Differential discovery with CATALYST |
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Differential discovery with CATALYST
Helena L Crowell1,2* and Mark D Robinson1,2 1Institute for Molecular Life Sciences, University of Zurich, Switzerland2SIB Swiss Institute of Bioinformatics, University of Zurich, Switzerland 24 October 2023 PackageCATALYST 1.26.0 Contents 1 Example data 2 Data preparation 3 Clustering 3.1 cluster: FlowSOM clustering & ConsensusClusterPlus metaclustering 3.2 mergeClusters: Manual cluster merging 3.3 Delta area plot 4 Visualization 4.1 plotCounts: Number of cells measured per sample 4.2 pbMDS: Pseudobulk-level MDS plot 4.2.1 Ex. 2: MDS on sample-level pseudobulks 4.2.2 Ex. 1: MDS on pseudobulks by cluster-sample 4.3 clrDR: Reduced dimension plot on CLR of proportions 4.3.1 Ex. 1: CLR on cluster proportions across samples 4.3.2 Ex. 2: CLR on sample proportions across clusters 4.4 plotExprHeatmap: Heatmap of aggregated marker expressions 4.5 plotPbExprs: Pseudobulk expression boxplot 4.6 plotClusterExprs: Marker-densities by cluster 4.7 plotAbundances: Relative population abundances 4.8 plotFreqHeatmap: Heatmap of cluster fequencies 4.9 plotMultiHeatmap: Multi-panel Heatmaps 4.9.1 Ex. 1: Type- & state-markers 4.9.2 Ex. 2: CDx markers & cluster frequencies 4.9.3 Ex. 3: Selected markers 5 Dimensionality reduction 6 Filtering 7 Differental testing with diffcyt 7.1 plotDiffHeatmap: Heatmap of differential testing results 7.2 Ex. 1: DA testing results 7.3 Ex. 2: DS testing results 7.4 Ex. 3: Filtering results 7.5 Ex. 4: Customizing appearance 8 More 8.1 Exporting FCS files 8.2 Using other clustering algorithms 8.3 Customizing visualizations 8.3.1 Modifying ggplots 8.3.2 Modifying ComplexHeatmaps 8.4 Combining ComplexHeatmaps 8.4.1 Ex. 1: type- & state-markers + cluster frequencies 8.4.2 Ex. 2: frequencies + selected markers + all markers 9 Session information ReferencesMost of the pipeline and visualizations presented herein have been adapted from Nowicka et al. (2019)’s “CyTOF workflow: differential discovery in high-throughput high-dimensional cytometry datasets” available here. # load required packages library(CATALYST) library(cowplot) library(flowCore) library(diffcyt) library(scater) library(SingleCellExperiment) 1 Example data PBMC_fs: a flowSet holding PBMCs samples from 4 patients, each containing between 500 and 1000 cells. For each sample, the expression of 10 cell surface and 14 signaling markers was measured before (REF) and upon BCR/FcR-XL stimulation (BCRXL) with B cell receptor/Fc receptor crosslinking for 30’, resulting in a total of 8 samples. This data set represents a subset of data originating from Bodenmiller et al. (2012) that was also used in the citrus paper (Bruggner et al. 2014). PBMC_panel: a data.frame containing each marker’s column name in the FCS file (fcs_colname column), its targeted protein marker (antigen column), and the marker_class (“type” or “state”). PBMC_md: a data.frame where rows correspond to samples, and columns specify each sample’s file_name, sample_id, condition, and patient_id. # load example data data(PBMC_fs, PBMC_panel, PBMC_md) PBMC_fs ## A flowSet with 8 experiments. ## ## column names(24): CD3(110:114)Dd CD45(In115)Dd ... HLA-DR(Yb174)Dd ## CD7(Yb176)Dd head(PBMC_panel) ## fcs_colname antigen marker_class ## 1 CD3(110:114)Dd CD3 type ## 2 CD45(In115)Dd CD45 type ## 3 pNFkB(Nd142)Dd pNFkB state ## 4 pp38(Nd144)Dd pp38 state ## 5 CD4(Nd145)Dd CD4 type ## 6 CD20(Sm147)Dd CD20 type head(PBMC_md) ## file_name sample_id condition patient_id ## 1 PBMC_patient1_BCRXL.fcs BCRXL1 BCRXL Patient1 ## 2 PBMC_patient1_Ref.fcs Ref1 Ref Patient1 ## 3 PBMC_patient2_BCRXL.fcs BCRXL2 BCRXL Patient2 ## 4 PBMC_patient2_Ref.fcs Ref2 Ref Patient2 ## 5 PBMC_patient3_BCRXL.fcs BCRXL3 BCRXL Patient3 ## 6 PBMC_patient3_Ref.fcs Ref3 Ref Patient3The code snippet below demonstrates how to construct a flowSet from a set of FCS files. However, we also give the option to directly specify the path to a set of FCS files (see next section). # download exemplary set of FCS files url |
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