aggregate_gene_expression: Creates a matrix with aggregated expression values for...

In cole-trapnell-lab/monocle3: Clustering, differential expression, and trajectory analysis for single- cell RNA-Seq

Creates a matrix with aggregated expression values for arbitrary groups of genes

Description

Creates a matrix with aggregated expression values for arbitrary groups of genes

Usage

aggregate_gene_expression(
  cds,
  gene_group_df = NULL,
  cell_group_df = NULL,
  norm_method = c("log", "binary", "size_only"),
  pseudocount = 1,
  scale_agg_values = TRUE,
  max_agg_value = 3,
  min_agg_value = -3,
  exclude.na = TRUE,
  gene_agg_fun = "sum",
  cell_agg_fun = "mean"
)

Arguments

cds	The cell_data_set on which this function operates
gene_group_df	A dataframe in which the first column contains gene ids or short gene names and the second contains groups. If NULL, genes are not grouped.
cell_group_df	A dataframe in which the first column contains cell ids and the second contains groups. If NULL, cells are not grouped.
norm_method	How to transform gene expression values before aggregating them. If "log", a pseudocount is added. If "size_only", values are divided by cell size factors prior to aggregation.
pseudocount	Value to add to expression prior to log transformation and aggregation.
scale_agg_values	Whether to center and scale aggregated groups of genes.
max_agg_value	If scale_agg_values is TRUE, the maximum value the resulting Z scores can take. Higher values are capped at this threshold.
min_agg_value	If scale_agg_values is TRUE, the minimum value the resulting Z scores can take. Lower values are capped at this threshold.
exclude.na	Logical indicating whether or not to exclude NA values from the aggregated matrix.
gene_agg_fun	Function used for gene aggregation. This can be either sum or mean. Default is sum.
cell_agg_fun	Function used for cell aggregation. Default is mean.

Value

A matrix of dimension NxM, where N is the number of gene groups and M is the number of cell groups.

Examples

  ## Not run: 
     expression_matrix <- readRDS(system.file('extdata',
                                               'worm_l2/worm_l2_expression_matrix.rds',
                                               package='monocle3'))
     cell_metadata <- readRDS(system.file('extdata',
                              'worm_l2/worm_l2_coldata.rds',
                               package='monocle3'))
     gene_metadata <- readRDS(system.file('extdata',
                              'worm_l2/worm_l2_rowdata.rds',
                              package='monocle3'))

     cds <- new_cell_data_set(expression_data=expression_matrix,
                              cell_metadata=cell_metadata,
                              gene_metadata=gene_metadata)

    cds <- preprocess_cds(cds, num_dim = 100)
    cds <- reduce_dimension(cds)
    cds <- cluster_cells(cds, resolution=1e-5)
    colData(cds)$assigned_cell_type <- as.character(partitions(cds))
    colData(cds)$assigned_cell_type <- dplyr::recode(colData(cds)$assigned_cell_type,
                                                    "1"="Germline",
                                                    "2"="Body wall muscle",
                                                    "3"="Unclassified neurons",
                                                    "4"="Vulval precursors",
                                                    "5"="Failed QC",
                                                    "6"="Seam cells",
                                                    "7"="Pharyngeal epithelia",
                                                    "8"="Coelomocytes",
                                                    "9"="Am/PH sheath cells",
                                                    "10"="Failed QC",
                                                    "11"="Touch receptor neurons",
                                                    "12"="Intestinal/rectal muscle",
                                                    "13"="Pharyngeal neurons",
                                                    "14"="NA",
                                                    "15"="flp-1(+) interneurons",
                                                    "16"="Canal associated neurons",
                                                    "17"="Ciliated sensory neurons",
                                                    "18"="Other interneurons",
                                                    "19"="Pharyngeal gland",
                                                    "20"="Failed QC",
                                                    "21"="Ciliated sensory neurons",
                                                    "22"="Oxygen sensory neurons",
                                                    "23"="Ciliated sensory neurons",
                                                    "24"="Ciliated sensory neurons",
                                                    "25"="Ciliated sensory neurons",
                                                    "26"="Ciliated sensory neurons",
                                                    "27"="Oxygen sensory neurons",
                                                    "28"="Ciliated sensory neurons",
                                                    "29"="Unclassified neurons",
                                                    "30"="Socket cells",
                                                    "31"="Failed QC",
                                                    "32"="Pharyngeal gland",
                                                    "33"="Ciliated sensory neurons",
                                                    "34"="Ciliated sensory neurons",
                                                    "35"="Ciliated sensory neurons",
                                                    "36"="Failed QC",
                                                    "37"="Ciliated sensory neurons",
                                                    "38"="Pharyngeal muscle")
    neurons_cds <- cds[,grepl("neurons", colData(cds)$assigned_cell_type, ignore.case=TRUE)]
    pr_graph_test_res <- graph_test(neurons_cds, neighbor_graph="knn")
    pr_deg_ids <- row.names(subset(pr_graph_test_res, q_value < 0.05))
    gene_module_df <- find_gene_modules(neurons_cds[pr_deg_ids,], resolution=1e-2)
    cell_group_df <- tibble::tibble(cell=row.names(colData(neurons_cds)),
                                    cell_group=partitions(cds)[colnames(neurons_cds)])
    agg_mat <- aggregate_gene_expression(neurons_cds, gene_module_df, cell_group_df)
  
## End(Not run)

cole-trapnell-lab/monocle3 documentation built on May 24, 2022, 5:25 p.m.