calcTStatFast           package:sigPathway           R Documentation

_C_o_m_p_u_t_e _T-_S_t_a_t_i_s_t_i_c_s _a_n_d _C_o_r_r_e_s_p_o_n_d_i_n_g _P-_V_a_l_u_e_s

_D_e_s_c_r_i_p_t_i_o_n:

     Computes t-statistics and corresponding p-values.

_U_s_a_g_e:

     calcTStatFast(tab, phenotype, ngroups = 2)

_A_r_g_u_m_e_n_t_s:

     tab: a numeric matrix of expression values, with the rows and
          columns representing probe sets and sample arrays,
          respectively

phenotype: a numeric (or character if 'ngroups' >= 2) vector indicating
          the phenotype

 ngroups: an integer indicating the number of groups in the expression
          matrix

_D_e_t_a_i_l_s:

     If there are two groups in the matrix, it is recommended to use 0
     and 1 to denote which sample columns belong to which group.  If
     the phenotype is a character vector, then the phenotype ranked
     first in the alphabet is considered as 0.

     If 'ngroups' = 2, the t-test done here is equivalent to a unpaired
     two-sample t-test, assuming unequal variances.  Please note that
     as of version 1.1.6, the sign of the t-statistic is positive when
     the mean of group 1 is greater than the mean of group 0.

     If there is only one group in the matrix (e.g., Alzheimer's data
     set as reanalyzed in Tian et al. (2005)), then the phenotype
     vector should consist of continuous values.  In this case, the
     association between phenotype and expression values is first
     calculated as Pearson correlation coefficients, transformed to
     Fisher's z, and then rescaled so that its variance is 1:

     z = 0.5*log((1+rho)/(1-rho))*sqrt(n-3), where n is the number of
     phenotypes.

     If 'ngroups' > 2, the f-statistics (from 1-way ANOVA) are
     calculated.  The user will need to check that the data have
     similar variances among the groups.

_V_a_l_u_e:

    pval: A vector of unadjusted p-values

   tstat: A vector of t-statistics ('ngroups' = 2) or rescaled Fisher's
          z ('ngroups' = 1)

     rho: (Also returned when 'ngroups' = 1) A vector of Pearson
          correlation coefficients

_A_u_t_h_o_r(_s):

     Weil Lai

_E_x_a_m_p_l_e_s:

     ## Load inflammatory myopathy data set
     data(MuscleExample)
     statList <- calcTStatFast(tab, phenotype, ngroups = 2)

     ## Generate histogram of p-values
     hist(statList$pval, xlab = "Unadjusted p-values", ylab = "Frequency")

