_plot()	Last plot	Returns the last plot produced
ggplot2	geom_point()	Geom point	Creates a scatterplot (when added to the ggplot() function)
ggplot2	xlab(); ylab()	X Axis Labels; Y Axis Labels	Modifies the labels on the x axis and on the y axis, respectively
ggplot2	coord_fixed()	Cartesian Coordinates with Fixed Aspect Ratio	Coerces the coordinates on the plot to represent a fixed specified ratio

tximeta and SummarizedExperiment

Read the tximeta package documentation here, and the package vignette by Love et al. here. Read the SummarizedExperiment package documentation here, and the package vignette by Morgan et al. here.

Library/Package	Piece of Code	What it's called	What it does
tximeta	tximeta()	tximeta	Imports transcript-level estimates, attaches transcriptome annotation, and returns a SummarizedExperiment object
tximeta	makeLinkedTxome()	Make Linked Transcriptome	Sets up transcriptome annotation to be used by the tximeta() function (Only necessary if tximeta() fails to find annotation, like for non-human, non-mouse species data)
tximeta	summarizeToGene()	Summarize to Gene	Takes a SummarizedExperiment that was set up by tximeta and summarizes transcript data to the gene-level
SummarizedExperiment	rowData() colData()	Col/Row Data	Accesses the row or column data from a SummarizedExperiment object
SummarizedExperiment	assay() assayNames()	Assay or AssayNames	Accesses the assay data or the names of the assays from SummarizedExperiment object

stringr, readr, dplyr, pheatmap

Documentation for each of these packages can be accessed by clicking the package name in the table below.

Library/Package	Piece of Code	What it's called	What it does
stringr	word()	Word	Extracts words from a character vector
readr	write_rds()	Write RDS	Writes data to a .RDS output file
dplyr	pull()	Pull	Extracts a variable (column) as a vector
pheatmap	pheatmap()	Pretty heatmap	Plots clustered heatmaps

Salmon

Read the Salmon tool documentation here.

Tool	Piece of Code	What it's called	What it does
Salmon	salmon index	Salmon index	Builds a transcriptome index which is required for Salmon quantification (from the command line)
Salmon	salmon quant	Salmon quantification	Runs Salmon’s quantification of transcript expression (from the command line)

Useful command line commands:

Feel free to give these commands a try on your own! (Note that our example begins in the training-module directory.)