scquantum (version 1.0.0)

ploidy.inference: Ploidy inference with quantogram

Description

Infer ploidy of a cell, given a copy number profile. Constructs a quantogram (either modular or cosine, depending on parameters). The maximum of the quantogram is the estimated ploidy. If unsegmented bincounts are given, segmentation will be performed using the fused lasso.

Usage

ploidy.inference(
  x,
  chrom = NULL,
  start = NULL,
  end = NULL,
  penalty = 25,
  do_segmentation = TRUE,
  seg_length = NULL,
  iod = NULL,
  mean_bincount = NULL
)

Arguments

x

Bincounts or segment means

chrom

Optional chromosome numbers

start

Optional bin/segment start positions

end

Optional bin/segment end positions

penalty

If segmenting, penalty parameter for the fused lasso (higher penalty, fewer segments)

do_segmentation

Boolean, whether to do segmentation (set this to TRUE if giving unsegmented bincounts)

seg_length

If giving already segmented data, length of each segment

iod

If giving already segmented data, the index of dispersion of the bincount distribution (that is, within segments, not including between-segment variance)

mean_bincount

If giving already segmented ratio values, the original mean bincount

Value

A ploidy inference object

penalty

The segmentation penalty given as an argument, if any

multiply_ratios_by

To convert ratios to (unrounded) copy number estimates, multiply by this number

subtract_from_scaled_ratios

To convert ratios to (unrounded) copy numbers, after multiplying, subtract this number. Only required if the count data have some extra reads even at copy number 0, generally due to mapping problems

ploidy

The estimated ploidy

peak_height

The height of the quantogram peak at the estimated ploidy. Between 0 and 1. Higher values indicate a stronger signal

segmentation

The segmented values (either given as an argument, or produced interally by segmentation)

polar_quantogram

The complex-valued quantogram, whose absolute values measure consistency with each possible ploidy

bincounts

The raw bincounts given as an argument (if a segmentation was not given directly)

theoretical_quantogram

Based on the inferred copy numbers and index of dispersion, what the absolute value of the quantogram should look like. Deviation of this theoretical quantogram from the real one indicate that the ploidy estimate may be wrong

theoretical_peak_height

Height of the peak in the theoretical quantogram, measuring the expected strength of signal for the ploidy value

confidence_ratio

Ratio of actual to theoretical peak height. Values near (or above) 1 indicate the signal was as strong as would be expected gievn this data quality and ploidy; low values indicate that the ploidy inference may be wrong or that there are unexpected quality issues with the data

Examples

# NOT RUN {
# Generating a random copy number profile
set.seed(705)
cns <- rpois(30, 3) + 1
x <- unlist(lapply(cns, function(cn) rpois(100, 25 * cn)))
annotations <- data.frame(chrom = 1, start = 1:length(x), end = 1:length(x))

# Inferring ploidy
# Annotations and penalty are optional
estimate.from.bincounts <- ploidy.inference(x, annotations$chrom, annotations$start, penalty = 25)

# Using scquantum internal functions to segment the data and estimate index
# of [e-mail]:navinlabcode/scquantum.git
# dispersion
mu.est <- mean(x)
iod.est <- timeseries.iod(x)
seg <- prof2invals(x, 25, annotations, "chrom", "start", "end")
mean.est <- mean(x)
iod.est <- timeseries.iod(x)
estimate.from.segmentation <-
  ploidy.inference(
    seg$mean,
    seg$chrom,
    seg$start,
    seg$end,
    iod = iod.est,
    mean_bincount = mean.est,
    do_segmentation = FALSE
  )
# }