fit_drc_4p | R Documentation |
Function for fitting four-parameter dose response curves for each group (precursor, peptide or protein). In addition it can filter data based on completeness, the completeness distribution and statistical testing using ANOVA.
fit_drc_4p( data, sample, grouping, response, dose, filter = "post", replicate_completeness = 0.7, condition_completeness = 0.5, correlation_cutoff = 0.8, log_logarithmic = TRUE, include_models = FALSE, retain_columns = NULL )
data |
a data frame that contains at least the input variables. |
sample |
a character column in the |
grouping |
a character column in the |
response |
a numeric column in the |
dose |
a numeric column in the |
filter |
a character value that determines if models should be filtered and if they should
be filtered before or after the curve fits. Filtering of models can be skipped with
|
replicate_completeness |
a numeric value which similar to |
condition_completeness |
a numeric value which determines how many conditions need to at
least fulfill the "complete enough" criteria set with |
correlation_cutoff |
a numeric vector that specifies the correlation cutoff used for data filtering. |
log_logarithmic |
a logical value that indicates if a logarithmic or log-logarithmic model is fitted. If response values form a symmetric curve for non-log transformed dose values, a logarithmic model instead of a log-logarithmic model should be used. Usually biological dose response data has a log-logarithmic distribution, which is the reason this is the default. Log-logarithmic models are symmetric if dose values are log transformed. |
include_models |
a logical value that indicates if model fit objects should be exported. These are usually very large and not necessary for further analysis. |
retain_columns |
a vector that specifies columns that should be retained from the input
data frame. Default is not retaining additional columns |
If data filtering options are selected, data is filtered based on multiple criteria. In general, curves are only fitted if there are at least 5 conditions with data points present to ensure that there is potential for a good curve fit. Therefore, this is also the case if no filtering option is selected. Furthermore, a completeness cutoff is defined for filtering. By default each entity (e.g. precursor) is filtered to contain at least 70% of total replicates (adjusted downward) for at least 50% of all conditions (adjusted downward). This can be adjusted with the according arguments. In addition to the completeness cutoff, also a significance cutoff is applied. ANOVA is used to compute the statistical significance of the change for each entity. The resulting p-value is adjusted using the Benjamini-Hochberg method and a cutoff of q <= 0.05 is applied. Curve fits that have a minimal value that is higher than the maximal value are excluded as they were likely wrongly fitted. Curves with a correlation below 0.8 are not passing the filtering. If a fit does not fulfill the significance or completeness cutoff, it has a chance to still be considered if half of its values (+/-1 value) pass the replicate completeness criteria and half do not pass it. In order to fall into this category, the values that fulfill t he completeness cutoff and the ones that do not fulfill it need to be consecutive, meaning located next to each other based on their concentration values. Furthermore, the values that do not pass the completeness cutoff need to be lower in intensity. Lastly, the difference between the two groups is tested for statistical significance using a Welch's t-test and a cutoff of p <= 0.1 (we want to mainly discard curves that falsly fit the other criteria but that have clearly non-significant differences in mean). This allows curves to be considered that have missing values in half of their observations due to a decrease in intensity. It can be thought of as conditions that are missing not at random (MNAR). It is often the case that those entities do not have a significant p-value since half of their conditions are not considered due to data missingness.
The final filtered list is ranked based on a score calculated on entities that pass the filter.
The score is the negative log10 of the adjusted ANOVA p-value scaled between 0 and 1 and the
correlation scaled between 0 and 1 summed up and divided by 2. Thus, the highest score an
entity can have is 1 with both the highest correlation and adjusted p-value. The rank is
corresponding to this score. Please note, that entities with MNAR conditions might have a
lower score due to the missing or non-significant ANOVA p-value. You should have a look at
curves that are TRUE for dose_MNAR
in more detail.
If include_models = FALSE
a data frame is returned that contains correlations
of predicted to measured values as a measure of the goodness of the curve fit, an associated
p-value and the four parameters of the model for each group. Furthermore, input data for plots
is returned in the columns plot_curve
(curve and confidence interval) and plot_points
(measured points). If \ codeinclude_models = TURE, a list is returned that contains:
fit_objects
: The fit objects of type drc
for each group.
correlations
: The correlation data frame described above
# Load libraries library(dplyr) set.seed(123) # Makes example reproducible # Create example data data <- create_synthetic_data( n_proteins = 2, frac_change = 1, n_replicates = 3, n_conditions = 8, method = "dose_response", concentrations = c(0, 1, 10, 50, 100, 500, 1000, 5000), additional_metadata = FALSE ) # Perform dose response curve fit drc_fit <- fit_drc_4p( data = data, sample = sample, grouping = peptide, response = peptide_intensity_missing, dose = concentration, retain_columns = c(protein, change_peptide) ) glimpse(drc_fit) head(drc_fit, n = 10)
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