Normalize consumption release data
Arguments
- data
DF which contains unique sample identifiers as row names and metabolite numerical values in columns with metabolite identifiers as column names. Use NA for metabolites that were not detected and consider converting any zeros to NA unless they are true zeros.
- metadata_sample
DF which contains information about the samples, which will be combined with the input data based on the unique sample identifiers used as rownames.
- metadata_info
Named vector containing the information about the names of the experimental parameters. c(Conditions="ColumnName_Plot_SettingsFile", core_norm_factor = "Columnname_Input_SettingsFile", core_media = "Columnname_Input_SettingsFile"). Column core_norm_factor is used for normalization and core_media is used to specify the name of the media controls in the Conditions.
Value
List with two elements: DF (including output table) and Plot (including all plots generated)
This function is intended for standalone CoRe-style experiments with
control-media metadata available via metadata_info.
Examples
data(medium_raw)
Media <-
medium_raw %>%
tibble::column_to_rownames("Code") %>%
subset(!Conditions == "Pool") %>%
dplyr::mutate_all(~ ifelse(grepl("^0*(\\.0*)?$", as.character(.)), NA, .))
Res <- core_norm(
data = Media[, -c(1:3)],
metadata_sample = Media[, c(1:3)],
metadata_info = c(
Conditions = "Conditions",
core_norm_factor = "GrowthFactor",
core_media = "blank"
)
)
#> For Consumption Release experiment we are using the method from Jain M. REF: Jain et. al, (2012), Science 336(6084):1040-4, doi: 10.1126/science.1218595.
#> 9 of variables have high variability (CV > 30) in the core_media control samples. Consider checking the pooled samples to decide whether to remove these metabolites or not.
#> `stat_bin()` using `bins = 30`. Pick better value `binwidth`.
#> Bin width defaults to 1/30 of the range of the data. Pick better value with
#> `binwidth`.
#> Warning: The core_media samples MS51-04 were found to be different from the rest. They will not be included in the sum of the core_media samples.
#> core data are normalised by substracting mean (blank) from each sample and multiplying with the core_norm_factor