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My regret is not learning linear algebra well during college.

I barely passed the exam for it (and calculus, it was a nightmare :) ).

To be fair..

It was not taught well and it sounded too boring. I did not know what the application of matrix multiplication was, not until…

Many years later, I started to learn bioinformatics. I find many data are just data matrices:

- an RNAseq expression matrix is a gene-by-sample matrix, with entries to be read counts for each gene
- a single-cell expression matrix is a gene-by-cell matrix, with entries to be read counts for each gene
- a ChIP-seq count matrix is a peak-by-sample matrix, with entries to be the number of reads in each peak
- a drug response matrix is a drug-by-sample matrix, with entries to be IC50 for example

and many more… in other words,

**Matrix is EVERYWHERE for bioinformatics (and many other data science topics)!**

Many of the bioinformatics problems can be rephrased as matrix manipulation.

Read this blog post on Matrix factorization on single-cell RNAseq data to see how useful it is!

I have written two blog posts on how Seurat PCA projection/CCA alignment and label transfer work for single-cell RNAseq data in low-level details.

Man, it was hard. I spent 8 hours for each post spanning serveral nights. But I enjoyed it as I also learned the topic deeper.

During the writing of the posts, I really wished I learned linear algebra better during college:) but it is never too late to learn. You can take MIT1806, which is a great course for linear algebra.

And I am re-watching eigenvalues and eigenvectors from 3blue1brown.

You know what? Deep learning is also closely related with matrix calculations, so understand it is definitely helpful.

What’s your regret?

Happy Learning!

Tommy