MEM, Marker Enrichment Modeling, automatically generates and displays quantitative labels for cell populations that have been identified from single-cell data. The input for MEM is a dataset that has pre-clustered or pre-gated populations with cells in rows and features in columns. Labels convey a list of measured features and the features' levels of relative enrichment on each population. MEM can be applied to a wide variety of data types and can compare between MEM labels from flow cytometry,
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Perform Marker Enrichment Modeling (MEM) analysis on single-cell data to generate quantitative population labels, visualize them via heatmaps, and calculate similarity scores between populations.
library(cytoMEM)
data(PBMC)
# Run MEM analysis on the PBMC dataset
MEM_values <- MEM(
PBMC,
transform = TRUE,
cofactor = 15,
choose.markers = FALSE,
markers = "all",
choose.ref = FALSE,
zero.ref = FALSE,
rename.markers = FALSE,
new.marker.names = "none",
IQR.thresh = NULL
)
# Generate population labels and heatmaps
build_heatmaps(
MEM_values,
cluster.MEM = "both",
cluster.medians = "none",
cluster.IQRs = "none",
display.thresh = 1,
output.files = FALSE,
labels = FALSE,
only.MEMheatmap = FALSE
)
# Calculate similarity (RMSD) scores between populations
MEM_RMSD(
MEM_values[[5]][[1]],
format = NULL,
output.matrix = FALSE
)
Input/Output Note: Inputs a matrix or data frame of single-cell expression data with a cluster ID column; outputs MEM scores, enrichment labels, heatmaps, and population similarity matrices.
MEM().build_heatmaps().MEM_RMSD().Seurat or scran because cytoMEM requires pre-clustered or pre-gated populations.edgeR or DESeq2 because cytoMEM is designed to generate descriptive enrichment labels rather than statistical hypothesis testing.flowCore because cytoMEM expects pre-processed, gated, or clustered expression matrices..txt, .csv, .fcs).file.is.clust = TRUE).transform = TRUE with a specified cofactor, e.g., cofactor = 15 for mass cytometry).FALSE): Logical indicating whether to apply a hyperbolic arcsine transformation to the data.15): The cofactor used in the hyperbolic arcsine transformation.FALSE): Logical indicating whether to select markers interactively via the console."all"): Character string specifying which markers to include in the analysis.FALSE): Logical indicating whether to choose an alternative reference population.FALSE): Logical indicating whether to use a zero (synthetic negative) reference.NULL): Threshold for interquartile range to avoid artificial inflation of MEM values (defaults internally to 0.5).0): Numeric value (0-10) specifying the minimum MEM score required for a marker to be displayed.MEM() and set transform = FALSE if different cofactors are required for different fluorescence channels.IQR.thresh of 0.5 unless you have a deep understanding of the dataset and the implications of changing it.labels = TRUE in build_heatmaps() to display the full MEM labels directly on the generated heatmap.output.files = TRUE) to prevent row names (MEM labels) from being cut off due to R window dimensions.output.files = TRUE to save the heatmap as a PDF and open it in a PDF viewer, or read the full labels from the generated enrichment score-rownames.txt file.IQR.thresh is set to at least 0.5 (the default) to threshold low IQR values.Seurat: For comprehensive single-cell RNA-seq clustering and marker identification using differential expression.scater: For single-cell quality control and visualization of expression patterns.scran: For formal statistical testing of marker genes using differential expression.flowCore: For low-level flow cytometry data import, compensation, and transformation.还没有评价。欢迎分享你的使用感受。
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