markerpen (version 0.1.2)

refine_markers: Marker Gene Selection via Penalized Principal Component Analysis

Description

This function refines a prior marker gene list by combining information from bulk tissue data, based on the penalized principal component analysis. The current implementation computes on one cell type at a time. To get marker genes for multiple cell types, call this function iteratively.

Usage

refine_markers(
  mat_exp,
  range,
  markers,
  lambda,
  w = 1.5,
  thresh = 0.001,
  alpha = 0.01,
  maxit = 1000,
  eps = 1e-04,
  verbose = 0
)

Value

A list containing the following components:

spca

The sparse PCA result as in pca_pen().

markers

A character vector of selected markers genes.

markers_coef

The estimated factor loadings for the associated genes.

Arguments

mat_exp

The gene expression matrix in the original scale (not logarithm-transformed), with rows standing for observations and columns for genes. The matrix should include gene names as column names.

range

A character vector of gene names, representing the range of genes in which markers are sought.

markers

A character vector of gene names giving the prior marker gene list.

lambda

A tuning parameter to control the number of selected marker genes. A larger value typically means a smaller number of genes.

w

Tuning parameter to control the weight on prior information. Larger \(w\) means genes not in the prior list are less likely to be selected as markers.

thresh

Below this threshold small factor loadings are treated as zeros.

alpha

Step size of the optimization algorithm.

maxit

Maximum number of iterations.

eps

Tolerance parameter for convergence.

verbose

Level of verbosity.

References

Qiu, Y., Wang, J., Lei, J., & Roeder, K. (2020). Identification of cell-type-specific marker genes from co-expression patterns in tissue samples.

Examples

# Data used in the vignette
load(system.file("examples", "gene_expr.RData", package = "markerpen"))
load(system.file("examples", "published_markers.RData", package = "markerpen"))
load(system.file("examples", "markers_range.RData", package = "markerpen"))

# Get expression matrix - rows are observations, columns are genes
ind = match(rownames(dat), markerpen::gene_mapping$name)
ind = na.omit(ind)
ensembl = markerpen::gene_mapping$ensembl[ind]
mat_exp = t(dat[markerpen::gene_mapping$name[ind], ])
colnames(mat_exp) = ensembl

# We compute the marker genes for two cell types with a reduced problem size
# See the vignette for the full example

# Markers for astrocytes
set.seed(123)
search_range = intersect(markers_range$astrocytes, ensembl)
search_range = sample(search_range, 300)
prior_markers = intersect(pub_markers$astrocytes, search_range)
ast_re = refine_markers(
    mat_exp, search_range, prior_markers,
    lambda = 0.35, w = 1.5, maxit = 500, eps = 1e-3, verbose = 0
)
# Remove selected markers from the expression matrix
mat_rest = mat_exp[, setdiff(colnames(mat_exp), ast_re$markers)]

# Markers for microglia
search_range = intersect(markers_range$microglia, ensembl)
search_range = sample(search_range, 300)
prior_markers = intersect(pub_markers$microglia, search_range)
mic_re = refine_markers(
    mat_exp, search_range, prior_markers,
    lambda = 0.35, w = 1.5, maxit = 500, eps = 1e-3, verbose = 0
)

# Refined markers
markers_re = list(astrocytes = ast_re$markers,
                  microglia  = mic_re$markers)
# Visualize the correlation matrix
cor_markers = cor(mat_exp[, unlist(markers_re)])
image(cor_markers, asp = 1)

# Post-process the selected markers
# Pick the first 20 ordered markers
markers_ord = sort_markers(cor_markers, markers_re)
markers_ord = lapply(markers_ord, head, n = 20)
# Visualize the correlation matrix
image(cor(mat_exp[, unlist(markers_ord)]), asp = 1)