lmscFit                package:limma                R Documentation

_F_i_t _L_i_n_e_a_r _M_o_d_e_l _t_o _I_n_d_i_v_i_d_u_a_l _C_h_a_n_n_e_l_s _o_f _T_w_o-_C_o_l_o_r _D_a_t_a

_D_e_s_c_r_i_p_t_i_o_n:

     Fit a linear model to the individual log-intensities for each gene
     given a series of two-color arrays

_U_s_a_g_e:

     lmscFit(object, design, correlation)

_A_r_g_u_m_e_n_t_s:

  object: an 'MAList' object or a list from which 'M' and 'A' values
          may be extracted

  design: a numeric matrix containing the design matrix for linear
          model in terms of the individual channels. The number of rows
          should be twice the number of arrays. The number of columns
          will determine the number of coefficients estimated for each
          gene.

correlation: numeric value giving the intra-spot correlation

_D_e_t_a_i_l_s:

     For two color arrays, the channels measured on the same set of
     arrays are correlated. The 'M' and 'A' however are uncorrelated
     for each gene. This function fits a linear model to the set of M
     and A-values for each gene after re-scaling the M and A-values to
     have equal variances. The input correlation determines the scaling
     required. The input correlation is usually estimated using
     'intraspotCorrelation' before using 'lmscFit'.

     Missing values in 'M' or 'A' are not allowed.

_V_a_l_u_e:

     An object of class 'MArrayLM'

_A_u_t_h_o_r(_s):

     Gordon Smyth

_R_e_f_e_r_e_n_c_e_s:

     Smyth, G. K. (2005). Individual channel analysis of two-colour
     microarray data. Invited Session IPM 11: Computational Tools For
     Microarray Analysis, 55th Session of the International Statistics
     Institute, Sydney, 12 April 2005. (Four-page paper distributed on
     CD at the conference.)

_S_e_e _A_l_s_o:

     'lm.fit'.

     An overview of methods for single channel analysis in limma is
     given by 07.SingleChannel.

_E_x_a_m_p_l_e_s:

     library(sma)
     # Subset of data from ApoAI case study in Limma User's Guide
     data(MouseArray)
     # Avoid non-positive intensities
     RG <- backgroundCorrect(mouse.data,method="normexp")
     MA <- normalizeWithinArrays(RG,mouse.setup)
     MA <- normalizeBetweenArrays(MA,method="Aq")
     # Randomly choose 500 genes for this example
     i <- sample(1:nrow(MA),500)
     MA <- MA[i,]
     targets <- data.frame(Cy3=I(rep("Pool",6)),Cy5=I(c("WT","WT","WT","KO","KO","KO")))
     targets.sc <- targetsA2C(targets)
     targets.sc$Target <- factor(targets.sc$Target,levels=c("Pool","WT","KO"))
     design <- model.matrix(~Target,data=targets.sc)
     corfit <- intraspotCorrelation(MA,design)
     fit <- lmscFit(MA,design,correlation=corfit$consensus)
     cont.matrix <- cbind(KOvsWT=c(0,-1,1))
     fit2 <- contrasts.fit(fit,cont.matrix)
     fit2 <- eBayes(fit2)
     topTable(fit2,adjust="fdr")

