calculate_feature_CV {proBatch} | R Documentation |
Calculate CV distribution for each feature
calculate_feature_CV( df_long, sample_annotation = NULL, feature_id_col = "peptide_group_label", sample_id_col = "FullRunName", measure_col = "Intensity", batch_col = NULL, biospecimen_id_col = NULL, unlog = TRUE, log_base = 2, offset = 1 )
df_long |
data frame where each row is a single feature in a single
sample. It minimally has a |
sample_annotation |
data frame with:
.
See |
feature_id_col |
name of the column with feature/gene/peptide/protein
ID used in the long format representation |
sample_id_col |
name of the column in |
measure_col |
if |
batch_col |
column in |
biospecimen_id_col |
column in |
unlog |
(logical) whether to reverse log transformation of the original data |
log_base |
base of the logarithm for transformation |
offset |
small positive number to prevent 0 conversion to |
data frame with Total CV for each feature & (optionally) per-batch CV
CV_df = calculate_feature_CV(example_proteome, sample_annotation = example_sample_annotation, measure_col = 'Intensity', batch_col = 'MS_batch')