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* checking for file ‘MetaboCoreUtils/DESCRIPTION’ ... OK
* preparing ‘MetaboCoreUtils’:
* checking DESCRIPTION meta-information ... OK
* installing the package to build vignettes
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quality_assessment       package:MetaboCoreUtils       R Documentation

_B_a_s_i_c _q_u_a_l_i_t_y _a_s_s_e_s_s_m_e_n_t _f_u_n_c_t_i_o_n_s _f_o_r _m_e_t_a_b_o_l_o_m_i_c_s

_D_e_s_c_r_i_p_t_i_o_n:

     The following functions allow to calculate basic quality
     assessment estimates typically employed in the analysis of
     metabolomics data. These functions are designed to be applied to
     entire rows of data, where each row corresponds to a feature.
     Subsequently, these estimates can serve as a foundation for
     feature filtering.

        • 'rsd' and 'rowRsd' are convenience functions to calculate the
          relative standard deviation (i.e. coefficient of variation)
          of a numerical vector or for rows of a numerical matrix,
          respectively.

        • 'rowDratio' computes the D-ratio or _dispersion ratio_,
          defined as the standard deviation for QC (Quality Control)
          samples divided by the standard deviation for biological test
          samples, for each feature (row) in the matrix.

        • 'percentMissing' and 'rowPercentMissing' determine the
          percentage of missing values in a vector or for each row of a
          matrix, respectively.

        • 'rowBlank' identifies rows (i.e., features) where the mean of
          test samples is lower than a specified multiple (defined by
          the 'threshold' parameter) of the mean of blank samples. This
          can be used to flag features that result from contamination
          in the solvent of the samples.

     These functions are based on standard filtering methods described
     in the literature, and they are implemented to assist in
     preprocessing metabolomics data.

_U_s_a_g_e:

     rsd(x, na.rm = TRUE, mad = FALSE)
     
     rowRsd(x, na.rm = TRUE, mad = FALSE)
     
     rowDratio(x, y, na.rm = TRUE, mad = FALSE)
     
     percentMissing(x)
     
     rowPercentMissing(x)
     
     rowBlank(x, y, threshold = 2, na.rm = TRUE)
     
_A_r_g_u_m_e_n_t_s:

       x: 'numeric' For 'rsd', a numeric vector; for 'rowRsd',
          'rowDratio', 'percentMissing' and 'rowBlank', a numeric
          matrix representing the biological samples.

   na.rm: 'logical(1)' indicates whether missing values ('NA') should
          be removed prior to the calculations.

     mad: 'logical(1)' indicates whether the _Median Absolute
          Deviation_ (MAD) should be used instead of the standard
          deviation. This is suggested for non-gaussian distributed
          data.

       y: 'numeric' For 'rowDratio' and 'rowBlank', a numeric matrix
          representing feature abundances in QC samples or blank
          samples, respectively.

threshold: 'numeric' For 'rowBlank', indicates the minimum difference
          required between the mean of a feature in samples compared to
          the mean of the same feature in blanks for it to not be
          considered a possible contaminant. For example, the default
          threshold of 2 signifies that the mean of the features in
          samples has to be at least twice the mean in blanks for it
          not to be flagged as a possible contaminant.

_V_a_l_u_e:

     See individual function description above for details.

_N_o_t_e:

     For 'rsd' and 'rowRsd' the feature abundances are expected to be
     provided in natural scale and not e.g. log2 scale as it may lead
     to incorrect interpretations.

_A_u_t_h_o_r(_s):

     Philippine Louail, Johannes Rainer

_R_e_f_e_r_e_n_c_e_s:

     Broadhurst D, Goodacre R, Reinke SN, Kuligowski J, Wilson ID,
     Lewis MR, Dunn WB. Guidelines and considerations for the use of
     system suitability and quality control samples in mass
     spectrometry assays applied in untargeted clinical metabolomic
     studies. Metabolomics. 2018;14(6):72. doi:
     10.1007/s11306-018-1367-3. Epub 2018 May 18. PMID: 29805336;
     PMCID: PMC5960010.

_E_x_a_m_p_l_e_s:

     ## coefficient of variation
     a <- c(4.3, 4.5, 3.6, 5.3)
     rsd(a)
     
     A <- rbind(a, a, a)
     rowRsd(A)
     
     ## Dratio
     x <- c(4.3, 4.5, 3.6, 5.3)
     X <- rbind(a, a, a)
     rowDratio(X, X)
     
     #' ## Percent Missing
     b <- c(1, NA, 3, 4, NA)
     percentMissing(b)
     
     B <- matrix(c(1, 2, 3, NA, 5, 6, 7, 8, 9), nrow = 3)
     rowPercentMissing(B)
     
     ## Blank Rows
     test_samples <- matrix(c(13, 21, 3, 4, 5, 6), nrow = 2)
     blank_samples <- matrix(c(0, 1, 2, 3, 4, 5), nrow = 2)
     rowBlank(test_samples, blank_samples)
     

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