Error(s) in re-building vignettes:
  ...
--- re-building ‘basic-deconvolution.Rmd’ using rmarkdown
--- finished re-building ‘basic-deconvolution.Rmd’

--- re-building ‘basic-deconvolution2.Rmd’ using rmarkdown
dtangle2                package:dtangle                R Documentation

_D_e_c_o_n_v_o_l_v_e _c_e_l_l _t_y_p_e _m_i_x_i_n_g _p_r_o_p_o_r_t_i_o_n_s _f_r_o_m _g_e_n_e _e_x_p_r_e_s_s_i_o_n _d_a_t_a.

_D_e_s_c_r_i_p_t_i_o_n:

     Deconvolve cell type mixing proportions from gene expression data.

_U_s_a_g_e:

     dtangle2(Y, references = NULL, pure_samples = NULL, n_markers = NULL,
       markers = NULL, marker_method = "ratio", weights = NULL,
       sto = TRUE, inv_scale = function(x) 2^x, fit_scale = log,
       loss_smry = "var", dtangle_init = TRUE, seed = NULL,
       verbose = FALSE, optim_opts = NULL)
     
_A_r_g_u_m_e_n_t_s:

       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.

references: Cell-type reference expression matrix.

          (Optional) Two-dimensional numeric. Must implement
          'as.matrix'. Must have same number of columns as 'Y'. Columns
          must correspond to columns of 'Y'.

          Each row contains expression measurements for a reference
          profile of a particular cell type. Columns contain
          measurements of reference profiles of a gene. Optionally may
          merge this matrix with 'Y' and use 'pure_samples' to indicate
          which rows of 'Y' are pure samples. If 'pure_samples' is not
          specified 'references' must be specified. In this case each
          row of 'references' is assumed to be a distinct cell-type. If
          both 'pure_samples' and 'references' are specified then
          'pure_samples' specifies to which cell-type each row of
          'references' corresponds.

pure_samples: The pure sample indicies.

          (Optional) List of one-dimensional integer. Must implement
          'as.list'.

          The i-th element of the top-level list is a vector of
          indicies (rows of 'Y' or 'references') that are pure samples
          of type i. If 'references' is not specified then this
          argument identifies which rows of 'Y' correspond to pure
          reference samples of which cell-types. If 'references' is
          specified then this makes same idenficiation but for the
          'references' matrix instead.

n_markers: Number of marker genes.

          (Optional) One-dimensional numeric.

          How many markers genes to use for deconvolution. Can either
          be a single integer, vector of integers (one for each cell
          type), or single or vector of percentages (numeric in 0 to
          1). If a single integer then all cell types use that number
          of markers. If a vector then the i-th element determines how
          many marker genes are used for the i-th cell type. If single
          percentage (in 0 to 1) then that percentage of markers are
          used for all types. If vector of percentages then that
          percentage used for each type, respectively. If not specified
          then top 10% of genes are used.

 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.

marker_method: Method used to rank marker genes.

          (Optional) One-dimensional string.

          The method used to rank genes as markers. If not supplied
          defaults to ``ratio''. Only used if markers are not provided
          to argument ``markers''. Options are

            • 'ratio' selects and ranks markers by the ratio of the
              mean expression of each gene in each cell type to the
              mean of that gene in all other cell types.

            • 'regression ' selects and ranks markers by estimated
              regression coefficients in a series of regressions with
              single covariate that is indicator of each type.

            • 'diff' selects and ranks markers based upon the
              difference, for each cell type, between the median
              expression of a gene by each cell type and the median
              expression of that gene by the second most highly
              expressed cell type.

            • 'p.value' selects and ranks markers based upon the
              p-value of a t-test between the median expression of a
              gene by each cell type and the median expression of that
              gene by the second most highly expressed cell type.

 weights: Weights for the genes.

          (Optional) String or one-dimensional numeric vector.

          Weights for the genes in the optimization. If NULL (default)
          then does not weight genes differently. If 'variance' then
          inversely weights with the variance of the references. This
          only works if there is more than one reference per cell type
          so that the variance can be estimated. If a numeric then this
          uses whatever is specified as weights. They must be
          non-negative.

     sto: Sum-to-one constraint.

          (Optional) Boolean.

          Re-normalize the estimates so that the cell-type proportions
          sum to one.

inv_scale: Inverse scale transformation.

          (Optional) Function.

          Defaults to 2^x. This is equivalent to assuming that the data
          has been log2-transformed. If another transformation has been
          applied to the data then this function should be used to
          specify the inverse of that transformation needed to put gene
          expressions on the linear scale.

fit_scale: Transformation to used as part of optimization.

          (Optional) Function.

          Function to apply to gene expressions as part of
          optimization. Defaults to log.

loss_smry: Loss summary function minimized to find estimated
          proportions.

          (Optional) String.

          Either 'var' (default) to minimze the (weighted) variance of
          the residuals or 'L2' to minimize the (weighted) sums of
          squares of the residuals.

dtangle_init: Optimization initialization.

          (Optional) Boolean.

          Boolean controlling if dtangle2 optimization should be
          initialized using dtangle1 estimates.

    seed: (Optional) Integer.

          Value at which to seed the random seed before estimating.
          Optimization initialization might change if this value is not
          specified.

 verbose: (Optional) Boolean.

          Controls if optimization output is printed or not.

optim_opts: (Optional) List.

          Optimization options passed to DEoptimR controlling
          optimization. Options that may be set are

            • 'constr' constraint to enforce. Either 'box' for 0-1 box
              constraints that proportions are between zero and one,
              'ineq' for constraints that proportions sum to less than
              one, 'eq' for equality constraints that proportions sum
              to one, or 'eq_solve' to solve for one of the parameters
              in terms of the other and enfoce equality constraints
              using inequality on remaining parameters. Default and
              recommended is 'box'.

            • 'ninit' number of randomly initalized points as part of
              the DEoptimR initial population.

            • 'tritter' how often to print results if 'verbose=TRUE'.

            • 'maxiter' maximum number of optimization iterations to
              use before exiting.

            • 'convtol' tolerance for convergence tolerance stopping
              criterion.

            • 'constrtol' tolerance for constraint enforcement.

_V_a_l_u_e:

     List.

        • 'estimates' a matrix estimated mixing proportions. One row
          for each sample, one column for each cell type.

        • 'markers' list of vectors of marker used for each cell type.
          Each element of list is vector of columns of 'Y' used as a
          marker for the i-th cell type.

        • 'n_markers' vector of number of markers used for each cell
          type.

        • 'weights' the weights used as part of the optimization.

        • 'diag' diagnostic values for the estimated proportions.
          'resids_hat','loss_hat', and 'p_hat' are the residuals, loss,
          and estimates for the proportions returned by dtangle2.
          Similarly, 'resids_opt','loss_opt' and 'p_opt' are these
          values for the optimized value not re-scaled to enforce the
          STO constraint.

_S_e_e _A_l_s_o:

     'find_markers'

_E_x_a_m_p_l_e_s:

     truth = shen_orr_ex$annotation$mixture
     pure_samples <- lapply(1:3, function(i) {
        which(truth[, i] == 1)
     })
     Y <- shen_orr_ex$data$log
     n_markers = 20
     
     dtangle2(Y, pure_samples = pure_samples,
     n_markers=n_markers)
     

Killed
SUMMARY: processing the following file failed:
  ‘basic-deconvolution2.Rmd’

Error: Vignette re-building failed.
Execution halted