lumiR.batch               package:lumi               R Documentation

_R_e_a_d _B_e_a_d_S_t_u_d_i_o _o_u_t_p_u_t _f_i_l_e_s _i_n _b_a_t_c_h

_D_e_s_c_r_i_p_t_i_o_n:

     Read BeadStudio output files in batch and combine them as a single
     LumiBatch object

_U_s_a_g_e:

     lumiR.batch(fileList, convertNuID = TRUE, lib.mapping = NULL, detectionTh = 0.01, QC = TRUE, transform = c('none', 'vst', 'log2', 'cubicRoot'), sampleInfoFile = NULL, verbose = TRUE, ...)

_A_r_g_u_m_e_n_t_s:

fileList: a vector of file names or a directory keeping the data files
          in the format of .csv 

convertNuID: determine whether convert the probe identifier as nuID 

lib.mapping: same as 'lumiR' parameter lib.mapping (optional) 

detectionTh: the p-value threshold of determining detectability of the
          expression. See more details in 'lumiQ'  

      QC: determine whether to do quality control assessment after read
          in the data.

transform: determine whether to do transform after input each file 

sampleInfoFile: a Tab-separated text file or a data.frame keeping the
          sample information (optional) 

 verbose: a boolean to decide whether to print out some messages 

     ...: other parameters used by 'lumiR' 

_D_e_t_a_i_l_s:

     The function basically call lumiR for individual files and then
     combine the returns. The sampleInfoFile parameter is optional. It
     provides the sample information (for phenoData slot in LumiBatch
     object), it is a Tab-separated text file. ID column is required.
     It represents sample ID, which is defined based on the column
     names of BeadStudio output file. For example, sample ID of column
     "1881436070_A_STA.AVG_Signal" is "1881436070_A_STA". The sample ID
     column can also be found in the "Samples Table.txt" file output by
     BeadStudio. Another "Label" column (if provided) will be used as
     the sampleNames of LumiBatch object. All information of
     sampleInfoFile will be directly added in the phenoData slot in
     LumiBatch object.

     To save memory space in the case of reading large data set, we can
     do transformation using lumiT function right after input the data,
     and the information like se.exprs, beadNum will be removed from
     the LumiBatch object after transformation.

_V_a_l_u_e:

     A LumiBatch object which combines the individual LumiBatch object
     corresponding to each file

_A_u_t_h_o_r(_s):

     Pan Du

_S_e_e _A_l_s_o:

     'lumiR'

_E_x_a_m_p_l_e_s:

     ## fileList <- c('file1.csv', 'file2.cvs')
     ## x.lumi <- lumiR.batch(fileList, sampleInfoFile='sampleInfo.txt')

