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      "title": "Constructor for a tof_model.",
      "topics": [
        "new_tof_model"
      ]
    },
    {
      "page": "new_tof_tibble",
      "title": "Constructor for a tof_tibble.",
      "concept": [
        "tof_tbl utilities"
      ],
      "topics": [
        "new_tof_tibble"
      ]
    },
    {
      "page": "phenograph_data",
      "title": "CyTOF data from 6,000 healthy immune cells from a single patient.",
      "topics": [
        "phenograph_data"
      ]
    },
    {
      "page": "rev_asinh",
      "title": "Reverses arcsinh transformation with cofactor `scale_factor` and a shift of `shift_factor`.",
      "topics": [
        "rev_asinh"
      ]
    },
    {
      "page": "tidytof_example_data",
      "title": "Get paths to tidytof example data",
      "topics": [
        "tidytof_example_data"
      ]
    },
    {
      "page": "tof_analyze_abundance",
      "title": "Perform Differential Abundance Analysis (DAA) on high-dimensional cytometry data",
      "concept": [
        "differential abundance analysis functions"
      ],
      "topics": [
        "tof_analyze_abundance"
      ]
    },
    {
      "page": "tof_analyze_abundance_diffcyt",
      "title": "Differential Abundance Analysis (DAA) with diffcyt",
      "concept": [
        "differential abundance analysis functions"
      ],
      "topics": [
        "tof_analyze_abundance_diffcyt"
      ]
    },
    {
      "page": "tof_analyze_abundance_glmm",
      "title": "Differential Abundance Analysis (DAA) with generalized linear mixed-models (GLMMs)",
      "concept": [
        "differential abundance analysis functions"
      ],
      "topics": [
        "tof_analyze_abundance_glmm"
      ]
    },
    {
      "page": "tof_analyze_abundance_ttest",
      "title": "Differential Abundance Analysis (DAA) with t-tests",
      "concept": [
        "differential abundance analysis functions"
      ],
      "topics": [
        "tof_analyze_abundance_ttest"
      ]
    },
    {
      "page": "tof_analyze_expression",
      "title": "Perform Differential Expression Analysis (DEA) on high-dimensional cytometry data",
      "concept": [
        "differential expression analysis functions"
      ],
      "topics": [
        "tof_analyze_expression"
      ]
    },
    {
      "page": "tof_analyze_expression_diffcyt",
      "title": "Differential Expression Analysis (DEA) with diffcyt",
      "concept": [
        "differential expression analysis functions"
      ],
      "topics": [
        "tof_analyze_expression_diffcyt"
      ]
    },
    {
      "page": "tof_analyze_expression_lmm",
      "title": "Differential Expression Analysis (DEA) with linear mixed-models (LMMs)",
      "concept": [
        "differential expression analysis functions"
      ],
      "topics": [
        "tof_analyze_expression_lmm"
      ]
    },
    {
      "page": "tof_analyze_expression_ttest",
      "title": "Differential Expression Analysis (DEA) with t-tests",
      "concept": [
        "differential expression analysis functions"
      ],
      "topics": [
        "tof_analyze_expression_ttest"
      ]
    },
    {
      "page": "tof_annotate_clusters",
      "title": "Manually annotate tidytof-computed clusters using user-specified labels",
      "topics": [
        "tof_annotate_clusters"
      ]
    },
    {
      "page": "tof_apply_classifier",
      "title": "Perform developmental clustering on CyTOF data using a pre-fit classifier",
      "topics": [
        "tof_apply_classifier"
      ]
    },
    {
      "page": "tof_assess_channels",
      "title": "Detect low-expression (i.e. potentially failed) channels in high-dimensional cytometry data",
      "topics": [
        "tof_assess_channels"
      ]
    },
    {
      "page": "tof_assess_clusters_distance",
      "title": "Assess a clustering result by calculating the z-score of each cell's mahalanobis distance to its cluster centroid and flagging outliers.",
      "topics": [
        "tof_assess_clusters_distance"
      ]
    },
    {
      "page": "tof_assess_clusters_entropy",
      "title": "Assess a clustering result by calculating the shannon entropy of each cell's mahalanobis distance to all cluster centroids and flagging outliers.",
      "topics": [
        "tof_assess_clusters_entropy"
      ]
    },
    {
      "page": "tof_assess_clusters_knn",
      "title": "Assess a clustering result by calculating a cell's cluster assignment to that of its K nearest neighbors.",
      "topics": [
        "tof_assess_clusters_knn"
      ]
    },
    {
      "page": "tof_assess_flow_rate",
      "title": "Detect flow rate abnormalities in high-dimensional cytometry data",
      "topics": [
        "tof_assess_flow_rate"
      ]
    },
    {
      "page": "tof_assess_flow_rate_tibble",
      "title": "Detect flow rate abnormalities in high-dimensional cytometry data (stored in a single data.frame)",
      "topics": [
        "tof_assess_flow_rate_tibble"
      ]
    },
    {
      "page": "tof_assess_model",
      "title": "Assess a trained elastic net model",
      "concept": [
        "modeling functions"
      ],
      "topics": [
        "tof_assess_model"
      ]
    },
    {
      "page": "tof_assess_model_new_data",
      "title": "Compute a trained elastic net model's performance metrics using new_data.",
      "topics": [
        "tof_assess_model_new_data"
      ]
    },
    {
      "page": "tof_assess_model_tuning",
      "title": "Access a trained elastic net model's performance metrics using its tuning data.",
      "topics": [
        "tof_assess_model_tuning"
      ]
    },
    {
      "page": "tof_batch_correct",
      "title": "Perform groupwise linear rescaling of high-dimensional cytometry measurements",
      "topics": [
        "tof_batch_correct"
      ]
    },
    {
      "page": "tof_batch_correct_quantile",
      "title": "Batch-correct a tibble of high-dimensional cytometry data using quantile normalization.",
      "topics": [
        "tof_batch_correct_quantile"
      ]
    },
    {
      "page": "tof_batch_correct_quantile_tibble",
      "title": "Batch-correct a tibble of high-dimensional cytometry data using quantile normalization.",
      "topics": [
        "tof_batch_correct_quantile_tibble"
      ]
    },
    {
      "page": "tof_batch_correct_rescale",
      "title": "Perform groupwise linear rescaling of high-dimensional cytometry measurements",
      "topics": [
        "tof_batch_correct_rescale"
      ]
    },
    {
      "page": "tof_build_classifier",
      "title": "Calculate centroids and covariance matrices for each cell subpopulation in healthy CyTOF data.",
      "topics": [
        "tof_build_classifier"
      ]
    },
    {
      "page": "tof_calculate_flow_rate",
      "title": "Calculate the relative flow rates of different timepoints throughout a flow or mass cytometry run.",
      "topics": [
        "tof_calculate_flow_rate"
      ]
    },
    {
      "page": "tof_check_model_args",
      "title": "Check argument specifications for a glmnet model.",
      "topics": [
        "tof_check_model_args"
      ]
    },
    {
      "page": "tof_classify_cells",
      "title": "Classify each cell (i.e. each row) in a matrix of cancer cells into its most similar healthy developmental subpopulation.",
      "topics": [
        "tof_classify_cells"
      ]
    },
    {
      "page": "tof_clean_metric_names",
      "title": "Rename glmnet's default model evaluation metrics to make them more interpretable",
      "topics": [
        "tof_clean_metric_names"
      ]
    },
    {
      "page": "tof_cluster",
      "title": "Cluster high-dimensional cytometry data.",
      "concept": [
        "clustering functions"
      ],
      "topics": [
        "tof_cluster"
      ]
    },
    {
      "page": "tof_cluster_ddpr",
      "title": "Perform developmental clustering on high-dimensional cytometry data.",
      "concept": [
        "clustering functions"
      ],
      "topics": [
        "tof_cluster_ddpr"
      ]
    },
    {
      "page": "tof_cluster_flowsom",
      "title": "Perform FlowSOM clustering on high-dimensional cytometry data",
      "concept": [
        "clustering functions"
      ],
      "topics": [
        "tof_cluster_flowsom"
      ]
    },
    {
      "page": "tof_cluster_grouped",
      "title": "Cluster (grouped) high-dimensional cytometry data.",
      "topics": [
        "tof_cluster_grouped"
      ]
    },
    {
      "page": "tof_cluster_kmeans",
      "title": "Perform k-means clustering on high-dimensional cytometry data.",
      "concept": [
        "clustering functions"
      ],
      "topics": [
        "tof_cluster_kmeans"
      ]
    },
    {
      "page": "tof_cluster_phenograph",
      "title": "Perform PhenoGraph clustering on high-dimensional cytometry data.",
      "concept": [
        "clustering functions"
      ],
      "topics": [
        "tof_cluster_phenograph"
      ]
    },
    {
      "page": "tof_cluster_tibble",
      "title": "Cluster (ungrouped) high-dimensional cytometry data.",
      "topics": [
        "tof_cluster_tibble"
      ]
    },
    {
      "page": "tof_compute_km_curve",
      "title": "Compute a Kaplan-Meier curve from sample-level survival data",
      "topics": [
        "tof_compute_km_curve"
      ]
    },
    {
      "page": "tof_cosine_dist",
      "title": "A function for finding the cosine distance between each of the rows of a numeric matrix and a numeric vector.",
      "topics": [
        "tof_cosine_dist"
      ]
    },
    {
      "page": "tof_create_grid",
      "title": "Create an elastic net hyperparameter search grid of a specified size",
      "concept": [
        "modeling functions"
      ],
      "topics": [
        "tof_create_grid"
      ]
    },
    {
      "page": "tof_create_recipe",
      "title": "Create a recipe for preprocessing sample-level cytometry data for an elastic net model",
      "topics": [
        "tof_create_recipe"
      ]
    },
    {
      "page": "tof_downsample",
      "title": "Downsample high-dimensional cytometry data.",
      "concept": [
        "downsampling functions"
      ],
      "topics": [
        "tof_downsample"
      ]
    },
    {
      "page": "tof_downsample_constant",
      "title": "Downsample high-dimensional cytometry data by randomly selecting a constant number of cells per group.",
      "concept": [
        "downsampling functions"
      ],
      "topics": [
        "tof_downsample_constant"
      ]
    },
    {
      "page": "tof_downsample_density",
      "title": "Downsample high-dimensional cytometry data by randomly selecting a proportion of the cells in each group.",
      "concept": [
        "downsampling functions"
      ],
      "topics": [
        "tof_downsample_density"
      ]
    },
    {
      "page": "tof_downsample_prop",
      "title": "Downsample high-dimensional cytometry data by randomly selecting a proportion of the cells in each group.",
      "concept": [
        "downsampling functions"
      ],
      "topics": [
        "tof_downsample_prop"
      ]
    },
    {
      "page": "tof_estimate_density",
      "title": "Estimate the local densities for all cells in a high-dimensional cytometry dataset.",
      "concept": [
        "local density estimation functions"
      ],
      "topics": [
        "tof_estimate_density"
      ]
    },
    {
      "page": "tof_extract_central_tendency",
      "title": "Extract the central tendencies of CyTOF markers in each cluster in a `tof_tibble`.",
      "concept": [
        "feature extraction functions"
      ],
      "topics": [
        "tof_extract_central_tendency"
      ]
    },
    {
      "page": "tof_extract_emd",
      "title": "Extract aggregated features from CyTOF data using earth-mover's distance (EMD)",
      "concept": [
        "feature extraction functions"
      ],
      "topics": [
        "tof_extract_emd"
      ]
    },
    {
      "page": "tof_extract_features",
      "title": "Extract aggregated, sample-level features from CyTOF data.",
      "concept": [
        "feature extraction functions"
      ],
      "topics": [
        "tof_extract_features"
      ]
    },
    {
      "page": "tof_extract_jsd",
      "title": "Extract aggregated features from CyTOF data using the Jensen-Shannon Distance (JSD)",
      "concept": [
        "feature extraction functions"
      ],
      "topics": [
        "tof_extract_jsd"
      ]
    },
    {
      "page": "tof_extract_proportion",
      "title": "Extract the proportion of cells in each cluster in a `tof_tibble`.",
      "concept": [
        "feature extraction functions"
      ],
      "topics": [
        "tof_extract_proportion"
      ]
    },
    {
      "page": "tof_extract_threshold",
      "title": "Extract aggregated features from CyTOF data using a binary threshold",
      "concept": [
        "feature extraction functions"
      ],
      "topics": [
        "tof_extract_threshold"
      ]
    },
    {
      "page": "tof_find_best",
      "title": "Find the optimal hyperparameters for an elastic net model from candidate performance metrics",
      "topics": [
        "tof_find_best"
      ]
    },
    {
      "page": "tof_find_cv_predictions",
      "title": "Calculate and store the predicted outcomes for each validation set observation during model tuning",
      "topics": [
        "tof_find_cv_predictions"
      ]
    },
    {
      "page": "tof_find_emd",
      "title": "Find the earth-mover's distance between two numeric vectors",
      "topics": [
        "tof_find_emd"
      ]
    },
    {
      "page": "tof_find_jsd",
      "title": "Find the Jensen-Shannon Divergence (JSD) between two numeric vectors",
      "topics": [
        "tof_find_jsd"
      ]
    },
    {
      "page": "tof_find_knn",
      "title": "Find the k-nearest neighbors of each cell in a high-dimensional cytometry dataset.",
      "topics": [
        "tof_find_knn"
      ]
    },
    {
      "page": "tof_find_log_rank_threshold",
      "title": "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.",
      "topics": [
        "tof_find_log_rank_threshold"
      ]
    },
    {
      "page": "tof_find_panel_info",
      "title": "Use tidytof's opinionated heuristic for extracted a high-dimensional cytometry panel's metal-antigen pairs from a flowFrame (read from a .fcs file.)",
      "topics": [
        "tof_find_panel_info"
      ]
    },
    {
      "page": "tof_fit_split",
      "title": "Fit a glmnet model and calculate performance metrics using a single rsplit object",
      "topics": [
        "tof_fit_split"
      ]
    },
    {
      "page": "tof_generate_palette",
      "title": "Generate a color palette using tidytof.",
      "topics": [
        "tof_generate_palette"
      ]
    },
    {
      "page": "tof_get_model_mixture",
      "title": "Get a `tof_model`'s optimal mixture (alpha) value",
      "topics": [
        "tof_get_model_mixture"
      ]
    },
    {
      "page": "tof_get_model_outcomes",
      "title": "Get a `tof_model`'s outcome variable name(s)",
      "topics": [
        "tof_get_model_outcomes"
      ]
    },
    {
      "page": "tof_get_model_penalty",
      "title": "Get a `tof_model`'s optimal penalty (lambda) value",
      "topics": [
        "tof_get_model_penalty"
      ]
    },
    {
      "page": "tof_get_model_training_data",
      "title": "Get a `tof_model`'s training data",
      "topics": [
        "tof_get_model_training_data"
      ]
    },
    {
      "page": "tof_get_model_type",
      "title": "Get a `tof_model`'s model type",
      "topics": [
        "tof_get_model_type"
      ]
    },
    {
      "page": "tof_get_model_x",
      "title": "Get a `tof_model`'s processed predictor matrix (for glmnet)",
      "topics": [
        "tof_get_model_x"
      ]
    },
    {
      "page": "tof_get_model_y",
      "title": "Get a `tof_model`'s processed outcome variable matrix (for glmnet)",
      "topics": [
        "tof_get_model_y"
      ]
    },
    {
      "page": "tof_get_panel",
      "title": "Get panel information from a tof_tibble",
      "concept": [
        "tof_tbl utilities"
      ],
      "topics": [
        "tof_get_panel"
      ]
    },
    {
      "page": "tof_is_numeric",
      "title": "Find if a vector is numeric",
      "topics": [
        "tof_is_numeric"
      ]
    },
    {
      "page": "tof_knn_density",
      "title": "Estimate cells' local densities using K-nearest-neighbor density estimation",
      "concept": [
        "local density estimation functions"
      ],
      "topics": [
        "tof_knn_density"
      ]
    },
    {
      "page": "tof_log_rank_test",
      "title": "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.",
      "topics": [
        "tof_log_rank_test"
      ]
    },
    {
      "page": "tof_make_knn_graph",
      "title": "Title",
      "topics": [
        "tof_make_knn_graph"
      ]
    },
    {
      "page": "tof_make_roc_curve",
      "title": "Compute a receiver-operating curve (ROC) for a two-class or multiclass dataset",
      "topics": [
        "tof_make_roc_curve"
      ]
    },
    {
      "page": "tof_metacluster",
      "title": "Metacluster clustered CyTOF data.",
      "concept": [
        "metaclustering functions"
      ],
      "topics": [
        "tof_metacluster"
      ]
    },
    {
      "page": "tof_metacluster_consensus",
      "title": "Metacluster clustered CyTOF data using consensus clustering",
      "concept": [
        "metaclustering functions"
      ],
      "topics": [
        "tof_metacluster_consensus"
      ]
    },
    {
      "page": "tof_metacluster_flowsom",
      "title": "Metacluster clustered CyTOF data using FlowSOM's built-in metaclustering algorithm",
      "concept": [
        "metaclustering functions"
      ],
      "topics": [
        "tof_metacluster_flowsom"
      ]
    },
    {
      "page": "tof_metacluster_hierarchical",
      "title": "Metacluster clustered CyTOF data using hierarchical agglomerative clustering",
      "concept": [
        "metaclustering functions"
      ],
      "topics": [
        "tof_metacluster_hierarchical"
      ]
    },
    {
      "page": "tof_metacluster_kmeans",
      "title": "Metacluster clustered CyTOF data using k-means clustering",
      "concept": [
        "metaclustering functions"
      ],
      "topics": [
        "tof_metacluster_kmeans"
      ]
    },
    {
      "page": "tof_metacluster_phenograph",
      "title": "Metacluster clustered CyTOF data using PhenoGraph clustering",
      "concept": [
        "metaclustering functions"
      ],
      "topics": [
        "tof_metacluster_phenograph"
      ]
    },
    {
      "page": "tof_plot_cells_density",
      "title": "Plot marker expression density plots",
      "topics": [
        "tof_plot_cells_density"
      ]
    },
    {
      "page": "tof_plot_cells_embedding",
      "title": "Plot scatterplots of single-cell data using low-dimensional feature embeddings",
      "concept": [
        "visualization functions"
      ],
      "topics": [
        "tof_plot_cells_embedding"
      ]
    },
    {
      "page": "tof_plot_cells_layout",
      "title": "Plot force-directed layouts of single-cell data",
      "concept": [
        "visualization functions"
      ],
      "topics": [
        "tof_plot_cells_layout"
      ]
    },
    {
      "page": "tof_plot_cells_scatter",
      "title": "Plot scatterplots of single-cell data.",
      "concept": [
        "visualization functions"
      ],
      "topics": [
        "tof_plot_cells_scatter"
      ]
    },
    {
      "page": "tof_plot_clusters_heatmap",
      "title": "Make a heatmap summarizing cluster marker expression patterns in CyTOF data",
      "topics": [
        "tof_plot_clusters_heatmap"
      ]
    },
    {
      "page": "tof_plot_clusters_mst",
      "title": "Visualize clusters in CyTOF data using a minimum spanning tree (MST).",
      "topics": [
        "tof_plot_clusters_mst"
      ]
    },
    {
      "page": "tof_plot_clusters_volcano",
      "title": "Create a volcano plot from differential expression analysis results",
      "topics": [
        "tof_plot_clusters_volcano"
      ]
    },
    {
      "page": "tof_plot_heatmap",
      "title": "Make a heatmap summarizing group marker expression patterns in high-dimensional cytometry data",
      "topics": [
        "tof_plot_heatmap"
      ]
    },
    {
      "page": "tof_plot_model",
      "title": "Plot the results of a glmnet model fit on sample-level data.",
      "topics": [
        "tof_plot_model"
      ]
    },
    {
      "page": "tof_plot_model_linear",
      "title": "Plot the results of a linear glmnet model fit on sample-level data.",
      "topics": [
        "tof_plot_model_linear"
      ]
    },
    {
      "page": "tof_plot_model_logistic",
      "title": "Plot the results of a two-class glmnet model fit on sample-level data.",
      "topics": [
        "tof_plot_model_logistic"
      ]
    },
    {
      "page": "tof_plot_model_multinomial",
      "title": "Plot the results of a multiclass glmnet model fit on sample-level data.",
      "topics": [
        "tof_plot_model_multinomial"
      ]
    },
    {
      "page": "tof_plot_model_survival",
      "title": "Plot the results of a survival glmnet model fit on sample-level data.",
      "topics": [
        "tof_plot_model_survival"
      ]
    },
    {
      "page": "tof_plot_sample_features",
      "title": "Make a heatmap summarizing sample marker expression patterns in CyTOF data",
      "topics": [
        "tof_plot_sample_features"
      ]
    },
    {
      "page": "tof_plot_sample_heatmap",
      "title": "Make a heatmap summarizing sample marker expression patterns in CyTOF data",
      "topics": [
        "tof_plot_sample_heatmap"
      ]
    },
    {
      "page": "tof_postprocess",
      "title": "Post-process transformed CyTOF data.",
      "topics": [
        "tof_postprocess"
      ]
    },
    {
      "page": "tof_predict",
      "title": "Use a trained elastic net model to predict fitted values from new data",
      "concept": [
        "modeling functions"
      ],
      "topics": [
        "tof_predict"
      ]
    },
    {
      "page": "tof_prep_recipe",
      "title": "Train a recipe or list of recipes for preprocessing sample-level cytometry data",
      "topics": [
        "tof_prep_recipe"
      ]
    },
    {
      "page": "tof_preprocess",
      "title": "Preprocess raw high-dimensional cytometry data.",
      "topics": [
        "tof_preprocess"
      ]
    },
    {
      "page": "tof_read_csv",
      "title": "Read high-dimensional cytometry data from a .csv file into a tidy tibble.",
      "topics": [
        "tof_read_csv"
      ]
    },
    {
      "page": "tof_read_data",
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