WGCNA analysis using RNA-seq datasets

My main codes follow the instruction from Horvath lab

Useful links for the code

1. Harvard DE Workshop https://informatics.fas.harvard.edu/workshops/HarvardInformatics_DEworkshop_Fall2017.html

2. RNA-seq workshop at ASPB 2018, covered RNA-seq data analysis using Kallisto and Sleuth https://github.com/JasonJWilliamsNY/kallisto-rnaseq-jupyter/blob/master/sleuth.Rmd

3. Kallisto https://pachterlab.github.io/kallisto/starting. Note that Kallisto is not used for mapping novel transcripts.

4. https://rafalab.github.io/dsbook/

5. UT Austin RNA-seq training https://wikis.utexas.edu/display/bioiteam/Pseudomapping+with+Kallisto

6. I used MultiQC to examine the quality of multiple samples and give nice summary of FASTQC https://github.com/ewels/MultiQC

7. UT Austin training on WCGNA https://wikis.utexas.edu/display/bioiteam/Clustering+using+WGCNA

8. WGCNA slides https://pdfs.semanticscholar.org/ecac/5c1dc9dcbe6845abfd34e12ca091d3f993fb.pdf

9. Found the command line bootcamp which is really useful for beginner http://rik.smith-unna.com/command_line_bootcamp/?id=tb3uo8zpxks

10. https://edu.isb-sib.ch/pluginfile.php/158/course/section/65/_01_SIB2016_wgcna.pdf

11. Analysis of RNA-seq. PCA for outliers

12. Training on Differential gene expression, especially PCA to check sample quality

13. Lots of genomics data analysis references here

14. co-expression tutorials and WGCNA understanding

15. Bioconductor-RNA_seq analysis

Get gene name from biomaRt, Arabidopsis gene names. I figured out this is so much easier than the traditionally method that I used before to combine gene name from TAIR website with my results.

Instead using biomaRt and choose athaliana_eg_gene from host =plants.ensembl.org

Other turotials

• Venn diagram