# Save pre-regression Seurat object saveRDS ( pre_regressed_seurat, file = "data/seurat_pre_regress.rds" ) Apply regression variables To save the genes in the G2M and S phases as character vectors, we can subset the data frame: We are going to download the list of cell cycle phase marker genes by right-clicking here and saving to the data folder. M: M phase is the nuclear division of the cell (consisting of prophase, metaphase, anaphase and telophase).Īt the HBC core, we have accumulated a nice list of genes associated with particular cell cycle phases.During this phase th cell grows in preparation for mitosis and the spindle forms. G2: Gap 2 phase represents the end of interphase, prior to entering the mitotic phase.S: Synthesis phase for the replication of the chromosomes (also part of interphase).From this phase, the cell may enter G0 or S phase. ![]() During G1 there is growth of the non-chromosomal components of the cells. G1: Gap 1 phase represents the beginning of interphase.Some types of cells enter G0 for long periods of time (many neuronal cells), while other cell types never enter G0 by continuously dividing (epithelial cells). The cell is not actively dividing, which is common for cells that are fully differentiated. To examine cell cycle variation in our data, we assign each cell a score, based on its expression of G2/M and S phase markers.Īn overview of the cell cycle phases is given in the image below:Īdapted from Wikipedia (Image License is CC BY-SA 3.0) Cell cycle scoringĬell cycle variation is a common source of uninteresting variation in single-cell RNA-seq data. ![]() Similar to bulk RNA-seq analysis, we can use PCA to identify these sources of variation, which can then be regressed out prior to further analysis. This can include technical noise, batch effects, and/or uncontrolled biological variation (e.g. Your single-cell dataset likely contains “uninteresting” sources of variation. # Scale and center data pre_regressed_seurat % ScaleData ( e = "linear" ) Examining sources of variation in the data Create the script clustering_analysis.R and load the libraries: Therefore, we need to load the Seurat library in addition to the tidyverse library. To perform this analysis, we will be mainly using functions available in the Seurat package.
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