\donttest{
# ===================================
# Significance tests
# ===================================
## !!!require the brief working example in `?load_expts`
## global option
scale_log2r <- TRUE
## peptides (`Term` etc. are user-defined column keys in expt_smry.xlsx)
pepSig(
impute_na = FALSE,
W2_bat = ~ Term["W2.BI.TMT2-W2.BI.TMT1",
"W2.JHU.TMT2-W2.JHU.TMT1",
"W2.PNNL.TMT2-W2.PNNL.TMT1"],
W2_loc = ~ Term_2["W2.BI-W2.JHU",
"W2.BI-W2.PNNL",
"W2.JHU-W2.PNNL"],
W16_vs_W2 = ~ Term_3["W16-W2"],
)
pepVol()
## proteins (formulae matched to `pepSig` by default)
prnSig(impute_na = FALSE)
prnVol()
# note the incongruity in peptide and protein fold changes
# (no measures for peptides but for proteins)
# sequence | ref | sample_1 | sample_2 | log2FC
# -------------------------------------------------------
# prnX_pep1 | 0 | 1.15 | NA | NA
# prnX_pep2 | 0 | NA | 0.05 | NA
# protein | ref | sample_1 | sample_2 | log2FC
# -------------------------------------------------------
# prnX | 0 | 1.15 | 0.05 | 1.10
## averaged batch effect
# (suggest run both `pepSig` and `prnSig` for consistency)
pepSig(
impute_na = FALSE,
W2_loc_2 = ~ Term["(W2.BI.TMT2+W2.BI.TMT1)/2 - (W2.JHU.TMT2+W2.JHU.TMT1)/2"],
)
prnSig(impute_na = FALSE)
pepVol()
prnVol()
## random effects
# NA imputation (suggested for models with random effects)
pepImp(m = 2, maxit = 2)
prnImp(m = 5, maxit = 5)
# single
pepSig(
impute_na = TRUE,
W2_vs_W16_fix = ~ Term_3["W16-W2"],
W2_vs_W16_mix = ~ Term_3["W16-W2"] + (1|TMT_Set),
)
prnSig(impute_na = TRUE)
pepVol()
prnVol()
# one to multiple (method `lm` for multiple random)
pepSig(
impute_na = TRUE,
method = lm,
W2_vs_W16_fix = ~ Term_3["W16-W2"],
W2_vs_W16_mix = ~ Term_3["W16-W2"] + (1|TMT_Set),
W2_vs_W16_mix_2 = ~ Term_3["W16-W2"] + (1|TMT_Set) + (1|Color),
)
prnSig(
impute_na = TRUE,
method = lm,
)
pepVol()
prnVol()
}
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