- CODING SYSTEMS FOR CATEGORICAL VARIABLES IN REGRESSION ANALYSIS: http://bit.ly/3okguTN
- Linear Mixed Models: http://stanford.io/3bf2mqU
- A Bayesian Approach to Linear Mixed Models (LMM) in R/Python: http://bit.ly/3oqAXX6
- Probability concepts explained: Bayesian inference for parameter estimation: http://bit.ly/2JOjtVk
- Bayesian Statistics explained to Beginners in Simple English: http://bit.ly/3bktnJT
- RNA-Seq: a revolutionary tool for transcriptomics: https://www.nature.com/articles/nrg2484
- Recommended RNA-seq pipeline: http://bit.ly/3s9AUkG
- RNA-seq workflow: gene-level exploratory analysis and differential expression: http://bit.ly/38o2eE1
- Differential gene expression (DGE) analysis, training module: http://bit.ly/3hLyljM
- DGE normalisation methods: http://bit.ly/3rXDWZ9
- RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR: http://bit.ly/2LqJbQk
- Biomedical Data Science: different analyses: http://genomicsclass.github.io/book/
- Inferring differential exon usage in RNA-Seq data with the DEXSeq package: https://bioconductor.org/packages/devel/bioc/vignettes/DEXSeq/inst/doc/DEXSeq.html
- tidybulk: an R tidy framework for modular transcriptomic data analysis: https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-02233-7#Sec11
- MCMSeq: Bayesian hierarchical modeling of clustered and repeated measures RNA sequencing experiments: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-020-03715-y
- Biological classification with RNA-seq data: Can alternatively spliced transcript expression enhance machine learning classifiers?: https://rnajournal.cshlp.org/content/24/9/1119
- Misuse of RPKM or TPM normalization when comparing across samples and sequencing protocols: https://rnajournal.cshlp.org/content/early/2020/04/13/rna.074922.120
- Single cell: https://www.singlecellcourse.org/processing-raw-scrna-seq-sequencing-data-from-reads-to-a-count-matrix.html
- Using MultiQC: https://multiqc.info/docs/
- Minimal safe Bash script template: https://betterdev.blog/minimal-safe-bash-script-template/
- Population genetics and genomics in R, Analysis of genome data: http://bit.ly/3hMznMx ( https://grunwaldlab.github.io/Population_Genetics_in_R/index.html )
- Statistics for Genomic Data Science: https://www.coursera.org/learn/statistical-genomics#syllabus
- Command Line Tools for Genomic Data Science: https://www.coursera.org/learn/genomic-tools#syllabus
- Bioconductor for Genomic Data Science: https://www.coursera.org/learn/bioconductor#syllabus
- Teaching R online with RStudio Cloud: http://bit.ly/38hwekE
- Morphological characterization and staging of bumble bee pupae: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6302898/pdf/peerj-06-6089.pdf
- Bumblebee development: https://www.bumblebee.org/lifecycle.htm
- B. terrestris queen making an egg cell: https://www.youtube.com/watch?v=QX-3RMH0nFQ
- TM3’seq: A Tagmentation-Mediated 3’ Sequencing Approach for Improving Scalability of RNAseq Experiments: https://www.g3journal.org/content/10/1/143