# Analyze High-dimensional Cytometry Data Using Tidy Data Principles

## Documentation for package ‘tidytof’ version 1.3.0

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

A C D G L M N P R T W

## -- A --

as_flowFrame | Coerce an object into a 'flowFrame'
as_flowFrame.tof_tbl | Coerce an object into a 'flowFrame'
as_flowSet | Coerce an object into a 'flowSet'
as_flowSet.tof_tbl | Coerce an object into a 'flowSet'
as_seurat | Coerce an object into a 'SeuratObject'
as_seurat.tof_tbl | Coerce an object into a 'SeuratObject'
as_SingleCellExperiment | Coerce an object into a 'SingleCellExperiment'
as_SingleCellExperiment.tof_tbl | Coerce an object into a 'SingleCellExperiment'
as_tof_tbl | Coerce flowFrames or flowSets into tof_tbl's.
as_tof_tbl.flowSet | Convert an object into a tof_tbl

## -- C --

cosine_similarity | Find the cosine similarity between two vectors

## -- D --

ddpr_data | CyTOF data from two samples: 5,000 B-cell lineage cells from a healthy patient and 5,000 B-cell lineage cells from a B-cell precursor Acute Lymphoblastic Leukemia (BCP-ALL) patient.
ddpr_metadata | Clinical metadata for each patient sample in Good & Sarno et al. (2018).
dot | Find the dot product between two vectors.

## -- G --

get_extension | Find the extension for a file

## -- L --

l2_normalize | L2 normalize an input vector x to a length of 1

## -- M --

magnitude | Find the magnitude of a vector.
make_flowcore_annotated_data_frame | Make the AnnotatedDataFrame needed for the flowFrame class
metal_masterlist | A character vector of metal name patterns supported by tidytof.

## -- N --

new_tof_model | Constructor for a tof_model.
new_tof_tibble | Constructor for a tof_tibble.

## -- P --

phenograph_data | CyTOF data from 6,000 healthy immune cells from a single patient.

## -- R --

rev_asinh | Reverses arcsinh transformation with cofactor 'scale_factor' and a shift of 'shift_factor'.

## -- T --

tidytof_example_data | Get paths to tidytof example data
tof_analyze_abundance | Perform Differential Abundance Analysis (DAA) on high-dimensional cytometry data
tof_analyze_abundance_diffcyt | Differential Abundance Analysis (DAA) with diffcyt
tof_analyze_abundance_glmm | Differential Abundance Analysis (DAA) with generalized linear mixed-models (GLMMs)
tof_analyze_abundance_ttest | Differential Abundance Analysis (DAA) with t-tests
tof_analyze_expression | Perform Differential Expression Analysis (DEA) on high-dimensional cytometry data
tof_analyze_expression_diffcyt | Differential Expression Analysis (DEA) with diffcyt
tof_analyze_expression_lmm | Differential Expression Analysis (DEA) with linear mixed-models (LMMs)
tof_analyze_expression_ttest | Differential Expression Analysis (DEA) with t-tests
tof_annotate_clusters | Manually annotate tidytof-computed clusters using user-specified labels
tof_apply_classifier | Perform developmental clustering on CyTOF data using a pre-fit classifier
tof_assess_channels | Detect low-expression (i.e. potentially failed) channels in high-dimensional cytometry data
tof_assess_clusters_distance | Assess a clustering result by calculating the z-score of each cell's mahalanobis distance to its cluster centroid and flagging outliers.
tof_assess_clusters_entropy | Assess a clustering result by calculating the shannon entropy of each cell's mahalanobis distance to all cluster centroids and flagging outliers.
tof_assess_clusters_knn | Assess a clustering result by calculating a cell's cluster assignment to that of its K nearest neighbors.
tof_assess_flow_rate | Detect flow rate abnormalities in high-dimensional cytometry data
tof_assess_flow_rate_tibble | Detect flow rate abnormalities in high-dimensional cytometry data (stored in a single data.frame)
tof_assess_model | Assess a trained elastic net model
tof_assess_model_new_data | Compute a trained elastic net model's performance metrics using new_data.
tof_assess_model_tuning | Access a trained elastic net model's performance metrics using its tuning data.
tof_batch_correct | Perform groupwise linear rescaling of high-dimensional cytometry measurements
tof_batch_correct_quantile | Batch-correct a tibble of high-dimensional cytometry data using quantile normalization.
tof_batch_correct_quantile_tibble | Batch-correct a tibble of high-dimensional cytometry data using quantile normalization.
tof_batch_correct_rescale | Perform groupwise linear rescaling of high-dimensional cytometry measurements
tof_build_classifier | Calculate centroids and covariance matrices for each cell subpopulation in healthy CyTOF data.
tof_calculate_flow_rate | Calculate the relative flow rates of different timepoints throughout a flow or mass cytometry run.
tof_check_model_args | Check argument specifications for a glmnet model.
tof_classify_cells | Classify each cell (i.e. each row) in a matrix of cancer cells into its most similar healthy developmental subpopulation.
tof_clean_metric_names | Rename glmnet's default model evaluation metrics to make them more interpretable
tof_cluster | Cluster high-dimensional cytometry data.
tof_cluster_ddpr | Perform developmental clustering on high-dimensional cytometry data.
tof_cluster_flowsom | Perform FlowSOM clustering on high-dimensional cytometry data
tof_cluster_grouped | Cluster (grouped) high-dimensional cytometry data.
tof_cluster_kmeans | Perform k-means clustering on high-dimensional cytometry data.
tof_cluster_phenograph | Perform PhenoGraph clustering on high-dimensional cytometry data.
tof_cluster_tibble | Cluster (ungrouped) high-dimensional cytometry data.
tof_compute_km_curve | Compute a Kaplan-Meier curve from sample-level survival data
tof_cosine_dist | A function for finding the cosine distance between each of the rows of a numeric matrix and a numeric vector.
tof_create_grid | Create an elastic net hyperparameter search grid of a specified size
tof_create_recipe | Create a recipe for preprocessing sample-level cytometry data for an elastic net model
tof_downsample | Downsample high-dimensional cytometry data.
tof_downsample_constant | Downsample high-dimensional cytometry data by randomly selecting a constant number of cells per group.
tof_downsample_density | Downsample high-dimensional cytometry data by randomly selecting a proportion of the cells in each group.
tof_downsample_prop | Downsample high-dimensional cytometry data by randomly selecting a proportion of the cells in each group.
tof_estimate_density | Estimate the local densities for all cells in a high-dimensional cytometry dataset.
tof_extract_central_tendency | Extract the central tendencies of CyTOF markers in each cluster in a 'tof_tibble'.
tof_extract_emd | Extract aggregated features from CyTOF data using earth-mover's distance (EMD)
tof_extract_features | Extract aggregated, sample-level features from CyTOF data.
tof_extract_jsd | Extract aggregated features from CyTOF data using the Jensen-Shannon Distance (JSD)
tof_extract_proportion | Extract the proportion of cells in each cluster in a 'tof_tibble'.
tof_extract_threshold | Extract aggregated features from CyTOF data using a binary threshold
tof_find_best | Find the optimal hyperparameters for an elastic net model from candidate performance metrics
tof_find_cv_predictions | Calculate and store the predicted outcomes for each validation set observation during model tuning
tof_find_emd | Find the earth-mover's distance between two numeric vectors
tof_find_jsd | Find the Jensen-Shannon Divergence (JSD) between two numeric vectors
tof_find_knn | Find the k-nearest neighbors of each cell in a high-dimensional cytometry dataset.
tof_find_log_rank_threshold | Compute the log-rank test p-value for the difference between the two survival curves obtained by splitting a dataset into a "low" and "high" risk group using all possible relative-risk thresholds.
tof_find_panel_info | Use tidytof's opinionated heuristic for extracted a high-dimensional cytometry panel's metal-antigen pairs from a flowFrame (read from a .fcs file.)
tof_fit_split | Fit a glmnet model and calculate performance metrics using a single rsplit object
tof_generate_palette | Generate a color palette using tidytof.
tof_get_model_mixture | Get a 'tof_model''s optimal mixture (alpha) value
tof_get_model_outcomes | Get a 'tof_model''s outcome variable name(s)
tof_get_model_penalty | Get a 'tof_model''s optimal penalty (lambda) value
tof_get_model_training_data | Get a 'tof_model''s training data
tof_get_model_type | Get a 'tof_model''s model type
tof_get_model_x | Get a 'tof_model''s processed predictor matrix (for glmnet)
tof_get_model_y | Get a 'tof_model''s processed outcome variable matrix (for glmnet)
tof_get_panel | Get panel information from a tof_tibble
tof_is_numeric | Find if a vector is numeric
tof_knn_density | Estimate cells' local densities using K-nearest-neighbor density estimation
tof_log_rank_test | Compute the log-rank test p-value for the difference between the two survival curves obtained by splitting a dataset into a "low" and "high" risk group using a given relative-risk threshold.
tof_make_knn_graph | Title
tof_make_roc_curve | Compute a receiver-operating curve (ROC) for a two-class or multiclass dataset
tof_metacluster | Metacluster clustered CyTOF data.
tof_metacluster_consensus | Metacluster clustered CyTOF data using consensus clustering
tof_metacluster_flowsom | Metacluster clustered CyTOF data using FlowSOM's built-in metaclustering algorithm
tof_metacluster_hierarchical | Metacluster clustered CyTOF data using hierarchical agglomerative clustering
tof_metacluster_kmeans | Metacluster clustered CyTOF data using k-means clustering
tof_metacluster_phenograph | Metacluster clustered CyTOF data using PhenoGraph clustering
tof_plot_cells_density | Plot marker expression density plots
tof_plot_cells_embedding | Plot scatterplots of single-cell data using low-dimensional feature embeddings
tof_plot_cells_layout | Plot force-directed layouts of single-cell data
tof_plot_cells_scatter | Plot scatterplots of single-cell data.
tof_plot_clusters_heatmap | Make a heatmap summarizing cluster marker expression patterns in CyTOF data
tof_plot_clusters_mst | Visualize clusters in CyTOF data using a minimum spanning tree (MST).
tof_plot_clusters_volcano | Create a volcano plot from differential expression analysis results
tof_plot_heatmap | Make a heatmap summarizing group marker expression patterns in high-dimensional cytometry data
tof_plot_model | Plot the results of a glmnet model fit on sample-level data.
tof_plot_model_linear | Plot the results of a linear glmnet model fit on sample-level data.
tof_plot_model_logistic | Plot the results of a two-class glmnet model fit on sample-level data.
tof_plot_model_multinomial | Plot the results of a multiclass glmnet model fit on sample-level data.
tof_plot_model_survival | Plot the results of a survival glmnet model fit on sample-level data.
tof_plot_sample_features | Make a heatmap summarizing sample marker expression patterns in CyTOF data
tof_plot_sample_heatmap | Make a heatmap summarizing sample marker expression patterns in CyTOF data
tof_postprocess | Post-process transformed CyTOF data.
tof_predict | Use a trained elastic net model to predict fitted values from new data
tof_preprocess | Preprocess raw high-dimensional cytometry data.
tof_prep_recipe | Train a recipe or list of recipes for preprocessing sample-level cytometry data
tof_read_csv | Read high-dimensional cytometry data from a .csv file into a tidy tibble.
tof_read_data | Read data from an .fcs/.csv file or a directory of .fcs/.csv files.
tof_read_fcs | Read high-dimensional cytometry data from an .fcs file into a tidy tibble.
tof_read_file | Read high-dimensional cytometry data from a single .fcs or .csv file into a tidy tibble.
tof_reduce_dimensions | Apply dimensionality reduction to a single-cell dataset.
tof_reduce_pca | Perform principal component analysis on single-cell data
tof_reduce_tsne | Perform t-distributed stochastic neighborhood embedding on single-cell data
tof_reduce_umap | Apply uniform manifold approximation and projection (UMAP) to single-cell data
tof_set_panel | Set panel information from a tof_tibble
tof_spade_density | Estimate cells' local densities as done in Spanning-tree Progression Analysis of Density-normalized Events (SPADE)
tof_split_data | Split high-dimensional cytometry data into a training and test set
tof_split_tidytof_reduced_dimensions | Split the dimensionality reduction data that tidytof combines during 'SingleCellExperiment' conversion
tof_train_model | Train an elastic net model to predict sample-level phenomena using high-dimensional cytometry data.
tof_transform | Transform raw high-dimensional cytometry data.
tof_tune_glmnet | Tune an elastic net model's hyperparameters over multiple resamples
tof_upsample | Upsample cells into the closest cluster in a reference dataset
tof_upsample_distance | Upsample cells into the closest cluster in a reference dataset
tof_upsample_neighbor | Upsample cells into the cluster of their nearest neighbor a reference dataset
tof_write_csv | Write a series of .csv files from a tof_tbl
tof_write_data | Write high-dimensional cytometry data to a file or to a directory of files
tof_write_fcs | Write a series of .fcs files from a tof_tbl

## -- W --

where | Select variables with a function