adjust_batch_trend {proBatch}R Documentation

adjust batch signal trend with the custom (continuous) fit

Description

adjust batch signal trend with the custom (continuous) fit

Usage

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, ...)

Arguments

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 sample_annotation that should be used for batch comparison

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 df_long is among the parameters, it is the column with expression/abundance/intensity, otherwise, it is used internally for consistency

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 fit_func

Value

list of two items: 1) data_matrix, adjusted with continious fit; 2) fit_df, used to examine the fitting curves

See Also

fit_nonlinear

Examples

trend_corrected_matrix <- adjust_batch_trend(example_proteome_matrix, 
example_sample_annotation, span = 0.7, 
abs_threshold = 5, pct_threshold = 0.20)


[Package proBatch version 1.0.0 Index]