Dynverse: a one-stop shop to build and compare TI methods
I have to say that this is the coolest scRNA resource so far in 2019! Dynverse had a suite of tools that helps researcher to select, run...
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TRIALS AND ERRORS BIOINFORMATICS
I just fail forward through learning from published papers.
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Remove batch effects, just to make things more complicated
NB: This is just a commentary It all began when I was looking into the latest paper of Dynverse (https://dynverse.org/) (now published in...
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...
Integrated analysis of scRNA, HTO, sgRNA data : ECCITE-seq Part 2
Okay, so this week I looked into another ECCITE-seq data: the K562 cells infected with a CRISPr library, tagged with cell-hashing...
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...
Matching gene expression profile to staining patterns on a zebrafish embryo
In this post, I am looking into an earlier topic solved by Seurat infering spatial location of cells through marker gene expression in...
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....
Step 2.1: Getting it right; how many clusters are enough?
After deriving the distance matrix from the data, I can now move on to clustering the cells based on expression profiles. I usually...
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...
MetaNeighbor - assigning cell identity using Spearman correlation
With the increasing published scRNA-seq studies, one of the ideals is to compare the findings across studies to confirm 1) novel subtypes...
Calculating cell-cell distance: How far are the cells apart from each other?
It looks like that Euclidean and Manhattan both have lower within cluster distance and higher between cluster distance but Inner-product...
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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