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8. Feature selection — Single-cell best practices

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We now have a normalized data representation that still preserves biological heterogeneity but with reduced technical sampling effects in gene expression. Single-cell RNA-seq datasets usually contain up to 30,000 genes and so far we only removed genes that are not detected in at least 20 cells. However, many of the remaining genes are not informative and contain mostly zero counts. Therefore, a standard preprocessing pipeline involves the step of feature selection which aims to exclude uninformative genes which might not represent meaningful biological variation across samples. Fig. 8.1 Feature selection generally describes the process of only selecting a subset of relevant features which can be the most informative, most variable or most deviant ones. Usually, the scRNA-seq experiment and resulting dataset focuses on one specific tissue and hence, only a small fraction of genes is informative and biologically variable. Traditional approaches and pipelines either compute the coefficien

10. Feature selection # Key takeaways Feature selection in single-cell RNA-seq data focuses on identifying the most informative genes by using methods like deviance, which avoids biases from arbitrary normalization choices. Motivation Environment setup Steps Install conda : Before creating the environment, ensure that conda is installed on your system. Save the yml content : Copy the content from the yml tab into a file named environment.yml . Create the environment : Open a terminal or command prompt. Run the following command: conda env create -f environment.yml Activate the environment : Af

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