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Get top lasso result from glm_mg_result

Source: R/lm_mg_result.R
get_top_mg.Rd

Accessor function to retrieve the 'top_result' dataframe from an 'glm_mg_result' object.

Usage

get_top_mg(obj, coef_cutoff = 0.05)

Arguments

obj

An 'glm_mg_result' object.

coef_cutoff

A positive number giving the coefficient cutoff value. Genes whose top cluster showing a coefficient value smaller than the cutoff will be marked as non-marker genes ("NoSig"). Default is 0.05.

Value

A data frame with detailed information for each gene and the most relevant cluster label.

  • gene Gene name

  • top_cluster The name of the most relevant cluster after thresholding the coefficients.

  • glm_coef The coefficient of the selected cluster in the generalised linear model.

  • pearson Pearson correlation between the gene vector and the selected cluster vector.

  • max_gg_corr A number showing the maximum pearson correlation for this gene vector and all other gene vectors in the input gene_mt

  • max_gc_corr A number showing the maximum pearson correlation for this gene vector and every cluster vectors in the input cluster_mt

Examples

library(SpatialExperiment)
set.seed(100)
#  simulate coordinates for clusters
df_clA <- data.frame(x = rnorm(n=100, mean=20, sd=5),
                 y = rnorm(n=100, mean=20, sd=5), cluster="A")
df_clB <- data.frame(x = rnorm(n=100, mean=100, sd=5),
                y = rnorm(n=100, mean=100, sd=5), cluster="B")

clusters <- rbind(df_clA, df_clB)
clusters$sample<-"sample1"

# simulate coordinates for genes
trans_info <- data.frame(rbind(cbind(x = rnorm(n=100, mean=20,sd=5),
                                y = rnorm(n=100, mean=20, sd=5),
                                 feature_name="gene_A1"),
                           cbind(x = rnorm(n=100, mean=20, sd=5),
                                 y = rnorm(n=100, mean=20, sd=5),
                                 feature_name="gene_A2"),
                           cbind(x = rnorm(n=100, mean=100, sd=5),
                                 y = rnorm(n=100, mean=100, sd=5),
                                 feature_name="gene_B1"),
                           cbind(x = rnorm(n=100, mean=100, sd=5),
                                 y = rnorm(n=100, mean=100, sd=5),
                                 feature_name="gene_B2")))
trans_info$x<-as.numeric(trans_info$x)
trans_info$y<-as.numeric(trans_info$y)
trans_info$cell<-sample(c("cell1","cell2","cell2"),replace=TRUE,
                        size=nrow(trans_info))
trans_mol <- BumpyMatrix::splitAsBumpyMatrix(
    trans_info[, c("x", "y")], 
    row = trans_info$feature_name, col = trans_info$cell )
spe<- SpatialExperiment(
     assays = list(molecules = trans_mol),sample_id ="sample1" )
w_x <- c(min(floor(min(trans_info$x)),
         floor(min(clusters$x))),
      max(ceiling(max(trans_info$x)),
          ceiling(max(clusters$x))))
w_y <- c(min(floor(min(trans_info$y)),
          floor(min(clusters$y))),
      max(ceiling(max(trans_info$y)),
          ceiling(max(clusters$y))))
vecs_lst <- get_vectors(x=spe,sample_names=c("sample1"),
                    cluster_info = clusters,
                    bin_type = "square",
                    bin_param = c(20,20),
                    test_genes =c("gene_A1","gene_A2","gene_B1","gene_B2"),
                    w_x = w_x, w_y=w_y)
lasso_res <- lasso_markers(gene_mt=vecs_lst$gene_mt,
                        cluster_mt = vecs_lst$cluster_mt,
                        sample_names=c("sample1"),
                        keep_positive=TRUE,
                        background=NULL)
# the top result
top_result<- get_top_mg(lasso_res, coef_cutoff=0.05)