calcSpm               package:KCsmart               R Documentation

_K_C_s_m_a_r_t _w_r_a_p_p_e_r

_D_e_s_c_r_i_p_t_i_o_n:

     Wrapper function that calculates the sample point matrix from the
     aCGH data

_U_s_a_g_e:

     calcSpm(data, mirrorLocs, sigma = 1e+06, sampleDensity = 50000, maxmem = 1000)

_A_r_g_u_m_e_n_t_s:

    data: The aCGH data. Can either be in DNAcopy format or as a
          data.frame described in the details section 

mirrorLocs: List containing the chromosome start, centromere and end
          positions 

   sigma: The kernel width 

sampleDensity: The sample point matrix resolution 

  maxmem: This parameter controls memory usage, set to lower value to
          lower memory consumption 

_D_e_t_a_i_l_s:

     'data' can be in cghRaw (CGHbase), DNAcopy or in data.frame
     format. When using the latter, the data.frame must have the
     following two columns: 'chrom' stating the chromosome the probe is
     located on, 'maploc' describing the position on the chromosome of
     the probe. The remainder of the data.frame will be interpreted as
     sample data points. The row names of that data will be used as
     probe names (when available). Important note: the data can not
     contain any missing values. If your data includes missing values
     you will need to preprocess (for example impute) it using other
     software solutions.

     The mirror locations for Homo Sapiens and Mus Musculus are
     provided in the package. These can be loaded using
     data(hsMirrorLocs) and data(mmMirrorLocs)  respectively. The
     'mirrorLocs' object is a list with vectors containing the start,
     centromere (optional) and end of each chromosome as the list
     elements. Additionally it should  contain an attribute
     'chromNames' listing the chromosome names of each respective list
     element.

     'sigma' defines the kernel width of the kernel used to convolute
     the data.

     'sampleDensity' defines the resolution of the sample point matrix
     to be calculated. A sampleDensity of 50000 would correspond to a
     sample point every 50k base pairs.

_V_a_l_u_e:

     Returns a sample point matrix object. The object has several slots
     of which the 'data' slot contains a list where each list item
     represents a chromosome. Each list item in turn contains the
     sample point matrix for the gains and the losses separately and an
     attribute specifying the corresponding chromosome. The sample
     point matrix contains the following additional slots: totalLength:
     Total length of the sample point matrix maxy and miny: Maximal and
     minimal score attained

     The other slots just represent the parameters used to calculate
     the sample point matrix.

     Use 'plot' to plot the sample point matrix and 'findSigLevelTrad'
     to find a significance threshold. 'plotScaleSpace' can be used to
     plot the significant regions of multiple sample point matrices
     (using different sigmas).

_A_u_t_h_o_r(_s):

     Jorma de Ronde

_S_e_e _A_l_s_o:

     'plot',  'findSigLevelTrad', 'plotScaleSpace'

_E_x_a_m_p_l_e_s:

     data(hsSampleData)
     data(hsMirrorLocs)

     spm1mb <- calcSpm(hsSampleData, hsMirrorLocs)
     spm4mb <- calcSpm(hsSampleData, hsMirrorLocs, sigma=4000000)

     plot(spm1mb)
     plot(spm1mb, chromosomes=c(1,5,6,'X'))

