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  "Description": "These tools facilitate batch effects analysis and\ncorrection in high-throughput experiments. It was developed\nprimarily for mass-spectrometry proteomics (DIA/SWATH), but\ncould also be applicable to most omic data with minor\nadaptations. The package contains functions for diagnostics\n(proteome/genome-wide and feature-level), correction\n(normalization and batch effects correction) and quality\ncontrol. Non-linear fitting based approaches were also included\nto deal with complex, mass spectrometry-specific signal drifts.",
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    "adjust_batch_trend_dm",
    "as_ProBatchFeatures",
    "calculate_feature_CV",
    "calculate_peptide_corr_distr",
    "calculate_PVCA",
    "calculate_sample_corr_distr",
    "center_feature_batch",
    "center_feature_batch_means_df",
    "center_feature_batch_means_dm",
    "center_feature_batch_medians_df",
    "center_feature_batch_medians_dm",
    "check_sample_consistency",
    "convert_annotation_classes",
    "correct_batch_effects",
    "correct_batch_effects_df",
    "correct_batch_effects_dm",
    "correct_with_ComBat",
    "correct_with_ComBat_df",
    "correct_with_ComBat_dm",
    "correct_with_removeBatchEffect",
    "correct_with_removeBatchEffect_df",
    "correct_with_removeBatchEffect_dm",
    "create_peptide_annotation",
    "date_to_sample_order",
    "dates_to_posix",
    "define_sample_order",
    "detect_nested_batches",
    "detect_outlier_samples",
    "filter_metadata_columns",
    "find_duplicated_columns",
    "fit_nonlinear",
    "generate_colors_for_numeric",
    "get_chain",
    "get_operation_log",
    "guess_factor_columns_if_needed",
    "handle_factor_numeric_overlap",
    "handle_missing_values",
    "log_transform_df",
    "log_transform_dm",
    "long_to_matrix",
    "matrix_to_long",
    "merge_rare_levels",
    "metadata_column_summary",
    "normalize_data_df",
    "normalize_data_dm",
    "normalize_sample_medians_df",
    "normalize_sample_medians_dm",
    "pb_add_level",
    "pb_aggregate_level",
    "pb_apply_matrix_method",
    "pb_as_long",
    "pb_as_wide",
    "pb_assay_matrix",
    "pb_current_assay",
    "pb_eval",
    "pb_filterNA",
    "pb_groupfilterNA",
    "pb_has_step",
    "pb_infIsNA",
    "pb_list_steps",
    "pb_nNA",
    "pb_pipeline_name",
    "pb_register_step",
    "pb_step_result",
    "pb_subset_samples",
    "pb_transform",
    "pb_unregister_steps",
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    "plot_boxplot",
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    "plot_CV_distr",
    "plot_CV_distr.df",
    "plot_grouped_NA_heatmap",
    "plot_heatmap_diagnostic",
    "plot_heatmap_generic",
    "plot_hierarchical_clustering",
    "plot_iRT",
    "plot_NA_density",
    "plot_NA_frequency",
    "plot_NA_heatmap",
    "plot_NA_intensity",
    "plot_PCA",
    "plot_peptide_corr_distribution",
    "plot_peptide_corr_distribution.corrDF",
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    "plot_protein_corrplot",
    "plot_PVCA",
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    "plot_sample_corr_heatmap",
    "plot_sample_mean",
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    "ProBatchFeatures",
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    "validate_batch_design",
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      "file": "example_ecoli_data.rda",
      "class": [
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      "tojson": true
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      "object": "example_peptide_annotation",
      "file": "example_peptide_annotation.rda",
      "class": [
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        "Gene",
        "ProteinName"
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      "table": true,
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      "file": "example_proteome.rda",
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      "file": "example_proteome_matrix.rda",
      "class": [
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        "array"
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      "title": "Sample annotation data version 1",
      "object": "example_sample_annotation",
      "file": "example_sample_annotation.rda",
      "class": [
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      "fields": [
        "FullRunName",
        "MS_batch",
        "EarTag",
        "Strain",
        "Diet",
        "Sex",
        "RunDate",
        "RunTime",
        "DateTime",
        "order",
        "digestion_batch"
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      "rows": 233,
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      "page": "ProBatchFeatures-subset",
      "title": "Subset 'ProBatchFeatures' objects without dropping metadata.",
      "topics": [
        "ProBatchFeatures-subset",
        "[,ProBatchFeatures,ANY,ANY,ANY-method"
      ]
    },
    {
      "page": "as_ProBatchFeatures",
      "title": "Coerce a QFeatures object into ProBatchFeatures",
      "topics": [
        "as_ProBatchFeatures"
      ]
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    {
      "page": "calculate_feature_CV",
      "title": "Calculate CV distribution for each feature",
      "topics": [
        "calculate_feature_CV"
      ]
    },
    {
      "page": "calculate_peptide_corr_distr",
      "title": "Calculate peptide correlation between and within peptides of one protein",
      "topics": [
        "calculate_peptide_corr_distr"
      ]
    },
    {
      "page": "calculate_PVCA",
      "title": "Calculate variance distribution by variable",
      "topics": [
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        "calculate_PVCA.default",
        "calculate_PVCA.ProBatchFeatures"
      ]
    },
    {
      "page": "calculate_sample_corr_distr",
      "title": "Calculates correlation for all pairs of the samples in data matrix, labels as replicated/same_batch/unrelated in output columns (see \"Value\").",
      "topics": [
        "calculate_sample_corr_distr"
      ]
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      "title": "Center features per-batch by median/mean (unified)",
      "topics": [
        "center_feature_batch"
      ]
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    {
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      "title": "DEPRECATED: center_feature_batch_means_df",
      "topics": [
        "center_feature_batch_means_df"
      ]
    },
    {
      "page": "center_feature_batch_means_dm",
      "title": "DEPRECATED: center_feature_batch_means_dm",
      "topics": [
        "center_feature_batch_means_dm"
      ]
    },
    {
      "page": "center_feature_batch_medians_df",
      "title": "DEPRECATED: center_feature_batch_medians_df",
      "topics": [
        "center_feature_batch_medians_df"
      ]
    },
    {
      "page": "center_feature_batch_medians_dm",
      "title": "DEPRECATED: center_feature_batch_medians_dm",
      "topics": [
        "center_feature_batch_medians_dm"
      ]
    },
    {
      "page": "check_sample_consistency",
      "title": "Check if sample annotation is consistent with data matrix and join the two",
      "topics": [
        "check_sample_consistency"
      ]
    },
    {
      "page": "convert_annotation_classes",
      "title": "Convert factor and numeric columns",
      "topics": [
        "convert_annotation_classes"
      ]
    },
    {
      "page": "correct_batch_effects",
      "title": "Batch correction methods for normalized data",
      "topics": [
        "adjust_batch_trend_df",
        "adjust_batch_trend_dm",
        "correct_batch_effects"
      ]
    },
    {
      "page": "correct_batch_effects_df",
      "title": "DEPRECATED: correct_batch_effects_df",
      "topics": [
        "correct_batch_effects_df"
      ]
    },
    {
      "page": "correct_batch_effects_dm",
      "title": "DEPRECATED: correct_batch_effects_dm",
      "topics": [
        "correct_batch_effects_dm"
      ]
    },
    {
      "page": "correct_with_ComBat",
      "title": "ComBat-based batch correction (unified)",
      "topics": [
        "correct_with_ComBat"
      ]
    },
    {
      "page": "correct_with_ComBat_df",
      "title": "DEPRECATED: correct_with_ComBat_df",
      "topics": [
        "correct_with_ComBat_df"
      ]
    },
    {
      "page": "correct_with_ComBat_dm",
      "title": "DEPRECATED: correct_with_ComBat_dm",
      "topics": [
        "correct_with_ComBat_dm"
      ]
    },
    {
      "page": "correct_with_removeBatchEffect",
      "title": "Batch effect correction with limma::removeBatchEffect (unified)",
      "topics": [
        "correct_with_removeBatchEffect"
      ]
    },
    {
      "page": "correct_with_removeBatchEffect_df",
      "title": "DEPRECATED: correct_with_removeBatchEffect_df",
      "topics": [
        "correct_with_removeBatchEffect_df"
      ]
    },
    {
      "page": "correct_with_removeBatchEffect_dm",
      "title": "DEPRECATED: correct_with_removeBatchEffect_dm",
      "topics": [
        "correct_with_removeBatchEffect_dm"
      ]
    },
    {
      "page": "create_peptide_annotation",
      "title": "Prepare peptide annotation from long format data frame",
      "topics": [
        "create_peptide_annotation"
      ]
    },
    {
      "page": "date_to_sample_order",
      "title": "Convert date/time to POSIXct and rank samples by it",
      "topics": [
        "date_to_sample_order"
      ]
    },
    {
      "page": "dates_to_posix",
      "title": "Convert date/time to POSIXct",
      "concept": [
        "date"
      ],
      "topics": [
        "dates_to_posix"
      ]
    },
    {
      "page": "define_sample_order",
      "title": "Defining sample order internally",
      "topics": [
        "define_sample_order"
      ]
    },
    {
      "page": "detect_nested_batches",
      "title": "Detect nested batch variables",
      "topics": [
        "detect_nested_batches"
      ]
    },
    {
      "page": "detect_outlier_samples",
      "title": "Detect outlier samples in PCA space",
      "topics": [
        "detect_outlier_samples"
      ]
    },
    {
      "page": "example_ecoli_data",
      "title": "Example multi-center DIA LFQ E. coli proteomics (DIA-NN)",
      "topics": [
        "example_ecoli_data"
      ]
    },
    {
      "page": "example_peptide_annotation",
      "title": "Peptide annotation data",
      "topics": [
        "example_peptide_annotation"
      ]
    },
    {
      "page": "example_proteome",
      "title": "Example protein data in long format",
      "topics": [
        "example_proteome"
      ]
    },
    {
      "page": "example_proteome_matrix",
      "title": "Example protein data in matrix",
      "topics": [
        "example_proteome_matrix"
      ]
    },
    {
      "page": "example_sample_annotation",
      "title": "Sample annotation data version 1",
      "topics": [
        "example_sample_annotation"
      ]
    },
    {
      "page": "feature_level_diagnostics",
      "title": "Plotting peptide measurements",
      "topics": [
        "feature_level_diagnostics",
        "plot_iRT",
        "plot_peptides_of_one_protein",
        "plot_single_feature",
        "plot_spike_in",
        "plot_with_fitting_curve"
      ]
    },
    {
      "page": "filter_metadata_columns",
      "title": "Filter metadata columns based on duplication and completeness",
      "topics": [
        "filter_metadata_columns",
        "filter_metadata_columns.default",
        "filter_metadata_columns.ProBatchFeatures"
      ]
    },
    {
      "page": "find_duplicated_columns",
      "title": "Identify duplicated columns in metadata or assays",
      "topics": [
        "find_duplicated_columns",
        "find_duplicated_columns.default",
        "find_duplicated_columns.ProBatchFeatures"
      ]
    },
    {
      "page": "fit_nonlinear",
      "title": "Fit a non-linear trend (currently optimized for LOESS)",
      "topics": [
        "fit_nonlinear"
      ]
    },
    {
      "page": "get_chain",
      "title": "Retrieve operation chain as vector or single string \"combat_on_mediannorm_on_log\"",
      "topics": [
        "get_chain"
      ]
    },
    {
      "page": "get_operation_log",
      "title": "Access the operation log (structured)",
      "topics": [
        "get_operation_log"
      ]
    },
    {
      "page": "guess_factor_columns_if_needed",
      "title": "Guess factors if numeric columns were not provided",
      "topics": [
        "guess_factor_columns_if_needed"
      ]
    },
    {
      "page": "handle_factor_numeric_overlap",
      "title": "Handle factor columns that are duplicated in numeric_columns",
      "topics": [
        "handle_factor_numeric_overlap"
      ]
    },
    {
      "page": "handle_missing_values",
      "title": "Handle missing values in a numeric matrix",
      "topics": [
        "handle_missing_values"
      ]
    },
    {
      "page": "long_to_matrix",
      "title": "Long to wide data format conversion",
      "concept": [
        "matrix manipulation functions"
      ],
      "topics": [
        "long_to_matrix"
      ]
    },
    {
      "page": "matrix_to_long",
      "title": "Wide to long conversion",
      "concept": [
        "matrix manipulation functions"
      ],
      "topics": [
        "matrix_to_long"
      ]
    },
    {
      "page": "metadata_column_summary",
      "title": "Summarise metadata column cardinality and missingness",
      "topics": [
        "metadata_column_summary",
        "metadata_column_summary.default",
        "metadata_column_summary.ProBatchFeatures"
      ]
    },
    {
      "page": "normalize",
      "title": "Data normalization methods",
      "topics": [
        "normalize",
        "normalize_data_df",
        "normalize_data_dm",
        "normalize_sample_medians_df",
        "normalize_sample_medians_dm",
        "quantile_normalize_df",
        "quantile_normalize_dm"
      ]
    },
    {
      "page": "pb_add_level",
      "title": "Add a new level from an external matrix and link to an existing assay",
      "topics": [
        "pb_add_level"
      ]
    },
    {
      "page": "pb_aggregate_level",
      "title": "Aggregate features (e.g., peptide -> protein) and store as new level",
      "topics": [
        "pb_aggregate_level"
      ]
    },
    {
      "page": "pb_apply_matrix_method",
      "title": "Apply a matrix-oriented method safely",
      "topics": [
        "pb_apply_matrix_method"
      ]
    },
    {
      "page": "pb_as_long",
      "title": "Get current assay as LONG (via proBatch::matrix_to_long)",
      "topics": [
        "pb_as_long"
      ]
    },
    {
      "page": "pb_as_wide",
      "title": "Get an assay matrix (wide)",
      "topics": [
        "pb_as_wide"
      ]
    },
    {
      "page": "pb_assay_matrix",
      "title": "Convenience accessor for stored or virtual assay matrices",
      "topics": [
        "pb_assay_matrix"
      ]
    },
    {
      "page": "pb_current_assay",
      "title": "Current (latest) assay name",
      "topics": [
        "pb_current_assay"
      ]
    },
    {
      "page": "pb_eval",
      "title": "Evaluate a pipeline and return the matrix, without storing",
      "topics": [
        "pb_eval"
      ]
    },
    {
      "page": "pb_has_step",
      "title": "Test whether a transformation step is registered or available",
      "topics": [
        "pb_has_step"
      ]
    },
    {
      "page": "pb_list_steps",
      "title": "List registered transformation steps",
      "topics": [
        "pb_list_steps"
      ]
    },
    {
      "page": "pb_missing_helpers",
      "title": "Apply 'QFeatures' missing-data helpers to stored assays",
      "topics": [
        "pb_filterNA",
        "pb_groupfilterNA",
        "pb_infIsNA",
        "pb_missing_helpers",
        "pb_nNA",
        "pb_zeroIsNA"
      ]
    },
    {
      "page": "pb_pipeline_name",
      "title": "Pretty pipeline name derived from the assay",
      "topics": [
        "pb_pipeline_name"
      ]
    },
    {
      "page": "pb_register_step",
      "title": "Register a provider-neutral transformation step",
      "topics": [
        "pb_register_step"
      ]
    },
    {
      "page": "pb_step_result",
      "title": "Return transformed data with structured method artifacts",
      "topics": [
        "pb_step_result"
      ]
    },
    {
      "page": "pb_subset_samples",
      "title": "Subset samples using 'ProBatchFeatures' metadata.",
      "topics": [
        "pb_subset_samples"
      ]
    },
    {
      "page": "pb_transform",
      "title": "Compute a pipeline and optionally store only the final result",
      "topics": [
        "pb_transform"
      ]
    },
    {
      "page": "pb_unregister_steps",
      "title": "Unregister all transformation steps from one provider",
      "topics": [
        "pb_unregister_steps"
      ]
    },
    {
      "page": "plot_corr_matrix",
      "title": "Visualise correlation matrix",
      "topics": [
        "plot_corr_matrix"
      ]
    },
    {
      "page": "plot_CV_distr",
      "title": "Plot CV distribution to compare various steps of the analysis",
      "topics": [
        "plot_CV_distr"
      ]
    },
    {
      "page": "plot_CV_distr.df",
      "title": "Plot the distribution (boxplots) of per-batch per-step CV of features",
      "topics": [
        "plot_CV_distr.df"
      ]
    },
    {
      "page": "plot_grouped_NA_heatmap",
      "title": "Plot grouped missing-value heatmap(s)",
      "topics": [
        "plot_grouped_NA_heatmap",
        "plot_grouped_NA_heatmap.default",
        "plot_grouped_NA_heatmap.ProBatchFeatures"
      ]
    },
    {
      "page": "plot_heatmap_diagnostic",
      "title": "Plot the heatmap of samples (cols) vs features (rows)",
      "topics": [
        "plot_heatmap_diagnostic",
        "plot_heatmap_diagnostic.default",
        "plot_heatmap_diagnostic.ProBatchFeatures"
      ]
    },
    {
      "page": "plot_heatmap_generic",
      "title": "Plot the heatmap",
      "topics": [
        "plot_heatmap_generic",
        "plot_heatmap_generic.default",
        "plot_heatmap_generic.ProBatchFeatures"
      ]
    },
    {
      "page": "plot_hierarchical_clustering",
      "title": "cluster the data matrix to visually inspect which confounder dominates",
      "topics": [
        "plot_hierarchical_clustering",
        "plot_hierarchical_clustering.default",
        "plot_hierarchical_clustering.ProBatchFeatures"
      ]
    },
    {
      "page": "plot_NA_density",
      "title": "Plot intensity density by missingness",
      "topics": [
        "plot_NA_density",
        "plot_NA_density.default",
        "plot_NA_density.ProBatchFeatures"
      ]
    },
    {
      "page": "plot_NA_frequency",
      "title": "Plot missing-value frequency distribution",
      "topics": [
        "plot_NA_frequency",
        "plot_NA_frequency.default",
        "plot_NA_frequency.ProBatchFeatures"
      ]
    },
    {
      "page": "plot_NA_heatmap",
      "title": "Plot missing-value heatmap(s)",
      "topics": [
        "plot_NA_heatmap",
        "plot_NA_heatmap.default",
        "plot_NA_heatmap.ProBatchFeatures"
      ]
    },
    {
      "page": "plot_NA_intensity",
      "title": "Plot missingness versus mean intensity per feature",
      "topics": [
        "plot_NA_intensity",
        "plot_NA_intensity.default",
        "plot_NA_intensity.ProBatchFeatures"
      ]
    },
    {
      "page": "plot_PCA",
      "title": "plot PCA plot",
      "topics": [
        "plot_PCA",
        "plot_PCA.default",
        "plot_PCA.ProBatchFeatures"
      ]
    },
    {
      "page": "plot_peptide_corr_distribution",
      "title": "Create violin plot of peptide correlation distribution",
      "topics": [
        "plot_peptide_corr_distribution",
        "plot_peptide_corr_distribution.corrDF"
      ]
    },
    {
      "page": "plot_protein_corrplot",
      "title": "Peptide correlation matrix (heatmap)",
      "topics": [
        "plot_protein_corrplot"
      ]
    },
    {
      "page": "plot_PVCA",
      "title": "Plot variance distribution by variable",
      "topics": [
        "plot_PVCA",
        "plot_PVCA.default",
        "plot_PVCA.ProBatchFeatures"
      ]
    },
    {
      "page": "plot_PVCA_stacked_from_saved",
      "title": "Plot stacked PVCA results from saved CSV files",
      "topics": [
        "plot_PVCA_stacked_from_saved"
      ]
    },
    {
      "page": "plot_PVCA.df",
      "title": "plot PVCA, when the analysis is completed",
      "topics": [
        "plot_PVCA.df"
      ]
    },
    {
      "page": "plot_sample_corr_distribution",
      "title": "Create violin plot of sample correlation distribution",
      "topics": [
        "plot_sample_corr_distribution",
        "plot_sample_corr_distribution.corrDF"
      ]
    },
    {
      "page": "plot_sample_corr_heatmap",
      "title": "Sample correlation matrix (heatmap)",
      "topics": [
        "plot_sample_corr_heatmap"
      ]
    },
    {
      "page": "plot_sample_mean_or_boxplot",
      "title": "Plot per-sample mean or boxplots for initial assessment",
      "topics": [
        "plot_boxplot",
        "plot_boxplot.default",
        "plot_boxplot.ProBatchFeatures",
        "plot_sample_mean",
        "plot_sample_mean.default",
        "plot_sample_mean.ProBatchFeatures",
        "plot_sample_mean_or_boxplot"
      ]
    },
    {
      "page": "plot_split_violin_with_boxplot",
      "title": "Plot split violin plot (convenient to compare distribution before and after)",
      "topics": [
        "plot_split_violin_with_boxplot"
      ]
    },
    {
      "page": "plot_TSNE",
      "title": "Plot a t-SNE embedding of samples",
      "topics": [
        "plot_TSNE",
        "plot_TSNE.default",
        "plot_TSNE.ProBatchFeatures"
      ]
    },
    {
      "page": "plot_UMAP",
      "title": "Plot a UMAP embedding of samples",
      "topics": [
        "plot_UMAP",
        "plot_UMAP.default",
        "plot_UMAP.ProBatchFeatures"
      ]
    },
    {
      "page": "prepare_PVCA_df",
      "title": "prepare the weights of Principal Variance Components",
      "topics": [
        "prepare_PVCA_df",
        "prepare_PVCA_df.default",
        "prepare_PVCA_df.ProBatchFeatures"
      ]
    },
    {
      "page": "proBatch",
      "title": "proBatch: A package for diagnostics and correction of batch effects, primarily in proteomics",
      "topics": [
        "proBatch-package",
        "proBatch"
      ]
    },
    {
      "page": "ProBatchFeatures",
      "title": "Construct a ProBatchFeatures object from a wide matrix + sample annotation.",
      "topics": [
        "ProBatchFeatures"
      ]
    },
    {
      "page": "ProBatchFeatures_from_long",
      "title": "Construct from LONG df via proBatch::long_to_matrix",
      "topics": [
        "ProBatchFeatures_from_long"
      ]
    },
    {
      "page": "ProBatchFeatures-class",
      "title": "ProBatchFeatures: QFeatures subclass with operation log, levels/pipelines, and lazy storage",
      "topics": [
        "ProBatchFeatures-class"
      ]
    },
    {
      "page": "sample_annotation_to_colors",
      "title": "Generate colors for sample annotation",
      "topics": [
        "sample_annotation_to_colors",
        "sample_annotation_to_colors.default",
        "sample_annotation_to_colors.ProBatchFeatures"
      ]
    },
    {
      "page": "subbatch_detection",
      "title": "Detect sub-batches within batches",
      "topics": [
        "subbatch_detection"
      ]
    },
    {
      "page": "summarize_design",
      "title": "Summarise design matrix structure",
      "topics": [
        "summarize_design"
      ]
    },
    {
      "page": "transform_raw_data",
      "title": "Functions to log transform raw data before normalization and batch correction",
      "topics": [
        "log_transform_df",
        "log_transform_dm",
        "log_transform_dm.default",
        "log_transform_dm.ProBatchFeatures",
        "transform_raw_data",
        "unlog_df",
        "unlog_dm",
        "unlog_dm.default",
        "unlog_dm.ProBatchFeatures"
      ]
    },
    {
      "page": "validate_batch_design",
      "title": "Validate batch/design annotations",
      "topics": [
        "validate_batch_design"
      ]
    },
    {
      "page": "warn_unmapped_columns",
      "title": "Warn about unmapped columns",
      "topics": [
        "warn_unmapped_columns"
      ]
    }
  ],
  "_readme": "https://github.com/bioc/proBatch/raw/HEAD/README.md",
  "_rundeps": [
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    "htmltools",
    "htmlwidgets",
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    "iterators",
    "jquerylib",
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    "labeling",
    "lambda.r",
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    "pvca",
    "QFeatures",
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    "rappdirs",
    "rbibutils",
    "RColorBrewer",
    "Rcpp",
    "RcppEigen",
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    "rlang",
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    "Seqinfo",
    "snow",
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    "statmod",
    "stringi",
    "stringr",
    "SummarizedExperiment",
    "survival",
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    "wesanderson",
    "WGCNA",
    "withr",
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    "xtable",
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    "yaml"
  ],
  "_vignettes": [
    {
      "source": "proBatch.Rmd",
      "filename": "proBatch.html",
      "title": "proBatch",
      "author": "| Jelena Čuklina, Chloe H. Lee, Patrick Pedrioli and Ruedi Aebersold | Institute of Molecular Systems Biology, Department of Biology, ETH Zurich, Switzerland",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Introduction",
        "Batch effects analysis in large-scale data",
        "Analysis of large-scale data: steps before and after batch correction",
        "Preparation for the analysis",
        "Installation",
        "Preparing the data for analysis",
        "Loading the libraries",
        "Input data formats",
        "Example dataset",
        "Preparing sample and peptide annotations",
        "Defining the order of samples from running date and time",
        "Generating peptide annotation from OpenSWATH data",
        "Other utility functions",
        "Transforming the data to long or wide format",
        "Transforming the data to log scale",
        "Defining the color scheme",
        "Step-by-step workflow",
        "Initial assessment of the raw data matrix",
        "Plotting the sample mean",
        "Plotting boxplots",
        "Normalization",
        "Median normalization",
        "Quantile normalization",
        "Diagnostics of batch effects in normalized data",
        "Hierarchical clustering",
        "Principal component analysis (PCA)",
        "t-SNE and UMAP",
        "Principal variance component analysis (PVCA)",
        "Peptide-level diagnostics and spike-ins",
        "Correction of batch effects",
        "Continuous drift correction",
        "Discrete batch correction: centering, ComBat, or limmaRBE",
        "Feature-level median centering",
        "ComBat",
        "limma removeBatchEffect() (limmaRBE)",
        "Correct batch effects: universal function",
        "Quality control on batch-corrected data matrix",
        "Heatmap of selected replicate samples",
        "Correlation distribution of samples",
        "Correlation of peptide distributions within and between proteins",
        "SessionInfo",
        "Citation",
        "References"
      ],
      "created": "2019-09-30 11:13:04",
      "modified": "2026-08-01 16:27:55",
      "commits": 12
    },
    {
      "source": "proBatchFeatures.Rmd",
      "filename": "proBatchFeatures.html",
      "title": "ProBatchFeatures",
      "author": "| Yuliya Burankova | Institute for Computational Systems Biology, University of Hamburg, Germany",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Overview",
        "Setup",
        "Installation",
        "Loading required packages",
        "Loading and preparing the example dataset",
        "Constructing a ProBatchFeatures object",
        "Processing pipeline with diagnostics",
        "Step 1 - filtering features with too many missing values",
        "Step 2 - log2-transformation",
        "Step 3 - median normalization",
        "Step 4 - batch-effect correction",
        "Step 5 - assess processed assays",
        "Principal component analysis",
        "t-SNE and UMAP",
        "Hierarchical clustering",
        "Principal Variance Component Analysis (PVCA)",
        "Inspecting operation log",
        "Extracting data matrices from pbf object",
        "Session info",
        "Citation",
        "References"
      ],
      "created": "2025-09-28 17:16:39",
      "modified": "2026-08-01 16:27:55",
      "commits": 14
    }
  ],
  "_score": 6.068185861746162,
  "_indexed": true,
  "_nocasepkg": "probatch",
  "_universes": [
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  ],
  "_binaries": [
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      "r": "4.7.0",
      "os": "linux",
      "version": "2.1.3",
      "date": "2026-10-31T10:27:43.000Z",
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