est_phats {dtangle}

Estimate the gene type proportions.

Description

Estimate the gene type proportions.

Usage

est_phats(Y, markers, baseline_ests, gamma, summary_fn = mean,
  inv_scale = function(x) 2^x)

Arguments

Y

Expression matrix.

(Required) Two-dimensional numeric. Must implement as.matrix.

Each row contains expression measurements for a particular sample. Each columm contains the measurements of the same gene over all individuals. Can either contain just the mixture samples to be deconvolved or both the mixture samples and the reference samples. See pure_samples and references for more details.

markers

Marker gene indices.

(Optional) List of one-dimensional integer.

Top-level list should be same length as pure_samples, i.e. one element for each cell type. Each element of the top-level list is a vector of indicies (columns of Y) that will be considered markers of that particular type. If not supplied then dtangle finds markers internally using find_markers. Alternatively, one can supply the output of find_markers to the markers argument.

baseline_ests

List of vectors (same structure as markers). One list entry for each cell type. Each list element is a vector of estimated offset for each marker of the respective type (output from baseline_exprs).

gamma

Expression adjustment term.

(Optional) One-dimensional positive numeric.

If provided as a single positive number then that value will be used for gamma and over-ride the value of gamma chosen by the data_type argument. If neither gamma nor data_type are specified then gamma will be set to one.

summary_fn

What summary statistic to use when aggregating expression measurements.

(Optional) Function that takes a one-dimensional vector of numeric and returns a single numeric.

Defaults to the mean. Other good options include the median.

inv_scale

Inverse scale transformation. Default to exponential as dtangle assumes data has been logarithmically transformed.

Value

Estimated matrix of mixing proportions.

Examples

truth = shen_orr_ex$annotation$mixture
pure_samples <- lapply(1:3, function(i) {
   which(truth[, i] == 1)
})
Y <- shen_orr_ex$data$log
markers = find_markers(Y=Y,pure_samples = pure_samples,
data_type='microarray-gene',marker_method='ratio')$L
K = length(pure_samples)
n_markers = rep(20,K)
mrkrs <- lapply(1:K, function(i) {
       markers[[i]][1:n_markers[i]]
})
baseline = dtangle:::baseline_exprs(Y, pure_samples, mrkrs)
phats <- dtangle:::est_phats(Y, mrkrs, baseline, gamma=.8)