create_data_singlecells {alphabetr}

Simulate sequencing data obtained single-cell sequencing

Description

create_data_singlecells() simulates a single-cell sequencing experiment by sampling clones from a clonal structure specified by the user and using the same error models and frequency distributions used in create_data. These functions are almost identical except this one simulates the sampling and sequencing of single T cells.

Usage

create_data_singlecells(TCR, plates = 5, error_drop = c(0.15, 0.01),
  error_seq = c(0.05, 0.01), error_mode = c("constant", "constant"),
  skewed = 15, prop_top = 0.5, dist = "linear")

Arguments

TCR

The specified clonal structure, which can be created from create_clones.

plates

The number of plates of data. The number of single-cells is 96 times plates.

error_drop

A vector of length 2 with the mean of the drop error rate and the sd of the drop error rate.

error_seq

A vector of length 2 with the mean of the in-frame error rate and the sd of the in-frame error rate.

error_mode

A vector of two strings determining the "mode" of the error models. The first element sets the mode of the drop errors, and the second element sets the mode of the in-frame errors. The two modes available are "constant" for a constant error rate and "lognormal" for error rates drawn from a lognormal distribution. If the mode is set to "constant" the sd specified in error_drop and/or error_seq will be ignored.

skewed

Number of clones represent the top proportion of the population by frequency (which is specified by prop_top).

prop_top

The proportion of the population in frequency represented by the number of clones specified by skewed.

dist

The distribution of frequency of the top clones. Currently only "linear" is available.

Value

A list of length 3. The first element is a matrix representing the data of the alpha chains ($alpha), and the second element is a matrix representing the data of beta chains ($beta). The matrix represents the sequencing data by representing the wells of the data by rows and the chain indices by column. Entry [i, j] of the matrix represents if chain j is found in well i (yes == 1, no == 0). e.g. if alpha chain 25 is found in well 10, then [10, 25] of the alpha matrix will be 1.

The third element of the list ($drop) is a matrix that records the index of the clone sampled in the well (col 1), records if a drop error occurred (col 2), and record if an in-frame error occurred (col 3).

Examples

 # see the help for create_clones() for details of this function call
 clones <- create_clones(numb_beta = 1000,
                      dual_alpha = .3,
                      dual_beta  = .06,
                      alpha_sharing = c(0.80, 0.15, 0.05),
                      beta_sharing  = c(0.75, 0.20, 0.05))

 # creating a data set with 480 single cells, lognormal error rates, 10 clones
 # making up the top 60% of the population in frequency, and a constant
 # sampling strategy of 50 cells per well for 480 wells (five 96-well plates)
 dat <- create_data_singlecells(clones$TCR, plate = 5,
                                error_drop = c(.15, .01),
                                error_seq  = c(.05, .001),
                                error_mode = c("lognormal", "lognormal"),
                                skewed = 10,
                                prop_top = 0.6,
                                dist = "linear")