Bookmarks: Tutorials for bioinformatics & computational science tools

Links to free tools and tutorials. To be updated occasionally...

Reading in large csv spreadsheets (e.g. 1-2GB)

• Optimize your data read in. Use the "data.table" R package fread() function instead of base read.csv(). It's faster and allows you to import only certain columns or rows. 
• https://www.infoworld.com/article/3566384/5-handy-options-in-r-datatables-fread.html
• Read data as a matrix and make column 1 the row names, and row 1 the header:
  m = as.matrix(fread("bigmatrix.csv", header=TRUE), rownames=1)
• To read in a 2 GB spreadsheet, you need 16 GB of RAM. In my experience, 8 GB RAM results in out of memory errors.

Annotating omics data with Ensembl's BioMart tool (R, Perl)

• "biomaRt" R package 
• https://www.bioconductor.org/packages/devel/bioc/vignettes/biomaRt/inst/doc/biomaRt.html
• https://ucdavis-bioinformatics-training.github.io/2018-September-Bioinformatics-Prerequisites/friday/limma_biomart_vignettes.html
• Ensembl.org also provides a Perl API for BioMart

RNA-seq workflow (Unix, R)

• UCLA workshops (YouTube recordings of live workshops): 
• https://qcb.ucla.edu/collaboratory/workshops/
• Click on each topic box to get slides and a YouTube video of the workshop

Comparing edgeR, DESeq2, and limma

• Table comparison from ArrayStar Help
  
  

DESeq2 differential expression analysis (R)

• Analyzing RNA-seq data with DESeq2 by Michael I. Love, Simon Anders, and Wolfgang Huber
• Analysis of RNA-Seq data: gene-level exploratory analysis and differential expression
• RNA-seq workflow (2019 version): variance stabilizing transformations
• 
  

edgeR workflow

• https://web.stanford.edu/class/bios221/labs/rnaseq/lab_4_rnaseq.html
• 
  

Machine learning 101 (Matlab, Python)

• https://medium.com/machine-learning-101
• Naive Bayes
• SVM = Support Vector Machine
• Decision Tree Classifier
• K Nearest Neighbors
• Random Forest Classifier
• Adaboost Classifier

Comparing sequences

• BLAST - compare your sequence to another given sequence, or to the human genome, or to other genomes or transcriptomes
• Nucleotide BLAST compares sequences
• blastx - give it a DNA/RNA sequence to be compared to proteins
• tblastn - give it a protein sequence to be compared to DNA/RNA sequences
• Protein BLAST - compare protein sequences
• ClustalW - input sequences in a FASTA format and align them
• Reduce the "Gap Extension Penalty" to zero if you're comparing DNA to mRNA (thus allowing for introns to interrupt the alignment, which the default algorithm avoids)
• Use this when you want to see the full alignment, not just short windows of good alignment that BLAST provides

Enrichment Analysis - methods with no coding required

• Ingenuity Pathway Analysis (QIAGEN) - click on "Resources" and search for webinars. The software is free, but the license is not. Cedars-Sinai has an institutional license that you can request through EIS.
• Gene Ontology - free but less detailed than IPA

Data visualization

• Principal components analysis (PCA) in R with a matrix of data (Hefin Rhys, YouTube)
• Cluster analysis in R with a matrix of data (Hefin Rhys, YouTube)
• Heatmap demystified (Ming Tang)
• Understand how to scale data in heatmaps (Ming Tang, "A Tale of Two Heatmaps")
• Adjusting axes in ggplot2

Statistical models

• "Beyond Multiple Linear Regression" by Paul Roback and Julie Legler - textbook for statistics with R programming, advanced linear regression, model selection, evaluating model assumptions
• Model assumptions and examples that fit or don't fit those assumptions
• Introduction to multilevel models  - MLM is not the same as a GLM with covariables
• Generalized linear models (GLM) versus multilevel generalized linear models (MLM), an example case

General links

• Pak Yu's github: https://sfpacman.github.io/cookbook/index.html