tcell              package:longitudinal              R Documentation

_M_i_c_r_o_a_r_r_a_y _T_i_m_e _S_e_r_i_e_s _D_a_t_a _f_o_r _T-_C_e_l_l _A_c_t_i_v_a_t_i_o_n

_D_e_s_c_r_i_p_t_i_o_n:

     The data result from two experiments investigating the expression
     response of human T cells to PMA and ionomicin treatment.

     The first data set ('tcell.34') contains the temporal expression
     levels of 58 genes for 10 unequally spaced time points.  At each
     time point there are 34  separate measurements. The second data
     set ('tcell.10') stems from a related experiment considering  the
     same genes and identical time points, and contains  10 further
     measurements per time point. See Rangel et al. (2004) for more
     details.

_U_s_a_g_e:

     data(tcell)

_F_o_r_m_a_t:

     'tcell.10' and 'tcell.34' are 'longitudinal' objects, i.e.
     matrices with 58 colums each and a number of extra attributes (see
     'longitudinal' and 'longitudinal.util').

     The vector 'tcell.gene.descriptions' contains the  description of
     the functions of the 58 investigated genes.

_S_o_u_r_c_e:

     This data is described in Rangel et al. (2004) and can be freely
     downloaded from  <URL: http://public.kgi.edu/~wild/LDS/index.htm>.
     The 'tcell' object contains the log-transformed and quantile
     normalized data (<URL:
     http://public.kgi.edu/~wild/LDS/Bioinformatics_data.xls>).

_R_e_f_e_r_e_n_c_e_s:

     Rangel, C., Angus, J., Ghahramani, Z., Lioumi, M., Sotheran, E.,
     Gaiba, A., Wild, D. L., and Falciani, F. (2004) Modeling T-cell
     activation using gene expression profiling and state-space models.
     _Bioinformatics_, *20*, 1361-1372.

_E_x_a_m_p_l_e_s:

     # load "longitudinal" library
     library("longitudinal")

     # load data sets
     data(tcell)

     # data set with 10 repeats 
     dim(tcell.10)
     summary(tcell.10)
     is.longitudinal(tcell.10)
     is.regularly.sampled(tcell.10)
     is.equally.spaced(tcell.10)
     get.time.repeats(tcell.10)

     # data set with 34 repeats 
     dim(tcell.34)
     summary(tcell.34)
     is.longitudinal(tcell.34)
     is.regularly.sampled(tcell.34)
     is.equally.spaced(tcell.34)
     get.time.repeats(tcell.34)

     # descriptions of the first nine genes
     tcell.gene.description[1:9]

     # plot the first nine time series
     plot(tcell.10, 1:9)
     plot(tcell.34, 1:9)

     # Rangel et al. use the combined data set
     tcell.44 <- combine.longitudinal(tcell.34, tcell.10)
     plot(tcell.44, 1:9)

