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TRIALS AND ERRORS BIOINFORMATICS

I just fail forward through learning from published papers.

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Getting the gaps right: using UMAP on scRNA-seq data

It has been about one month now that I am getting my head spinning to learn a lot of bits and pieces about spectral clustering (useful...

Handling new type of data: ECCITE-seq Part 1

So before losing myself in learning about diffusion maps and many other spectral clustering techniques, I am quite excite to see the...

Using Random Forest to find important gene markers

It has been a while since my last update, mainly because there are quite a few things to learn before i could implement random forest on...

Tracing differentiation using RaceID

Cell differentiation is one of the fields that actively employ scRNA-seq to answer intersting questions. scRNA-seq has the high enough...

On selecting the optimal cluster using SC3

It has been a recurring theme on getting the clustering correct in my quest to learn about ScRNA-seq. In the previous posts I have...

How much do we trust the t-SNE plot?

Today I am going to try out a new way to aggregate sc-RNA seq data from independent experiments. The new methodology is called scvis....

Part 1: Dive into network analysis

Clustering methods are heavily employed in single-cell RNA analysis. In this post, I would like to learn more about the underlining...

Finding deceptive cells

Single-cell sequencing generate a lot of noises. One of the sources comes from sequencing low-quality cells. During the high-throughput...

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