Cleaned up the README and related docs.

README.md

@@ -76,7 +76,7 @@ let qc_thresholds = scran.suggestRnaQcFilters(qc_metrics);
 let filtered = scran.filterCells(mat, qc_thresholds.filter(qc_metrics));
 
 // Log-normalizing.
-let normalized = scran.logNormCounts(filtered);
+let normalized = scran.normalizeCounts(filtered);
 
 // Modelling per-gene variance and selecting top HVGs. 
 let varmodel = scran.modelGeneVariances(normalized);
@@ -90,7 +90,7 @@ let index = scran.buildNeighborSearchIndex(pcs);
 
 // Performing the clustering. 
 let cluster_graph = scran.buildSnnGraph(index, { neighbors: 10 });
-let clustering = scran.clusterSnnGraph(cluster_graph);
+let clustering = scran.clusterGraph(cluster_graph);
 
 // Performing the t-SNE and UMAP.
 let tsne_res = scran.runTsne(index);
@@ -114,7 +114,7 @@ Developer notes are also available [here](docs/related/developer_notes.md).
 
 ## Links
 
-Check out [kana](https://github.com/jkanche/kana) to see how **scran.js** is used in an interactive scRNA-seq analysis application.
+Check out [kana](https://github.com/kanaverse/kana) to see how **scran.js** is used in an interactive scRNA-seq analysis application.
 
-The [**scran.chan**](https://github.com/LTLA/scran.chan) R package and [**scran**](https://github.com/LTLA/scran-cli) executable 
+The [**scrapper**](https://github.com/libscran/scrapper) R package and [**scran**](https://github.com/LTLA/scran-cli) executable 
 are based on the same C++ libraries and allow the same analysis to be performed in different environments.

docs/related/cell_type_annotation.md

@@ -18,14 +18,14 @@ Several datasets have been processed and made available [here](https://github.co
 Assuming the files have been downloaded and are available as `ArrayBuffer`s, we load the reference:
 
 ```js
-let loaded = scran.loadLabelledReferenceFromBuffers(rankbuf, markbuf, labbuf); // files 1, 2 and 3, respectively.
+let loaded = scran.loadLabelCellsReferenceFromBuffers(rankbuf, markbuf, labbuf); // files 1, 2 and 3, respectively.
 ```
 
 We then build the reference given `testfeatures`, an array with the names of the features in our test dataset;
 and `reffeatures`, an array with the names of the features in our reference (extracted from file **4**).
 
 ```js
-let built = scran.buildLabelledReference(testfeatures, loaded, reffeatures);
+let built = scran.trainLabelCellsReference(testfeatures, loaded, reffeatures);
 ```
 
 Finally, we can generate labels from our input matrix.
@@ -44,24 +44,25 @@ Advanced users can integrate cell labels from multiple references with the `inte
 Firstly, we classify our test dataset against each of the references individually:
 
 ```js
-let loaded_A = scran.loadLabelledReferenceFromBuffers(rankbuf_A, markbuf_A, labbuf_A); 
-let built_A = scran.buildLabelledReference(testfeatures, loaded_A, reffeatures_A);
+let loaded_A = scran.loadLabelCellsReferenceFromBuffers(rankbuf_A, markbuf_A, labbuf_A); 
+let built_A = scran.trainLabelCellsReference(testfeatures, loaded_A, reffeatures_A);
 let labels_A = scran.labelCells(mat, built_A);
 
-let loaded_B = scran.loadLabelledReferenceFromBuffers(rankbuf_B, markbuf_B, labbuf_B); 
-let built_B = scran.buildLabelledReference(testfeatures, loaded_B, reffeatures_B);
+let loaded_B = scran.loadLabelCellsReferenceFromBuffers(rankbuf_B, markbuf_B, labbuf_B); 
+let built_B = scran.trainLabelCellsReference(testfeatures, loaded_B, reffeatures_B);
 let labels_B = scran.labelCells(mat, built_B);
 ```
 
 We then integrate the references and the associated labels to obtain a single set of cell type calls across all references.
 More specifically, this chooses the best reference for each cell, based on each reference's best cell type call for that cell. 
 
 ```js
-let interbuilt = scran.integrateLabelledReferences(testfeatures, 
+let interbuilt = scran.integrateLabelCellsReferences(
+    testfeatures, 
     [loaded_A, loaded_B], 
     [reffeatures_A, reffeatures_B], 
     [built_A, built_B]
 );
 
-let interlabels = scran.integrateCellLabels(mat, [labels_A, labels_B], inter);
+let interlabels = scran.integrateLabelCells(mat, [labels_A, labels_B], inter);
 ```

js/modelGeneVariances.js

@@ -61,7 +61,6 @@ export class ModelGeneVariancesResults {
      * @return {Float64Array|Float64WasmArray} Array of length equal to the number of genes,
      * containing the fitted value of the mean-variance trend for the specified `block`
      * (or the average across all blocks, if `block = null`).
-     * Alternatively `null`, if `fillable = false` and the array was not already filled.
      */
     fitted(options = {}) {
         const { block = null, copy = true, ...others } = options;

js/suggestRnaQcFilters.js

@@ -146,7 +146,6 @@ export function suggestRnaQcFilters(metrics, options = {}) {
 
 /**
  * Create an empty {@linkplain SuggestRnaQcFiltersResults} object, to be filled with custom results.
- * Note that filling requires use of `fillable: true` in the various getters to obtain a writeable memory view.
  *
  * @param {number} numberOfSubsets Number of feature subsets.
  * @param {number} numberOfBlocks Number of blocks in the dataset.