Package index • SpatialEcoTyper

Discovery of spatial ecotypes (SEs)

SpatialEcoTyper(): Identify Spatial EcoTypes from Single-cell Spatial Data (A Single Sample)

MultiSpatialEcoTyper(): Integrate Multiple Spatial Transcriptomics Datasets to Identify Conserved Spatial Ecotypes

IntegrateSpatialEcoTyper(): Integrate Multiple Spatial Transcriptomics Datasets to Identify Conserved Spatial Ecotypes

GetSpatialMetacells(): Construct Spatial Metacells from Single-Cell Spatial Data

ComputeAvgs(): Compute Cell-Type-Specific Average Expression of Spatial Clusters

SNF2(): Enhanced Similarity Network Fusion

GetPCList(): Generate Principal Component (PC) List for Spatial Microregions

GetSNList(): Construct Cell-Type-Specific Similarity Network

Integrate(): Integrate Spatial Clusters From Multiple Samples Via Similarity Network Fusion

Development of SE recovery models

Developing models for recovering spatial ecotypes from single-cell spatial data, scRNA-seq data, Visium spatial data, or bulk tumor expression data.

CreatePseudobulks(): Create Pseudo-bulk Mixtures

NMFGenerateW(): Train SE Deconvolution Model

NMFGenerateWList(): Train Cell Type-Specific NMF Models for Recovering Spatial EcoTypes

SE recovery

Recovering spatial ecotypes from single-cell spatial data, scRNA-seq data, Visium spatial data, or bulk tumor expression data.

RecoverSE(): Recovery of SEs Using Pretrained NMF Models

DeconvoluteSE(): Infer SE Abundances Using Pretrained NMF Model

NMFpredict(): Prediction Using Pretrained NMF Model

NMF (Non-negative Matrix Factorization) clustering

Clustering via NMF.

nmfClustering(): Robust Clustering via NMF (non-negative matrix factorization)

Visualization

SpatialView(): Visualize Spatial Landscape of Cells / Spots

HeatmapView(): Draw Heatmap

drawRectangleAnnotation(): Draw Rectangle Annotations

getColors(): Generate a List of Colors

Data processing

PreprocessST(): Preprocess Spatial Transcriptomics Data

AnnotateCells(): Extract Spatial Ecotype Annotations for Single Cells

Znorm(): Weighted / Unweighted Uni-variance Normalization

rankSparse(): Transform a Sparse Matrix to Rank Space (Rank Non-zeros in Each Column)

matrixMultiply(): Matrix Multiplication with Minibatching and Parallel Processing

fillspots(): Handle Missing Values

mostFrequent(): Identify the most frequent category in a vector