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Functions105 in github.com/SingleRust/SingleRust

⨍ Functions 105◇ Types & classes 20

↓ 25 callersMethodclone

(&self)

src/shared/mod.rs:36

↓ 12 callersFunctioncombine_filters

(filter1: &[bool], filter2: &[bool])

src/memory/processing/filtering/mod.rs:271

↓ 12 callersMethodis_some

(&self)

src/shared/mod.rs:86

↓ 8 callersFunctionconvert_arrayd_to_array2_f64

(arrayd: &ArrayD<T>)

src/shared/mod.rs:313

↓ 7 callersFunctionrank_gene_groups

( adata: &IMAnnData, groupby: &str, reference: Option<&str>, groups: Option<&[&str]>, key_

src/memory/processing/diffexp/mod.rs:189

↓ 6 callersMethodsum_whole

(&self, direction: &Direction)

src/memory/statistics/mod.rs:86

↓ 4 callersFunctionarr1_conversion

Convert a 1D array between numeric types. Type-safe conversion for 1D arrays, useful for converting vectors of statistics, scores, or other single-di

src/memory/utils/mod.rs:699

↓ 4 callersFunctionarr2_conversion

Convert a 2D array between numeric types. Type-safe conversion between different numeric types for 2D arrays, commonly used when interfacing between

src/memory/utils/mod.rs:659

↓ 4 callersFunctioncreate_dataframe_from_map

Create a Polars DataFrame from a HashMap of numeric vectors. Converts analysis results stored as HashMap<String, Vec<T>> into a structured DataFrame

src/memory/utils/mod.rs:579

↓ 4 callersFunctioncreate_test_anndata

()

src/memory/processing/diffexp/mod.rs:1086

↓ 4 callersFunctionneed_conversion_target_float_type

(scalar_type: &ScalarType)

src/shared/mod.rs:456

↓ 4 callersFunctionselect_info_elem_to_indices

( elem: &SelectInfoElem, bound: usize, )

src/shared/utils/mod.rs:5

↓ 2 callersFunctioncompute_rank_gene_groups

( adata: &IMAnnData, groupby: &str, reference: Option<&str>, groups: Option<&[&str]>, meth

src/memory/processing/diffexp/mod.rs:351

↓ 2 callersFunctionconvert_to_float_if_non_float_type

Convert matrix data to floating-point format if needed for analysis. This is the main conversion function that handles in-place transformation of mat

src/memory/utils/mod.rs:176

↓ 2 callersFunctionget_group_indices

Get row indices for cells belonging to a specific group. Searches through the groupby column and returns the indices of all cells that match the spec

src/memory/processing/diffexp/mod.rs:890

↓ 2 callersMethodnonzero_whole

(&self, direction: &Direction)

src/memory/statistics/mod.rs:24

↓ 2 callersFunctionrun_differential_expression

( adata: &IMAnnData, csr_matrix: &CsrMatrix<T>, groups_to_test: &[String], reference_group: &O

src/memory/processing/diffexp/mod.rs:445

↓ 1 callersFunctionapply_correction

Apply multiple testing correction to a vector of p-values. Converts p-values to the appropriate numeric type and applies the specified correction met

src/memory/processing/diffexp/mod.rs:983

↓ 1 callersFunctioncalculate_bin_stats

Calculate mean and standard deviation of dispersions within each expression bin. For the Seurat method, this establishes the expected mean-variance r

src/memory/processing/hvg/mod.rs:293

↓ 1 callersFunctioncalculate_qc_metrics

( adata: &IMAnnData, expr_type: Option<&str>, var_type: Option<&str>, qc_vars: Option<Vec<&str

src/memory/statistics/qc.rs:340

↓ 1 callersFunctioncompute_cell_ranger_hvg

Placeholder for Cell Ranger highly variable gene detection. The Cell Ranger method follows a similar approach to Seurat but with modifications specif

src/memory/processing/hvg/mod.rs:584

↓ 1 callersFunctioncompute_seurat_hvg

Compute highly variable genes using the Seurat method. The Seurat method models the mean-variance relationship by: 1. Calculating dispersion (varianc

src/memory/processing/hvg/mod.rs:493

↓ 1 callersFunctioncompute_svr_hvg

Compute highly variable genes using Support Vector Regression (SVR). The SVR method uses machine learning to model the mean-variance relationship: 1.

src/memory/processing/hvg/mod.rs:623

↓ 1 callersFunctionconvert_to_array_f64_array

(darray: &DynArray)

src/shared/mod.rs:296

↓ 1 callersFunctioncreate_string_dataframe_from_map

Create a Polars DataFrame from a HashMap of String vectors. Specialized version of DataFrame creation for string data, commonly used for gene names,

src/memory/utils/mod.rs:620

↓ 1 callersFunctioncreate_test_anndata_three_groups

()

src/memory/processing/diffexp/mod.rs:1372

↓ 1 callersFunctiondataframe_from_csv_bytes

( bytes: &[u8], separator: u8, has_header: bool, infer_schema_length: Option<usize>, )

src/shared/utils/mod.rs:44

↓ 1 callersFunctiondescribe_obs

Calculate cell-level (observation) quality control metrics. Computes comprehensive QC statistics for each cell including expression totals, gene dete

src/memory/statistics/qc.rs:117

↓ 1 callersFunctiondescribe_var

Calculate gene-level (variable) quality control metrics. Computes QC statistics for each gene including detection rates across cells, mean expression

src/memory/statistics/qc.rs:240

↓ 1 callersFunctiondownload_resource

(url: T)

src/connectors/utils.rs:4

↓ 1 callersFunctionequal_width_binning

Create equal-width bins for gene expression levels. Divides the expression range into equal-width bins for the Seurat method. This allows modeling of

src/memory/processing/hvg/mod.rs:219

↓ 1 callersFunctionfilter_groups_to_test

Filter and validate the list of groups to test from user input. If specific groups are provided, validates they exist in the data. If no groups speci

src/memory/processing/diffexp/mod.rs:810

↓ 1 callersFunctionfit_svr

(x: &[f64], y: &[f64])

src/shared/processing/mod.rs:252

↓ 1 callersFunctiongenerate_random_mask

Generate a random boolean mask for gene selection. Creates a boolean vector where `num_random_selection` randomly chosen positions are set to `true`,

src/memory/processing/dimred/pca/mod.rs:328

↓ 1 callersFunctionget_unique_groups

Extract unique group labels from a categorical or string column in the observation metadata. This function handles different data types commonly used

src/memory/processing/diffexp/mod.rs:739

↓ 1 callersMethodis_none

(&self)

src/shared/mod.rs:78

↓ 1 callersFunctionload_resource

(name: &str, license: &str, tax_id: Option<&str>)

src/connectors/omnipath.rs:14

↓ 1 callersFunctionlog1p

(matrix: &IMArrayElement)

src/memory/processing/transformation/mod.rs:37

↓ 1 callersMethodn_top_whole

(&self, direction: &Direction, n: usize)

src/memory/statistics/mod.rs:264

↓ 1 callersFunctionnormalize_dispersions

Normalize dispersions by subtracting expected values and dividing by standard deviation. This creates z-scores for dispersion values, allowing identi

src/memory/processing/hvg/mod.rs:355

↓ 1 callersFunctionnormalize_per_bin

( log_means: &[f64], log_dispersions: &[f64], n_bins: usize, )

src/shared/processing/mod.rs:138

↓ 1 callersFunctionpostprocess_seurat_dispersions

Post-process dispersion statistics for Seurat method to handle edge cases. Handles bins with single genes where standard deviation cannot be computed

src/memory/processing/hvg/mod.rs:184

↓ 1 callersFunctionprocess_omnipath_dataframe

( mut df: DataFrame, tax_id: Option<&str>, )

src/connectors/omnipath.rs:30

↓ 1 callersFunctionread_h5ad

( path_to_file: P, scope: FileScope, enable_cache: bool, )

src/io/mod.rs:12

↓ 1 callersFunctionresolve_reference_group

Resolve the reference group specification to an actual group name or "rest" mode. Handles three cases: - Specific group name: validates the group exi

src/memory/processing/diffexp/mod.rs:850

↓ 1 callersFunctionrun_tests_for_group

( csr_matrix: &CsrMatrix<T>, group_indices: &[usize], reference_indices: &[usize], method: Tes

src/memory/processing/diffexp/mod.rs:529

↓ 1 callersFunctionstandardize_log

(x: f64, mu: f64, sigma: f64)

src/shared/processing/mod.rs:82

↓ 1 callersFunctionstandardize_log_form_vec

(vec: &[f64])

src/shared/processing/mod.rs:89

↓ 1 callersFunctionstore_results

( adata: &IMAnnData, key: &str, groups: &[String], scores: HashMap<String, Vec<f64>>, pval

src/memory/processing/diffexp/mod.rs:998

↓ 1 callersFunctionsubset_genes

Select highly variable genes based on dispersion scores and expression filters. Final step of HVG detection that applies thresholds to identify the m

src/memory/processing/hvg/mod.rs:404

↓ 1 callersMethodsum_whole_masked

(&self, direction: &Direction, mask: &[bool])

src/memory/statistics/mod.rs:126

↓ 1 callersMethodtop_segment_proportions

( &self, direction: &Direction, ns: &[usize], )

src/memory/statistics/mod.rs:282

Function_calculate_dispersion_stats

( log_dispersions: &[f64], bin_indices: &[usize], mean_bins: &[usize], )

src/shared/processing/mod.rs:353

Function_get_mean_bins

( log_means: &[f64], n_bins: usize, )

src/shared/processing/mod.rs:304

Function_normalize_by_batch

( log_means: &[f64], log_dispersions: &[f64], batch_col: &polars::prelude::Column, n_bins: usi

src/shared/processing/mod.rs:202

Function_target_type_float_need_conversion_in_memory

Check if a matrix data type requires conversion to float for processing. Determines whether the given matrix data type needs to be converted to a flo

src/memory/utils/mod.rs:109

Functioncompute_highly_variable_genes

Compute highly variable genes using the specified method and parameters. This is the main entry point for HVG detection. It analyzes the mean-varianc

src/memory/processing/hvg/mod.rs:156

Functionconstruct_network_from_panglaodb

( license: &str, tax_id: Option<&str>, features: Vec<String>, tmin: u32, )

src/connectors/omnipath.rs:66

Functionconvert_array

Convert dense ndarray between numeric types. Transforms a dense array from one numeric type to another, preserving the multidimensional structure. Ha

src/memory/utils/mod.rs:532

Functionconvert_csc_sparse_matrix

Convert CSC sparse matrix between numeric types. Transforms a Compressed Sparse Column matrix from one numeric type to another while preserving the s

src/memory/utils/mod.rs:500

Functionconvert_csr_sparse_matrix

Convert CSR sparse matrix between numeric types. Transforms a Compressed Sparse Row matrix from one numeric type to another while preserving the spar

src/memory/utils/mod.rs:468

Functionconvert_to_array_f64

(arr_data: &ArrayData)

src/shared/mod.rs:285

Functionconvert_to_array_f64_csc

( csc: &CscMatrix<T>, shape: Shape, )

src/shared/mod.rs:335

Functionconvert_to_array_f64_csc_selected

( csc: &CscMatrix<T>, shape: Shape, row_selection: &SelectInfoElem, col_selection: &SelectInfo

src/shared/mod.rs:396

Functionconvert_to_array_f64_csr

( csr: &CsrMatrix<T>, shape: Shape, )

src/shared/mod.rs:348

Functionconvert_to_array_f64_csr_selected

( csr: &CsrMatrix<T>, shape: Shape, row_selection: &SelectInfoElem, col_selection: &SelectInfo

src/shared/mod.rs:361

Functionconvert_to_array_f64_selected

( data: &ArrayData, shape: Shape, row_selection: &SelectInfoElem, col_selection: &SelectInfoEl

src/shared/mod.rs:431

Methoddefault

()

src/shared/processing/mod.rs:67

Functionget_select_info_obs

( obs_mask: Option<ArrayView<'_, bool, Ix1>>, )

src/shared/processing/mod.rs:6

Functionget_select_info_vars

( vars_mask: Option<ArrayView<'_, bool, Ix1>>, )

src/shared/processing/mod.rs:26

Functionlog1p_expression

( matrix: &IMArrayElement, precision: Option<Precision>, )

src/memory/processing/transformation/mod.rs:28

Functionmark_filter_cells

Filter cells based on various quality control metrics. This function creates a boolean mask indicating which cells pass all specified filtering crite

src/memory/processing/filtering/mod.rs:51

Functionmark_filter_genes

Filter genes based on various quality control metrics. This function creates a boolean mask indicating which genes pass all specified filtering crite

src/memory/processing/filtering/mod.rs:184

Methodmin_max_chunk

( &self, direction: &Direction, reference: (&mut Vec<T>, &mut Vec<T>), )

src/memory/statistics/mod.rs:232

Methodmin_max_chunk

( &self, direction: &Direction, reference: (&mut Vec<T>, &mut Vec<T>), )

src/backed/statistics/mod.rs:169

Methodmin_max_whole

(&self, direction: &Direction)

src/memory/statistics/mod.rs:216

Methodmin_max_whole

(&self, direction: &Direction)

src/backed/statistics/mod.rs:156

Methodnonzero_chunk

(&self, direction: &Direction, reference: &mut [T])

src/memory/statistics/mod.rs:43

Methodnonzero_chunk

(&self, direction: &Direction, reference: &mut [T])

src/backed/statistics/mod.rs:27

Methodnonzero_whole

(&self, direction: &Direction)

src/backed/statistics/mod.rs:14

Methodnonzero_whole_masked

( &self, direction: &Direction, mask: &[bool], )

src/memory/statistics/mod.rs:64

Methodnonzero_whole_masked

( &self, _direction: &Direction, _mask: &[bool], )

src/backed/statistics/mod.rs:50

Functionnormalize_expression

( matrix: &IMArrayElement, expression_target: u32, direction: &Direction, precision: Option<Pr

src/memory/processing/transformation/mod.rs:11

Functionnormalize_with_type

( matrix: &IMArrayElement, expression_target: u32, direction: &Direction, )

src/memory/processing/transformation/mod.rs:150

Functionqc_metrics

Convenient function to calculate standard QC metrics with mitochondrial genes. This is a simplified interface for the most common QC analysis in sing

src/memory/statistics/qc.rs:448

Functionrank_gene_groups_dataframe

( adata: &IMAnnData, groupby: &str, reference: Option<&str>, groups: Option<&[&str]>, meth

src/memory/processing/diffexp/mod.rs:243

Functionread_h5ad_fast_memory

(path_to_file: P)

src/io/mod.rs:33

Functionread_h5ad_memory

(path_to_file: P)

src/io/mod.rs:28

Functionrun

( adata: IMAnnData, output_dim: u8, perplexity: f32, epochs: usize, theta: f32, pp_key

src/memory/processing/dimred/tsne/mod.rs:6

Functionrun_pca_sparse_masked

( matrix: &IMArrayElement, feature_selection_method: Option<FeatureSelectionMethod>, center: Optio

src/memory/processing/dimred/pca/mod.rs:199

Methodsum_chunk

(&self, direction: &Direction, reference: &mut [T])

src/memory/statistics/mod.rs:105

Methodsum_chunk

(&self, direction: &Direction, reference: &mut [T])

src/backed/statistics/mod.rs:76

Methodsum_whole

(&self, direction: &Direction)

src/backed/statistics/mod.rs:63

Methodsum_whole_masked

(&self, _direction: &Direction, _mask: &[bool])

src/backed/statistics/mod.rs:99

Functiontest_basic_rank_genes

()

src/memory/processing/diffexp/mod.rs:1161

Functiontest_edge_cases

()

src/memory/processing/diffexp/mod.rs:1316

Functiontest_mann_whitney_method

()

src/memory/processing/diffexp/mod.rs:1236

Functiontest_multiple_groups

()

src/memory/processing/diffexp/mod.rs:1279

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