dea | R Documentation |
Differential expression analysis
dea(
dataset,
qval_signif = 0.01,
fc_signif = 0,
dea_algorithm = "deqms",
rollup_algorithm = "maxlfq",
output_dir_for_eset = ""
)
dataset |
your dataset |
qval_signif |
threshold for significance of q-values |
fc_signif |
threshold for significance of log2 foldchanges. Set to zero or NA to disregard, or a positive value to apply a cutoff to absolute log2 foldchanges. MS-DAP can also perform a bootstrap analyses to infer a reasonable threshold by setting this parameter to NA |
dea_algorithm |
algorithm for differential expression analysis (provide an array of strings to run multiple, in parallel). Refer to |
rollup_algorithm |
algorithm for combining peptides to proteins as used in DEA algorithms that require a priori rollup from peptides to a protein-level abundance matrix before applying statistics (e.g. ebayes, deqms). Refer to |
output_dir_for_eset |
optionally, provide an output directory where the expressionset objects should be stored. Only useful if you're doing downstream data analysis that requires this data |
long-format tibble with results for each DEA algorithm requested via dea_algorithm
parameter. Note that a MS-DAP contrast for "A vs B" returns foldchanges for B/A. For example, for the contrast "control vs disease" a positive log2 foldchange implies protein abundances are higher in the "disease" sample group. The column signif
contains a boolean flag indicating significant hits according to both the user defined q-value threshold (parameter qval_signif
, also in result table column signif_threshold_qvalue
) and optional foldchange threshold (parameter fc_signif
, also in result table column signif_threshold_log2fc
)
dea_algorithms()
for available DEA algorithms and documentation.
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