Mapping gene names to Ensembl IDs

Description

A data set showing the mapping between gene names and Ensembl gene IDs, derived from the EnsDb.Hsapiens.v79 Bioconductor package.

Usage

gene_mapping

Format

A data frame with 59074 rows and 2 variables:

ensembl

Ensembl gene IDs

name

corresponding gene names

Source

https://bioconductor.org/packages/release/data/annotation/html/EnsDb.Hsapiens.v79.html

Penalized Principal Component Analysis for Marker Gene Selection

Description

This function solves the optimization problem

\min\quad-\mathrm{tr}(SX) + \lambda p(X),

s.t.\quad O\preceq X \preceq I, \quad X \ge 0, \quad\mathrm{and}\quad \mathrm{tr}(X)=1,

where O\preceq X \preceq I means all eigenvalues of X are between 0 and 1, X \ge 0 means all elements of X are nonnegative, and p(X) is a penalty function defined in the article (see the References section).

Usage

pca_pen(
  S,
  gr,
  lambda,
  w = 1.5,
  alpha = 0.01,
  maxit = 1000,
  eps = 1e-04,
  verbose = 0
)

Arguments

Value

A list containing the following components:

projection

The estimated projection matrix.

evecs

The estimated eigenvectors.

niter

Number of iterations used in the optimization process.

err_v

The optimization error in each iteration.

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

set.seed(123)
n = 200  # Sample size
p = 500  # Number of genes
s = 50   # Number of true signals

# The first s genes are true markers, and others are noise
Sigma = matrix(0, p, p)
Sigma[1:s, 1:s] = 0.9
diag(Sigma) = 1

# Simulate data from the covariance matrix
x = matrix(rnorm(n * p), n) %*% chol(Sigma)

# Sample correlation matrix
S = cor(x)

# Indices of prior marker genes
# Note that we have omitted 10 true markers, and included 10 false markers
gr = c(1:(s - 10), (s + 11):(s + 20))

# Run the algorithm
res = pca_pen(S, gr, lambda = 0.1, verbose = 1)

# See if we can recover the true correlation structure
image(res$projection, asp = 1)

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
)

Arguments

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.

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)

Post-processing Selected Marker Genes

Description

This function reorders the selected marker genes using information of the sample correlation matrix.

Usage

sort_markers(corr, markers)

Arguments

Value

A list that has the same structure as the input markers argument, with the elements in each component reordered. See the example in refine_markers().