Single Cell Transform normalization
The SCTransform normalization is done on the Seurat object prior to PCA. It fits a generalized linear model on the count matrix with library size as covariate. The Pearson residuals of the model are used as scaled data. Although many claims are made that SCT is MUCH better than log normalization, we do not notice […]
Functional analysis of DE genes
Mapping gene identifiers Most tools for functional analysis expect a specific type of gene IDs. The genes in our data set were represented by Ensembl IDs and we have added gene symbols in our R code. However, gene symbols are not always available. Note that there are also non-R alternatives to add info to a […]
Working with sequences
R script for this lesson The Biostrings package to work with sequences in R Create sets of sequences Manipulate sequences Access sequences and bases in a set Import sequences into a set Calculate and plot letter frequencies Calculate and plot letter frequencies in a sliding window Calculate and plot pattern frequencies
Fetch data from NCBI
R script for this lesson
VIB Training Session (AlphaFold)
Basic concepts of image processing
Advanced microscopy techniques
Fundamentals of light microscopy
VIB Training Session (AlphaFold)
Welcome to the VIB training session related to AlphaFold. In this section, you can find the exercises. The powerpoint presentation can be downloaded from here. Interesting further readings:https://blog.matteoferla.com/2022/10/move-aside-coip-westerns-colabfold-has.html
scATACSeq
Contributors to the content: Clint De Nolf. Tutorial on jointly analyzing scATACSeq and scRNASeq 10xGenomics data from the same cells Tutorial on merging different Seurat objects containing scATACSeq data Tutorial on integrating (with batch correction) different Seurat objects containing scATACSeq data (using FindAnchors and IntegrateData, as shown for scRNASeq data in the lesson Batch correction) […]
Functional analysis of DE genes
Almost all the tools below allow to define a custom background. Use a custom background if you can. For single cell RNASeq analysis, the background should be the list of genes present in the Seurat object. For more info on the background I refer to the Function analysis course on the e-learning system. If you’re […]
Annotation
Contributors to the content: Jana Roels, Leander Jonckheere, Ruth Seurinck. Annotating the clusters is the most tedious but also a very important step in the analysis. Annotation based on marker genes Annotation of cell types can be done using marker genes for the clusters, obtained via running FindAllMarkers() on the Seurat object. You look for […]
Subsets
CITESeq
Contributors to the code: Jeroen Sajdak, Ria Roelandt.
Removing noise from the data
There are many potential sources of noise in single cell data.
Finding doublets
Contributors to the code: Liesbet Martens. Below you can find a tutorial and R code on how to use the DoubletFinder tool to find doublets (droplets with more than 1 cell) in your data. It works on any data set. In the topic on cell hashing you find a description on how to remove doublets […]
Handling unwanted cell cycle effects
NicheNet: model cell interactions
Content created by Robin Browaeys. Slides for this lesson Tutorial for this lesson R markdown file for this lesson Simple R script for this lesson Example Seurat object for this lesson NicheNet predicts ligand–target links between interacting cells by combining their expression data with prior knowledge on signaling and gene regulatory networks.
Comparing groups of cells
Contributors to the code: Kevin Verstaen. Content on Muscat created by Robin Browaeys.
Cell ranger: from reads to counts
The video describes the functionality of the different Cellranger tools and provides tips on how to combine multiple samples, either with or without batch correction. If you want to know more about the different file types that are generated by Cellranger check out: Is Cell Ranger doing a good job? Check out this publication for […]
