Function reference • HTRfit

All functions

Area_Under_Curve(): Calculate the Area Under the Curve

ConfusionMatrix(): Confusion Matrix

accuracy(): accuracy

addBasalExpression(): Compute basal expresion for gene expression based on the coefficients data frame.

add_interaction(): Add interaction

already_init_variable(): Check if Variable is Already Initialized

anovaParallel(): Perform ANOVA on Multiple glmmTMB Models in Parallel

are_all_elements_identical(): Check if all elements in a list are identical.

averageByGroup(): Calculate average values by group

build_gg_pr_curve(): Builds a ggplot precision-recall curve.

build_gg_roc_curve(): Builds a ggplot ROC curve.

build_missingColumn_with_na(): Build DataFrame with Missing Columns and NA Values

build_sub_obj_return_to_user(): Build Sub Object to Return to User

calculate_actualMixed(): Calculate actual mixed effects.

calculate_actual_interactionX2_values(): Calculate actual interaction values between two terms in a data frame.

calculate_actual_interactionX3_values(): Calculate Actual Interaction Values for Three Fixed Effects

checkFractionOfZero(): Check Fraction of Zero or One in Counts Table

check_input2interaction(): Check input for interaction

clean_variable_name(): Clean Variable Name

combine_ground_truth(): Combine ground truth information from a list of mock objects.

combine_mock(): Combine multiple mock objects into a single mock object.

compareInferenceToExpected(): Compare inference results to expected values for a given model.

computeActualInteractionFixEff(): Compute actual interaction values for multiple interaction terms.

compute_covariation(): Compute Covariation from Correlation and Standard Deviations

compute_metrics_summary(): Compute summary metrics on classification results

compute_pr_auc(): Computes area under the precision-recall curve (AUC).

compute_pr_curve(): Computes the precision-recall curve (AUC).

compute_rmse(): Compute RMSE values on grouped data

compute_roc_auc(): Computes area under the ROC curve (AUC).

compute_roc_curve(): Computes the ROC curve.

compute_rsquare(): Compute R-squared values for linear regression on grouped data

convert2Factor(): Convert specified columns to factor

correlation_matrix_2df(): Convert Correlation Matrix to Data Frame

countMatrix_2longDtf(): Convert count matrix to long data frame

counts_plot(): Generate a density plot of gene counts

custom_matrix_transform(): Applies a custom function to a count matrix.

detect_categoricals_vars(): Detects categorical variables based on reference labels in a glmmTMB object.

detect_row_matx_bellow_threshold(): Detect rows in a matrix with all values below a given threshold

diagnostic_plot(): Plot Metrics for Generalized Linear Mixed Models (GLMM)

drop_randfx(): Drop Random Effects from a Formula

endsWithDigit(): Check if a string ends with a digit

eval_identityTerm(): Generate an identity term plot and get metrics associated

evaluation_report(): Compute evaluation report for TMB/DESeq2 analysis

export_dataframe(): Exports a dataframe to a specified file

export_eval_plots(): Exports evaluation plots from an eval_report object

export_evaluation_report(): Exports an eval_report object to a specified folder

extract_ddsDispersion(): Extract DESeq2 Dispersion Values

extract_fixed_effect(): Extract Fixed Effects from a GLMMTMB Model Summary

extract_ran_pars(): Extract Random Parameters from a glmmTMB Model

extract_tmbDispersion(): Extract TMB Dispersion Values

fillInCovarMatrice(): Fill in Covariance Matrix

fillInInteraction(): Fill in interaction

fillInVariable(): Fill in Variable

filter_dataframe(): Filter DataFrame

findAttribute(): Find Attribute

first_non_null_index(): Finds the index of the first non-null element in a list.

fitModel(): Fit a model using the fitModel function.

fitModelParallel(): Fit models in parallel for each group using mclapply and handle logging. Uses parallel_fit to fit the models.

fitUpdate(): Fit and update a GLMNB model.

generateActualForMainFixEff(): Generate actual values for a given term

generateActualInteractionX2_FixEff(): Generate actual values for the interaction fixed effect.

generateActualInteractionX3_FixEff(): Generate Actual Interaction Values for Three Fixed Effects

generateCountTable(): Generate count table

generateGridCombination_fromListVar(): Get grid combination from list_var

generateReplicationMatrix(): Generate replication matrix

generate_basal_expression(): Generate BE data.

getActualInteractionFixEff(): Get the actual interaction values for a given interaction term in the data.

getActualIntercept(): Get the intercept dataframe

getActualMainFixEff(): Get actual values for non-interaction terms

getActualMixed_type0(): Get Actual Mixed Type 0

getActualMixed_typeI(): Calculate actual mixed effect values for each gene.

getBinExpression(): Get bin expression for a data frame.

getCategoricalVar_inFixedEffect(): Get the categorical variable associated with the fixed effect in a type I formula.

getCoefficients(): getCoefficients

getColumnWithSampleID(): Get column name with sampleID

getCountsTable(): getCountsTable

getCovarianceMatrix(): getCovarianceMatrix

getData2computeActualFixEffect(): Get data for calculating actual values

getDataFromMvrnorm(): getDataFromMvrnorm

getDataFromRnorm(): Prepare data using effects from a normal distribution

getDataFromUser(): Get data from user

getDispersionComparison(): Get Dispersion Comparison

getDispersionMatrix(): getDispersionMatrix

getGeneMetadata(): getGeneMetadata

getGivenAttribute(): Get a given attribute from a list of variables

getGlance(): Extracts the summary statistics from a single glmmTMB model.

getGridCombination(): getGridCombination

getInput2mvrnorm(): getInput2mvrnorm

getInput2simulation(): Get input for simulation based on coefficients

getLabelExpected(): Get Labels for Expected Differential Expression

getLabels(): Get labels for variables

getListVar(): Get the list of variable names

getLog_qij(): Get the log_qij values from the coefficient data frame.

getMu_ij(): Calculate mu_ij values based on coefficient data frame and scaling factor

getMu_ij_matrix(): getMu_ij_matrix

getNumberOfCombinationsInInteraction(): Get the number of combinations in an interaction

getRefLevel(): Get the reference level for categorical variables in the data

getReplicationMatrix(): getReplicationMatrix

getSampleID(): getSampleID

getSampleMetadata(): Get sample metadata

getSettingsTable(): Get Setting Table

getStandardDeviationInCorrelation(): Get Standard Deviations for Variables in Correlation

getSubCountsTable(): getSubCountsTable

getTidyGlmmTMB(): Extract Tidy Summary of glmmTMB Model

getValidDispersion(): Validate and Filter Dispersion Values

get_actual_sd_2_replace(): Get actual sd values to replace based on the given formula and mock data.

get_effects_from_rnorm(): Generate effects from a normal distribution

get_eval_data(): Gets evaluation data from both TMB and DESeqDataSet (dds) objects.

get_eval_data_from_dds(): Extracts evaluation data from a DESeqDataSet (dds) object.

get_eval_data_from_ltmb(): Extracts evaluation data from a list of TMB models.

get_inference_dds(): Calculate Inference for Differential Expression Analysis

get_label_y_position(): Computes y-axis position for text labels.

get_list_of_mock_attribute(): Get a list of specified attributes from a list of mock objects.

get_mad_left_threshold(): Calculate the left threshold for MAD-based filtering.

get_mad_user_message(): Generate user message for MAD filtering.

get_messages_sequencing_depth(): Get messages related to sequencing depth

get_metrics_2plot(): Gets R-squared values for plotting.

get_ml_metrics_obj(): Get classification metrics for evaluation object

get_performances_metrics_obj(): Compute classification and regression performance metrics object

get_pr_curve(): Gets precision-recall curves and AUC for both aggregated and individual parameters.

get_pr_object(): Gets precision-recall objects for a given parameter.

get_roc_curve(): Gets ROC curves and AUC for both aggregated and individual parameters.

get_roc_object(): Gets ROC objects for a given parameter.

get_scaling_factor(): Get scaling factor for count normalization

get_violin_random_params(): Generate a Violin Plot of Random Parameters

glance_tmb(): Extracts the summary statistics from a list of glmmTMB models.

group_logQij_per_genes_and_labels(): Group log_qij values per genes and labels.

handleAnovaError(): Handle ANOVA Errors

identifyTopFit(): Identify top or low fitting observations based on specified diagnostic metric and filtering method.

inferenceToExpected_withFixedEff(): Compare the results of inference with the ground truth data.

inferenceToExpected_withMixedEff(): Compare the mixed-effects inference to expected values.

init_variable(): Initialize variable

inputs_checking(): Check Input Parameters

isValidEvalInput(): Validate input parameters for evaluation

isValidEval_report(): Checks if an eval_report object is valid

isValidExpression(): Validates a custom expression.

isValidGlmmTmb(): Check if a glmmTMB object is valid

isValidInput2fit(): Check if Data is Valid for Model Fitting

isValidList_tmb(): Check if a list of glmmTMB objects is valid

isValidMock_obj(): Checks if an object corresponds to a mock object generated by mock_rnaseq().

is_dispersionMatrixValid(): Check the validity of the dispersion matrix

is_formula_mixedType0(): Check if the formula follows a specific type I mixed effect structure.

is_formula_mixedTypeI(): Check if the formula follows a specific type I mixed effect structure.

is_fullrank(): Check if a Model Matrix is Full Rank

is_mixedEffect_inFormula(): Check if the formula contains a mixed effect structure.

is_positive_definite(): Check if a matrix is positive definite This function checks whether a given matrix is positive definite, i.e., all of its eigenvalues are positive.

is_truthLabels_valid(): Check Validity of Truth Labels

is_validGroupBy(): Check if group by exist in data

join_dtf(): Join two data frames using data.table

launchFit(): Launch the model fitting process for a specific group.

launchUpdate(): Launch the update process for a GLMNB model.

medianRatioNormalization(): Apply Median Ratio Normalization to a Counts Matrix

mock_rnaseq(): Perform RNA-seq simulation

parallel_fit(): Fit models in parallel for each group using mclapply and handle logging. Uses parallel_fit to fit the models.

parallel_update(): Internal function to fit glmmTMB models in parallel.

performance-class: Class performance

performance(): Function to create performance objects

precision(): precision

prediction-class: Class prediction

prediction(): Function to create prediction objects

prepareData2computeInteraction(): Prepare data for computing interaction values.

prepareData2fit(): Prepares data for fitting.

prepare_dataParallel(): Fit the model based using fitModel functions.

rbind_evaldata_tmb_dds(): Combines evaluation data from TMB and DESeqDataSet (dds) objects.

rbind_model_params_and_dispersion(): Combines model parameters and dispersion data frames.

recall(): recall

relevel_list_tmb_frame(): Re-levels categorical variables in a the frame of a list of glmmTMB objects.

relevelling_factors(): Relevels factors in a list of glmmTMB objects using specified reference labels.

removeDigitsAtEnd(): Remove digits at the end of a string

removeDuplicatedWord(): Remove Duplicated Words from Strings

renameColumns(): Rename Columns in a Data Frame

rename_genes_in_mock_obj(): Rename genes in a mock object by adding a specific index.

reorderColumns(): Reorder the columns of a dataframe

replaceUnexpectedInteractionValuesBy0(): Replace the effect by 0 in the data

replace_actual(): Replace actual values in params_df based on specified index and list of actual values.

replicateByGroup(): Replicate rows of a data frame by group

replicateMatrix(): Replicate matrix

replicateRows(): Replicate rows of a data frame

rmse(): Root Mean Squared Error (RMSE)

samplingFromMvrnorm(): getDataFromMvrnorm

scaleCountsTable(): Scale Counts Table

sensitivity(): sensitivity

set_correlation(): Set Correlation between Variables

specificity(): specificity

subsetByTermLabel(): subset data By Term Label

subsetFixEffectInferred(): Subset Fixed Effect Inferred Terms

subsetGenes(): Subset Genes in Genomic Data

subset_glance(): Subset the glance DataFrame based on selected variables.

theme_htrfit(): Custom Theme for htrfit Plots

tidy_results(): Perform statistical tests and return tidy results

tidy_tmb(): Extract Tidy Summary of Multiple glmmTMB Models

trimmedMeanMvaluesNormalization(): Normalize count data using Trimmed Mean of M-values (TMM) method

updateParallel(): Update glmmTMB models in parallel.

wald_test(): Wald test for hypothesis testing - new implementation (v2.1.1) adapted from the DESeq2 package.

warning_too_low_mu_ij_row(): Emit a warning message for rows with low mu_ij values

wrap_dds(): Wrapper Function for DESeq2 Analysis

wrapper_var_cor(): Wrapper for Extracting Variance-Covariance Components