View source: R/peptideLevel_DE.R
plot_3_pep_trends_NOfile | R Documentation |
Plot Raw, Normalized and Normalized & Imputed peptide trends for a protein
plot_3_pep_trends_NOfile( mm, prot.info, sorted_norm_m, sorted_prot.info, imp_mm, imp_prot.info, prot_to_plot, prot_to_plot_col, gene_name, gene_name_col, mylabs )
mm |
matrix of raw intensities |
prot.info |
metadata for the intensities in mm |
sorted_norm_m |
normalized intensities, possibly fewer than in mm due to filtering out peptides with fewer than one observation per treatment group |
sorted_prot.info |
metadata for the intensities in sorted_norm_m |
imp_mm |
imputed intensities (post normalization) |
imp_prot.info |
metadata for the imputed intensities in imp_mm |
prot_to_plot |
protein ID to plot |
prot_to_plot_col |
protein ID column index |
gene_name |
gene ID to plot |
gene_name_col |
gene ID to plot column index |
mylabs |
sample labels to be plotted on x-axis |
Nil
data("hs_peptides") # loads variable hs_peptides intsCols = 8:13 # column indeces that contain intensities m_logInts = make_intencities(hs_peptides, intsCols) # replace 0's with NA's as NA's are more appropriate # for anlysis and log2 transform m_logInts = convert_log2(m_logInts) # column indices that contain metadata such as protein IDs and sequences metaCols = 1:7 m_prot.info = make_meta(hs_peptides, metaCols) grps = as.factor(c('CG','CG','CG', 'mCG','mCG','mCG')) set.seed(135) hs_m_ints_eig1 = eig_norm1(m=m_logInts,treatment=grps,prot.info=m_prot.info) hs_m_ints_eig1$h.c = 2 # looks like there are 2 bias trends at least hs_m_ints_norm = eig_norm2(rv=hs_m_ints_eig1) hs_prot.info = hs_m_ints_norm$normalized[,metaCols] hs_norm_m = hs_m_ints_norm$normalized[,intsCols] set.seed(125) imp_hs = MBimpute(hs_norm_m, grps, prot.info=hs_prot.info, pr_ppos=3, my.pi=0.05, compute_pi=FALSE) mylabs = c( 'CG','CG','CG', 'mCG','mCG','mCG') prot_to_plot = 'Prot32' # 43 gene_to_plot = 'Gene32' plot_3_pep_trends_NOfile(as.matrix(hs_m_ints_eig1$m), hs_m_ints_eig1$prot.info, as.matrix(hs_norm_m), hs_prot.info, imp_hs$y_imputed, imp_hs$imp_prot.info, prot_to_plot, 3, gene_to_plot, 4, mylabs)
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