giaever               package:GraphAT               R Documentation

_Y_e_a_s_t _G_e_n_e-_K_n_o_c_k_o_u_t _F_i_t_n_e_s_s _D_a_t_a

_D_e_s_c_r_i_p_t_i_o_n:

     This data set contains fitness deficiency scores from gene
     knockout experiments involving yeast grown under a variety of
     altered environments (e.g. acid, heat, sorbitol, etc.)

_U_s_a_g_e:

     data(giaever)

_F_o_r_m_a_t:

     A matrix whose rows are the 5922 genes knocked out and whose
     columns are the 32 experimental conditions.

_S_o_u_r_c_e:

     <URL: http://gobi.lbl.gov/YeastFitnessData>

_R_e_f_e_r_e_n_c_e_s:

     Giaever, G. et al. 2002 ``Functional profiling of the
     Saccharomyces cerevisiae genome.'' Nature *418*, 387-391.

_E_x_a_m_p_l_e_s:

     data(giaever)

     ## Find the 3000 most variable genes, according to sd/mean:
     varMeas <- function(vec, na.rm=TRUE) 
     {
         if(na.rm)
           vec <- vec[!is.na(vec)]
         if(length(vec) == 0)
           measure <- NA
         else
           measure <- sd(vec)/mean(vec)
         return(measure)
     }

     variability <- apply(giaever, 1, varMeas)

     rks <- rank(variability)

     giaever3000 <- giaever[rks>length(rownames(giaever))-3000,]

