adjust_batch_trend {proBatch} | R Documentation |
adjust batch signal trend with the custom (continuous) fit
adjust_batch_trend(data_matrix, sample_annotation, batch_col = "MS_batch", feature_id_col = "peptide_group_label", sample_id_col = "FullRunName", measure_col = "Intensity", sample_order_col = "order", fit_func = fit_nonlinear, abs_threshold = 5, pct_threshold = 0.2, ...)
data_matrix |
features (in rows) vs samples (in columns) matrix, with feature IDs in rownames and file/sample names as colnames. Usually the log transformed version of the original data |
sample_annotation |
data frame with sample ID, technical (e.g. MS batches) and biological (e.g. Diet) covariates |
batch_col |
column in |
feature_id_col |
name of the column with feature/gene/peptide/protein ID used in the long format representation df_long. In the wide formatted representation data_matrix this corresponds to the row names. |
sample_id_col |
name of the column in sample_annotation file, where the filenames (colnames of the data matrix are found) |
measure_col |
if |
sample_order_col |
column, determining the order of sample MS run, used as covariate to fit the non-linear fit |
fit_func |
function to fit the (non)-linear trend |
abs_threshold |
the absolute threshold to filter data for curve fitting |
pct_threshold |
the percentage threshold to filter data for curve fitting |
... |
other parameters, usually those of the |
list of two items: 1) data_matrix
, adjusted with continious fit;
2) fit_df, used to examine the fitting curves
trend_corrected_matrix <- adjust_batch_trend(example_proteome_matrix, example_sample_annotation, span = 0.7, abs_threshold = 5, pct_threshold = 0.20)