diff --git a/js/MultiMatrix.js b/js/MultiMatrix.js
index c52d9986..f6ccaa10 100644
--- a/js/MultiMatrix.js
+++ b/js/MultiMatrix.js
@@ -14,7 +14,10 @@ export class MultiMatrix {
      * Each matrix should correspond to a different modality, named according to its key.
      * All matrices should have data for the same set of cells, i.e., same number of columns.
      */
-    constructor({ store = {} } = {}) {
+    constructor(options = {}) {
+        const { store = {}, ...others } = options;
+        utils.checkOtherOptions(others);
+
         this.#store = store;
         this.#ncols = null;
 
diff --git a/js/ScranMatrix.js b/js/ScranMatrix.js
index 6ce0e161..a6f9327d 100644
--- a/js/ScranMatrix.js
+++ b/js/ScranMatrix.js
@@ -82,7 +82,10 @@ export class ScranMatrix {
      * If `buffer` was supplied, the returned array is a view into it.
      * Note that this may be invalidated on the next allocation on the Wasm heap.
      */
-    row(i, { buffer = null } = {}) {
+    row(i, options = {}) {
+        let { buffer = null, ...others } = options;
+        utils.checkOtherOptions(others);
+
         if (buffer != null) {
             this.#matrix.row(i, buffer.offset);
             return buffer.array();
@@ -111,7 +114,10 @@ export class ScranMatrix {
      * If `buffer` was supplied, the returned array is a view into it.
      * Note that this may be invalidated on the next allocation on the Wasm heap.
      */
-    column(i, { buffer = null } = {}) {
+    column(i, options = {}) {
+        let { buffer = null, ...others } = options;
+        utils.checkOtherOptions(others);
+
         if (buffer != null) {
             this.#matrix.column(i, buffer.offset);
             return buffer.array();
diff --git a/js/aggregateAcrossCells.js b/js/aggregateAcrossCells.js
index 681efc0b..1da4bd4f 100644
--- a/js/aggregateAcrossCells.js
+++ b/js/aggregateAcrossCells.js
@@ -39,7 +39,9 @@ export class AggregateAcrossCellsResults {
      * @return {Float64Array|Float64WasmArray} Array of length equal to the number of genes, containing the per-gene sum of values across across all cells in the specified `group`.
      * If `average = true` in {@linkcode aggregateAcrossCells}, each element is the mean value instead.
      */
-    groupSums(group, { copy = true } = {}) {
+    groupSums(group, options = {}) {
+        const { copy = true, ...others } = options;
+        utils.checkOtherOptions(others);
         return utils.possibleCopy(this.#results.group_sums(group), copy);
     }
 
@@ -57,7 +59,9 @@ export class AggregateAcrossCellsResults {
      * If `average = true` in {@linkcode aggregateAcrossCells}, each element is the mean value instead.
      * If `buffer` is supplied, the function returns `buffer` if `asTypedArray = false`, or a view on `buffer` if `asTypedArray = true`.
      */
-    allSums({ asTypedArray = true, buffer = null } = {}) {
+    allSums(options = {}) {
+        let { asTypedArray = true, buffer = null, ...others } = options;
+        utils.checkOtherOptions(others);
         let tmp = null;
 
         try {
@@ -83,7 +87,9 @@ export class AggregateAcrossCellsResults {
      * @return {Float64Array|Float64WasmArray} Array of length equal to the number of genes, containing the number of cells with detected expression for each gene in the specified `group`.
      * If `average = true` in {@linkcode aggregateAcrossCells}, each element is the proportion of detected cells instead.
      */
-    groupDetected(group, { copy = true } = {}) {
+    groupDetected(group, options = {}) {
+        const { copy = true, ...others } = options;
+        utils.checkOtherOptions(others);
         return utils.possibleCopy(this.#results.group_detected(group), copy);
     }
 
@@ -100,7 +106,9 @@ export class AggregateAcrossCellsResults {
      * If `average = true` in {@linkcode aggregateAcrossCells}, each element is the proportion of detected cells instead.
      * If `buffer` is supplied, the function returns `buffer` if `asTypedArray = false`, or a view on `buffer` if `asTypedArray = true`.
      */
-    allDetected({ asTypedArray = true, buffer = null } = {}) {
+    allDetected(options = {}) {
+        let { asTypedArray = true, buffer = null, ...others } = options;
+        utils.checkOtherOptions(others);
         let tmp = null;
 
         try {
@@ -144,7 +152,9 @@ export class AggregateAcrossCellsResults {
  *
  * @return {AggregateAcrossCellsResults} Object containing the aggregation results.
  */
-export function aggregateAcrossCells(x, groups, { average = false, numberOfThreads = null } = {}) {
+export function aggregateAcrossCells(x, groups, options = {}) {
+    const { average = false, numberOfThreads = null, ...others } = options;
+    utils.checkOtherOptions(others);
     var group_data;
     var output;
     let nthreads = utils.chooseNumberOfThreads(numberOfThreads);
diff --git a/js/block.js b/js/block.js
index 59dc234c..f98dc84b 100644
--- a/js/block.js
+++ b/js/block.js
@@ -20,7 +20,9 @@ import * as fac from "./factorize.js";
  *
  * If `buffer` was supplied, it is used as the return value. 
  */
-export function createBlock(ncells, { buffer = null } = {}) {
+export function createBlock(ncells, options = {}) {
+    let { buffer = null, ...other } = options;
+    utils.checkOtherOptions(other);
     let total = 0;
     ncells.forEach(x => { total += x; });
 
@@ -62,7 +64,9 @@ export function subsetBlock(x, subset, { filter = null, buffer = null } = {}) {
 }
 
 // Soft-deprecated, just use subsetFactor().
-export function filterBlock(x, filter, { buffer = null } = {}) {
+export function filterBlock(x, filter, options = {}) {
+    let { buffer = null, ...other } = options;
+    utils.checkOtherOptions(other);
     return subsetBlock(x, filter, { buffer: buffer, filter: true });
 }
 
diff --git a/js/buildSnnGraph.js b/js/buildSnnGraph.js
index c54cc81c..95416377 100644
--- a/js/buildSnnGraph.js
+++ b/js/buildSnnGraph.js
@@ -52,7 +52,9 @@ export class BuildSnnGraphResults {
  *
  * @return {BuildSnnGraphResults} Object containing the graph.
  */
-export function buildSnnGraph(x, { scheme = "rank", neighbors = 10, numberOfThreads = null } = {}) {
+export function buildSnnGraph(x, options = {}) {
+    const { scheme = "rank", neighbors = 10, numberOfThreads = null, ...others } = options;
+    utils.checkOtherOptions(others);
     var output;
     var my_neighbors;
     let nthreads = utils.chooseNumberOfThreads(numberOfThreads);
diff --git a/js/chooseHvgs.js b/js/chooseHvgs.js
index b28e2804..eae19879 100644
--- a/js/chooseHvgs.js
+++ b/js/chooseHvgs.js
@@ -16,9 +16,12 @@ import { ModelGeneVariancesResults } from "./modelGeneVariances.js";
  * @return {Uint8WasmArray} Array of length equal to the number of genes.
  * Highly variable genes are marked with a value of 1 and all other genes have values of zero.
  */
-export function chooseHvgs(x, { number = 4000, minimum = 0 } = {}) {
+export function chooseHvgs(x, options = {}) {
+    const { number = 4000, minimum = 0, ...others } = options;
+    utils.checkOtherOptions(others);
     let stat;
     let chosen;
+
     try {
         if (x instanceof ModelGeneVariancesResults) {
             stat = x.residuals({ copy: "view" });
@@ -27,11 +30,13 @@ export function chooseHvgs(x, { number = 4000, minimum = 0 } = {}) {
         }
         chosen = utils.createUint8WasmArray(stat.length);
         wasm.call(module => module.choose_highly_variable_genes(stat.length, stat.offset, chosen.offset, number, minimum));
+
     } catch (e) {
         chosen.free();
         throw e;
     } finally {
         stat.free();
     }
+
     return chosen;
 }
diff --git a/js/clusterGraph.js b/js/clusterGraph.js
index 340c5368..6b4e2fec 100644
--- a/js/clusterGraph.js
+++ b/js/clusterGraph.js
@@ -10,7 +10,7 @@ export class ClusterMultilevelResults {
     #id;
     #results;
 
-    constructor(id, raw, filled = true) {
+    constructor(id, raw) {
         this.#id = id;
         this.#results = raw;
     }
@@ -36,7 +36,9 @@ export class ClusterMultilevelResults {
      *
      * @return {number} The modularity at the specified level.
      */
-    modularity({ level = null } = {}) {
+    modularity(options = {}) {
+        let { level = null, ...others } = options;
+        utils.checkOtherOptions(others);
         if (level == null) {
             level = this.bestLevel();
         }
@@ -51,7 +53,9 @@ export class ClusterMultilevelResults {
      *
      * @return {Int32Array|Int32WasmArray} Array containing the cluster membership for each cell.
      */
-    membership({ level = null, copy = true } = {}) {
+    membership(options = {}) {
+        let { level = null, copy = true, ...others } = options;
+        utils.checkOtherOptions(others);
         if (level == null) {
             level = -1;
         }
@@ -79,7 +83,7 @@ export class ClusterWalktrapResults {
     #id;
     #results;
 
-    constructor(id, raw, filled = true) {
+    constructor(id, raw) {
         this.#id = id;
         this.#results = raw;
     }
@@ -98,7 +102,9 @@ export class ClusterWalktrapResults {
      * Set to `null` to obtain the largest modularity across all merge steps.
      * @return {number} The modularity at the specified merge step, or the maximum modularity across all merge steps.
      */
-    modularity({ at = null } = {}) {
+    modularity(options = {}) {
+        let { at = null, ...others } = options;
+        utils.checkOtherOptions(others);
         if (at === null) {
             at = -1;
         }
@@ -110,7 +116,9 @@ export class ClusterWalktrapResults {
      * @param {boolean|string} [options.copy=true] - Whether to copy the results from the Wasm heap, see {@linkcode possibleCopy}.
      * @return {Int32Array|Int32WasmArray} Array containing the cluster membership for each cell.
      */
-    membership({ copy = true } = {}) {
+    membership(options = {}) {
+        const { copy = true, ...others } = options;
+        utils.checkOtherOptions(others);
         return utils.possibleCopy(this.#results.membership(), copy);
     }
 
@@ -157,7 +165,9 @@ export class ClusterLeidenResults {
      *
      * @return {(Int32Array|Int32WasmArray)} Array containing the cluster membership for each cell.
      */
-    membership({ copy = true } = {}) {
+    membership(options = {}) {
+        const { copy = true, ...others } = options;
+        utils.checkOtherOptions(others);
         return utils.possibleCopy(this.#results.membership(), copy);
     }
 
@@ -193,15 +203,18 @@ export class ClusterLeidenResults {
  * @return {ClusterMultiLevelResults|ClusterWalktrapResults|ClusterLeidenResults} Object containing the clustering results.
  * The class of this object depends on the choice of `method`.
  */
-export function clusterGraph(x, { 
-    method = "multilevel", 
-    multiLevelResolution = 1, 
-    leidenResolution = 1, 
-    leidenModularityObjective = false,
-    walktrapSteps = 4
-} = {}) {
-    var output;
+export function clusterGraph(x, options = {}) {
+    const { 
+        method = "multilevel", 
+        multiLevelResolution = 1, 
+        leidenResolution = 1, 
+        leidenModularityObjective = false,
+        walktrapSteps = 4,
+        ...others
+    } = options;
+    utils.checkOtherOptions(others);
 
+    var output;
     try {
         if (method == "multilevel") {
             output = gc.call(
diff --git a/js/clusterKmeans.js b/js/clusterKmeans.js
index 2eafad66..cc7055a0 100644
--- a/js/clusterKmeans.js
+++ b/js/clusterKmeans.js
@@ -10,7 +10,7 @@ export class ClusterKmeansResults {
     #id;
     #results;
 
-    constructor(id, raw, filled = true) {
+    constructor(id, raw) {
         this.#results = raw;
         this.#id = id;
     }
@@ -34,7 +34,9 @@ export class ClusterKmeansResults {
      * @param {boolean|string} [options.copy=true] - Whether to copy the results from the Wasm heap, see {@linkcode possibleCopy}.
      * @return {Int32Array|Int32WasmArray} Array containing the cluster assignment for each cell.
      */
-    clusters({ copy = true } = {}) {
+    clusters(options = {}) {
+        const { copy = true, ...others } = options;
+        utils.checkOtherOptions(others);
         return utils.possibleCopy(this.#results.clusters(), copy);
     }
 
@@ -43,7 +45,9 @@ export class ClusterKmeansResults {
      * @param {boolean|string} [options.copy=true] - Whether to copy the results from the Wasm heap, see {@linkcode possibleCopy}.
      * @return {Int32Array|Int32WasmArray} Array containing the number of cells in each cluster.
      */
-    sizes({ copy = true } = {}) {
+    sizes(options = {}) {
+        const { copy = true, ...others } = options;
+        utils.checkOtherOptions(others);
         return utils.possibleCopy(this.#results.cluster_sizes(), copy);
     }
 
@@ -53,7 +57,9 @@ export class ClusterKmeansResults {
      * @return {Float64Array|Float64WasmArray} Array containing the cluster centers in column-major format,
      * where rows are dimensions and columns are the clusters.
      */
-    centers({ copy = true } = {}) {
+    centers(options = {}) {
+        const { copy = true, ...others } = options;
+        utils.checkOtherOptions(others);
         return utils.possibleCopy(this.#results.centers(), copy);
     }
 
@@ -88,7 +94,7 @@ export class ClusterKmeansResults {
  * Cluster cells using k-means.
  * A variety of initialization and refinement algorithms can be used here, see the [**kmeans** documentation](https://github.com/LTLA/CppKmeans) for more details.
  *
-* @param {(RunPcaResults|Float64WasmArray|Array|TypedArray)} x - Numeric coordinates of each cell in the dataset.
+ * @param {(RunPcaResults|Float64WasmArray|Array|TypedArray)} x - Numeric coordinates of each cell in the dataset.
  * For array inputs, this is expected to be in column-major format where the rows are the variables and the columns are the cells.
  * For a {@linkplain RunPcaResults} input, we extract the principal components.
  * @param {number} clusters Number of clusters to create.
@@ -115,18 +121,22 @@ export class ClusterKmeansResults {
  *
  * @return {ClusterKmeansResults} Object containing the clustering results.
  */
-export function clusterKmeans(x, clusters, { 
-    numberOfDims = null, 
-    numberOfCells = null, 
-    initMethod = "var-part", 
-    initSeed = 5768, 
-    initVarPartSizeAdjust = 1, 
-    initVarPartOptimize = true, 
-    refineMethod = "hartigan-wong",
-    refineLloydIterations = 100,
-    refineHartiganWongIterations = 10,
-    numberOfThreads = null 
-} = {}) {
+export function clusterKmeans(x, clusters, options = {}) {
+    let { 
+        numberOfDims = null, 
+        numberOfCells = null, 
+        initMethod = "var-part", 
+        initSeed = 5768, 
+        initVarPartSizeAdjust = 1, 
+        initVarPartOptimize = true, 
+        refineMethod = "hartigan-wong",
+        refineLloydIterations = 100,
+        refineHartiganWongIterations = 10,
+        numberOfThreads = null,
+        ...others
+    } = options;
+    utils.checkOtherOptions(others);
+
     var buffer;
     var output;
     let nthreads = utils.chooseNumberOfThreads(numberOfThreads);
diff --git a/js/computeClrm1Factors.js b/js/computeClrm1Factors.js
index 131547dd..e4f054b6 100644
--- a/js/computeClrm1Factors.js
+++ b/js/computeClrm1Factors.js
@@ -18,7 +18,9 @@ import * as wa from "wasmarrays.js";
  * Note that the factors are not centered and should be passed to {@linkcode centerSizeFactors} before calling {@linkcode normalizeCounts}.
  * If `buffer` is supplied, the function returns `buffer` if `asTypedArray = false`, or a view on `buffer` if `asTypedArray = true`.
  */
-export function computeClrm1Factors(x, { asTypedArray = true, buffer = null, priorCount = 10, numberOfThreads = null } = {}) {
+export function computeClrm1Factors(x, options = {}) {
+    let { asTypedArray = true, buffer = null, priorCount = 10, numberOfThreads = null, ...others } = options;
+    utils.checkOtherOptions(others);
     var local_buffer = null;
     let nthreads = utils.chooseNumberOfThreads(numberOfThreads);
 
diff --git a/js/delayed.js b/js/delayed.js
index 29830c61..b8216ca6 100644
--- a/js/delayed.js
+++ b/js/delayed.js
@@ -21,7 +21,9 @@ import * as wasm from "./wasm.js";
  * @return {ScranMatrix} A ScranMatrix containing the delayed arithmetic operation on `x`.
  * If `inPlace = true`, this is a reference to `x`, otherwise it is a new ScranMatrix.
  */
-export function delayedArithmetic(x, operation, value, { right = true, along = "row", inPlace = false } = {}) {
+export function delayedArithmetic(x, operation, value, options = {}) {
+    const { right = true, along = "row", inPlace = false, ...others } = options;
+    utils.checkOtherOptions(others);
     let xcopy;
     let vbuffer;
     let target;
@@ -68,7 +70,9 @@ export function delayedArithmetic(x, operation, value, { right = true, along = "
  * @return {ScranMatrix} A ScranMatrix containing the delayed math operation on `x`.
  * If `inPlace = true`, this is a reference to `x`, otherwise it is a new ScranMatrix.
  */
-export function delayedMath(x, operation, { logBase = null, inPlace = false } = {}) {
+export function delayedMath(x, operation, options = {}) {
+    let { logBase = null, inPlace = false, ...others } = options;
+    utils.checkOtherOptions(others);
     let xcopy;
     let target;
 
@@ -104,7 +108,9 @@ export function delayedMath(x, operation, { logBase = null, inPlace = false } =
  * @return {ScranMatrix} A ScranMatrix containing the transposition of `x`.
  * If `inPlace = true`, this is a reference to `x`, otherwise it is a new ScranMatrix.
  */
-export function transpose(x, { inPlace = false } = {}) {
+export function transpose(x, options = {}) {
+    const { inPlace = false, ...others } = options;
+    utils.checkOtherOptions(others);
     let xcopy;
     let target;
 
diff --git a/js/factorize.js b/js/factorize.js
index d1597625..7bed65fd 100644
--- a/js/factorize.js
+++ b/js/factorize.js
@@ -34,7 +34,9 @@ import * as utils from "./utils.js";
  *
  * If `buffer` was supplied, it is used as the value of the `ids` property.
  */
-export function convertToFactor(x, { asWasmArray = true, buffer = null, levels = null, action = "error", placeholder = -1 } = {}) {
+export function convertToFactor(x, options = {}) {
+    let { asWasmArray = true, buffer = null, levels = null, action = "error", placeholder = -1, ...others } = options;
+    utils.checkOtherOptions(others);
     let local_buffer;
 
     let failure;
@@ -184,7 +186,10 @@ export function dropUnusedLevels(x) {
  * @return `x` is modified by reference such that `x.levels` is set to `newLevels`.
  * `x.ids` is updated so that the indices now refer to the appropriate value in `newLevels`.
  */
-export function resetLevels(x, newLevels, { action = "error", placeholder = -1 } = {}) {
+export function resetLevels(x, newLevels, options = {}) {
+    const { action = "error", placeholder = -1, ...others } = options;
+    utils.checkOtherOptions(others);
+
     let mapping = new Map;
     for (var i = 0; i < newLevels.length; i++) {
         mapping.set(newLevels[i], i);
@@ -275,7 +280,10 @@ export function resetLevels(x, newLevels, { action = "error", placeholder = -1 }
  *
  * If `buffer` is supplied, the returned `ids` will be set to `buffer`.
  */
-export function subsetFactor(x, subset, { drop = true, filter = null, buffer = null } = {}) {
+export function subsetFactor(x, subset, options = {}) {
+    let { drop = true, filter = null, buffer = null, ...others } = options;
+    utils.checkOtherOptions(others);
+
     let output = { ids: null, levels: x.levels };
 
     if (x.ids instanceof wa.WasmArray) {
diff --git a/js/findNearestNeighbors.js b/js/findNearestNeighbors.js
index 1ff71373..b1eac503 100644
--- a/js/findNearestNeighbors.js
+++ b/js/findNearestNeighbors.js
@@ -63,7 +63,9 @@ export class BuildNeighborSearchIndexResults {
  *
  * @return {BuildNeighborSearchIndexResults} Index object to use for neighbor searches.
  */
-export function buildNeighborSearchIndex(x, { numberOfDims = null, numberOfCells = null, approximate = true } = {}) {
+export function buildNeighborSearchIndex(x, options = {}) {
+    let { numberOfDims = null, numberOfCells = null, approximate = true, ...others } = options;
+    utils.checkOtherOptions(others);
     var buffer;
     var output;
 
@@ -156,7 +158,10 @@ export class FindNearestNeighborsResults {
      *
      * If only some of the arguments are non-`null`, an error is raised.
      */
-    serialize({ runs = null, indices = null, distances = null } = {}) {
+    serialize(options = {}) {
+        const { runs = null, indices = null, distances = null, ...others } = options;
+        utils.checkOtherOptions(others);
+
         var copy = (runs === null) + (indices === null) + (distances === null);
         if (copy != 3 && copy != 0) {
             throw new Error("either all or none of 'runs', 'indices' and 'distances' can be 'null'");
@@ -261,7 +266,9 @@ export class FindNearestNeighborsResults {
  *
  * @return {FindNearestNeighborsResults} Object containing the search results.
  */
-export function findNearestNeighbors(x, k, { numberOfThreads = null } = {}) {
+export function findNearestNeighbors(x, k, options = {}) {
+    const { numberOfThreads = null, ...others } = options;
+    utils.checkOtherOptions(others);
     let nthreads = utils.chooseNumberOfThreads(numberOfThreads);
     return gc.call(
         module => module.find_nearest_neighbors(x.index, k, nthreads),
diff --git a/js/guessFeatures.js b/js/guessFeatures.js
index d1aa823c..beb32d48 100644
--- a/js/guessFeatures.js
+++ b/js/guessFeatures.js
@@ -1,3 +1,5 @@
+import * as utils from "./utils.js";
+
 /**
  * Guess the identity of the features from their names.
  *
@@ -16,7 +18,10 @@
  *   This can either be `"ensembl"` or `"symbol"`.
  * - `confidence`: the percentage of entries in `x` that are consistent with the inferred identity.
  */
-export function guessFeatures(features, { forceTaxonomy = false } = {}) {
+export function guessFeatures(features, options = {}) {
+    const { forceTaxonomy = false, ...others } = options;
+    utils.checkOtherOptions(others);
+
     let ntotal = features.length;
     let early_threshold = Math.ceil(ntotal / 2);
     let format = payload => {
diff --git a/js/hdf5.js b/js/hdf5.js
index 5ee72aeb..e3bd7135 100644
--- a/js/hdf5.js
+++ b/js/hdf5.js
@@ -157,7 +157,10 @@ export class H5Base {
         return output;
     }
 
-    #create_attribute(attr, type, shape, { maxStringLength = null, levels = null } = {}) { // internal use only.
+    #create_attribute(attr, type, shape, options = {}) { // internal use only.
+        const { maxStringLength = null, levels = null, ...others } = options;
+        utils.checkOtherOptions(others);
+
         let shape_arr = utils.wasmifyArray(shape, "Int32WasmArray");
         try {
             if (type == "String") {
@@ -200,7 +203,10 @@ export class H5Base {
      * If supplied, `x` should be an array of integers that index into `levels`.
      * Alternatively, `levels` may be `null`, in which case `x` should be an array of strings that is used to infer `levels`.
      */
-    writeAttribute(attr, type, shape, x, { maxStringLength = null, levels = null } = {}) {
+    writeAttribute(attr, type, shape, x, options = {}) {
+        let { maxStringLength = null, levels = null, ...others } = options;
+        utils.checkOtherOptions(others);
+
         if (x === null) {
             throw new Error("cannot write 'null' to HDF5"); 
         }
@@ -259,8 +265,11 @@ export class H5Group extends H5Base {
     /**
      * @param {string} file - Path to the HDF5 file.
      * @param {string} name - Name of the group inside the file.
+     * @param {object} [options={}] - Optional parameters, for internal use only.
      */
-    constructor(file, name, { newlyCreated = false } = {}) {
+    constructor(file, name, options = {}) {
+        const { newlyCreated = false, ...others } = options;
+        utils.checkOtherOptions(others);
         super(file, name);
 
         if (newlyCreated) {
@@ -369,9 +378,11 @@ export class H5Group extends H5Base {
      * @return {H5DataSet} A dataset of the specified type and shape is created as an immediate child of the current group.
      * A {@linkplain H5DataSet} object is returned representing this new dataset.
      */
-    createDataSet(name, type, shape, { maxStringLength = 10, levels = null, compression = 6, chunks = null } = {}) {
-        let new_name = this.#child_name(name);
+    createDataSet(name, type, shape, options = {}) {
+        const { maxStringLength = 10, levels = null, compression = 6, chunks = null, ...others } = options;
+        utils.checkOtherOptions(others);
 
+        let new_name = this.#child_name(name);
         let shape_arr;
         let chunk_arr; 
         try {
@@ -433,7 +444,10 @@ export class H5Group extends H5Base {
      * The same dataset is then filled with the contents of `x`.
      * A {@linkplain H5DataSet} object is returned representing this new dataset.
      */
-     writeDataSet(name, type, shape, x, { levels = null, compression = 6, chunks = null, cache = false } = {}) {
+     writeDataSet(name, type, shape, x, options = {}) {
+        const { levels = null, compression = 6, chunks = null, cache = false, ...others } = options;
+        utils.checkOtherOptions(others);
+
         if (x === null) {
             throw new Error("cannot write 'null' to HDF5"); 
         }
@@ -477,6 +491,7 @@ export class H5Group extends H5Base {
 export class H5File extends H5Group {
     /**
      * @param {string} file - Path to the HDF5 file.
+     * @param {object} [options={}] - Further options to pass to the {@linkplain H5Group} constructor.
      */
     constructor(file, options = {}) {
         super(file, "/", options);
@@ -553,7 +568,9 @@ export class H5DataSet extends H5Base {
      * @param {boolean} [options.load=false] - Whether or not to load the contents of the dataset in the constructor.
      * If `false`, the contents can be loaded later with {@linkcode H5DataSet#load load}.
      */
-    constructor(file, name, { load = false, newlyCreated = false, shape = null, type = null, values = null } = {}) {
+    constructor(file, name, options = {}) {
+        const { load = false, newlyCreated = false, shape = null, type = null, values = null, ...others } = options;
+        utils.checkOtherOptions(others);
         super(file, name);
 
         if (newlyCreated) {
@@ -659,7 +676,7 @@ export class H5DataSet extends H5Base {
     }
 
     /**
-     * @param {(Array|TypedArray|number|string)} x - Values to write to the dataset.
+     * @param {Array|TypedArray|number|string} x - Values to write to the dataset.
      * This should be of length equal to the product of {@linkcode H5DataSet#shape shape};
      * unless `shape` is empty, in which case it should either be of length 1, or a single number or string.
      * @param {object} [options={}] - Optional parameters.
@@ -668,11 +685,13 @@ export class H5DataSet extends H5Base {
      * @return `x` is written to the dataset on file.
      * No return value is provided.
      */
-    write(x, { cache = false } = {}) {
+    write(x, options = {}) {
+        const { cache = false, ...others } = options;
+        utils.checkOtherOptions(others);
+
         if (x === null) {
             throw new Error("cannot write 'null' to HDF5"); 
         }
-
         x = check_shape(x, this.shape);
 
         if (this.type == "String") {
@@ -747,13 +766,17 @@ function extract_names(host, output, recursive = true) {
  * these nested objects are empty if `recursive = false`.
  * HDF5 datasets are represented by strings specifying the data type - i.e., `"integer"`, `"float"`, `"string"` or `"other"`.
  */
-export function extractHdf5ObjectNames (path, { group = "", recursive = true } = {}) {
+export function extractHdf5ObjectNames(path, options = {}) {
+    const { group = "", recursive = true, ...others } = options;
+    utils.checkOtherOptions(others);
+
     var src;
     if (group == "") {
         src = new H5File(path);
     } else {
         src = new H5Group(path, group);
     }
+
     var output = {};
     extract_names(src, output, recursive);
     return output;
diff --git a/js/hypergeometricTest.js b/js/hypergeometricTest.js
index b9674870..4d660e35 100644
--- a/js/hypergeometricTest.js
+++ b/js/hypergeometricTest.js
@@ -23,7 +23,10 @@ import * as wasm from "./wasm.js";
  * The i-th entry contains the p-value for enrichment computed using the i-th entry of each supplied array. 
  * If `buffer` is supplied, the function returns `buffer` if `asTypedArray = false`, or a view on `buffer` if `asTypedArray = true`.
  */
-export function hypergeometricTest(markersInSet, numberOfMarkers, geneSetSize, numberOfGenes, { asTypedArray = true, buffer = null, log = false, numberOfThreads = null } = {}) {
+export function hypergeometricTest(markersInSet, numberOfMarkers, geneSetSize, numberOfGenes, options = {}) {
+    let { asTypedArray = true, buffer = null, log = false, numberOfThreads = null, ...others } = options;
+    utils.checkOtherOptions(others);
+
     let markersInSet_data;
     let numberOfMarkers_data;
     let geneSetSize_data;
diff --git a/js/initializeSparseMatrixFromArrays.js b/js/initializeSparseMatrixFromArrays.js
index 99a40b5b..aae1c893 100644
--- a/js/initializeSparseMatrixFromArrays.js
+++ b/js/initializeSparseMatrixFromArrays.js
@@ -14,7 +14,10 @@ import { ScranMatrix } from "./ScranMatrix.js";
  *
  * @return {ScranMatrix} Matrix containing dense data.
  */
-export function initializeDenseMatrixFromDenseArray(numberOfRows, numberOfColumns, values, { forceInteger = false } = {}) {
+export function initializeDenseMatrixFromDenseArray(numberOfRows, numberOfColumns, values, options = {}) {
+    const { forceInteger = false, ...others } = options;
+    utils.checkOtherOptions(others);
+
     var val_data; 
     var output;
 
@@ -61,7 +64,10 @@ export function initializeDenseMatrixFromDenseArray(numberOfRows, numberOfColumn
  *
  * @return {ScranMatrix} Matrix containing sparse data.
  */
-export function initializeSparseMatrixFromDenseArray(numberOfRows, numberOfColumns, values, { forceInteger = true, layered = true } = {}) {
+export function initializeSparseMatrixFromDenseArray(numberOfRows, numberOfColumns, values, options = {}) {
+    const { forceInteger = true, layered = true, ...others } = options;
+    utils.checkOtherOptions(others);
+
     var val_data; 
     var output;
 
@@ -115,7 +121,10 @@ export function initializeSparseMatrixFromDenseArray(numberOfRows, numberOfColum
  *
  * @return {ScranMatrix} Matrix containing sparse data.
  */ 
-export function initializeSparseMatrixFromSparseArrays(numberOfRows, numberOfColumns, values, indices, pointers, { byRow = true, forceInteger = true, layered = true } = {}) {
+export function initializeSparseMatrixFromSparseArrays(numberOfRows, numberOfColumns, values, indices, pointers, options = {}) {
+    const { byRow = true, forceInteger = true, layered = true, ...others } = options;
+    utils.checkOtherOptions(others);
+
     var val_data;
     var ind_data;
     var indp_data;
diff --git a/js/initializeSparseMatrixFromHdf5.js b/js/initializeSparseMatrixFromHdf5.js
index 11e1c21b..326145f4 100644
--- a/js/initializeSparseMatrixFromHdf5.js
+++ b/js/initializeSparseMatrixFromHdf5.js
@@ -25,7 +25,10 @@ import { ScranMatrix } from "./ScranMatrix.js";
  *
  * @return {ScranMatrix} Matrix containing sparse data.
  */
-export function initializeSparseMatrixFromHdf5(file, name, { forceInteger = true, layered = true, subsetRow = null, subsetColumn = null } = {}) {
+export function initializeSparseMatrixFromHdf5(file, name, options = {}) {
+    const { forceInteger = true, layered = true, subsetRow = null, subsetColumn = null, ...others } = options;
+    utils.checkOtherOptions(others);
+
     const details = extractHdf5MatrixDetails(file, name);
     if (details.format == "dense") {
         return initializeSparseMatrixFromHdf5Dataset(file, name, { forceInteger, layered, subsetRow, subsetColumn });
@@ -103,7 +106,10 @@ function processSubsets(subsetRow, subsetColumn, FUN) {
  *
  * @return {ScranMatrix} Matrix containing sparse data.
  */
-export function initializeSparseMatrixFromHdf5Dataset(file, name, { transposed = true, forceInteger = true, layered = true, subsetRow = null, subsetColumn = null } = {}) {
+export function initializeSparseMatrixFromHdf5Dataset(file, name, options = {}) {
+    const { transposed = true, forceInteger = true, layered = true, subsetRow = null, subsetColumn = null, ...others } = options;
+    utils.checkOtherOptions(others);
+
     return processSubsets(
         subsetRow,
         subsetColumn, 
@@ -147,7 +153,10 @@ export function initializeSparseMatrixFromHdf5Dataset(file, name, { transposed =
  *
  * @return {ScranMatrix} Matrix containing sparse data.
  */
-export function initializeSparseMatrixFromHdf5Group(file, name, numberOfRows, numberOfColumns, byRow, { forceInteger = true, layered = true, subsetRow = null, subsetColumn = null } = {}) {
+export function initializeSparseMatrixFromHdf5Group(file, name, numberOfRows, numberOfColumns, byRow, options = {}) {
+    const { forceInteger = true, layered = true, subsetRow = null, subsetColumn = null, ...others } = options;
+    utils.checkOtherOptions(others);
+
     return processSubsets(
         subsetRow,
         subsetColumn, 
diff --git a/js/initializeSparseMatrixFromMatrixMarket.js b/js/initializeSparseMatrixFromMatrixMarket.js
index efd03e68..fa84f08d 100644
--- a/js/initializeSparseMatrixFromMatrixMarket.js
+++ b/js/initializeSparseMatrixFromMatrixMarket.js
@@ -18,7 +18,10 @@ import { ScranMatrix } from "./ScranMatrix.js";
  *
  * @return {ScranMatrix} Matrix containing sparse data.
  */
-export function initializeSparseMatrixFromMatrixMarket(x, { compression = "unknown", layered = true } = {}) {
+export function initializeSparseMatrixFromMatrixMarket(x, options = {}) {
+    const { compression = "unknown", layered = true, ...others } = options;
+    utils.checkOtherOptions(others);
+
     var buf_data;
     var output;
 
@@ -61,7 +64,10 @@ export function initializeSparseMatrixFromMatrixMarket(x, { compression = "unkno
  *
  * @return {object} An object containing the number of `rows`, `columns` and `lines` in the matrix.
  */
-export function extractMatrixMarketDimensions(x, { compression = "unknown" } = {}) {
+export function extractMatrixMarketDimensions(x, options = {}) {
+    const { compression = "unknown", ...others } = options;
+    utils.checkOtherOptions(others);
+
     var buf_data;
     var stats = utils.createFloat64WasmArray(3);
     let output = {};
diff --git a/js/initializeSparseMatrixFromRds.js b/js/initializeSparseMatrixFromRds.js
index 5fb6c87a..9c51dda6 100644
--- a/js/initializeSparseMatrixFromRds.js
+++ b/js/initializeSparseMatrixFromRds.js
@@ -16,7 +16,10 @@ import { ScranMatrix } from "./ScranMatrix.js";
  *
  * @return {ScranMatrix} Matrix containing sparse data.
  */
-export function initializeSparseMatrixFromRds(x, { forceInteger = true, layered = true } = {}) {
+export function initializeSparseMatrixFromRds(x, options = {}) {
+    const { forceInteger = true, layered = true, ...others } = options;
+    utils.checkOtherOptions(others);
+
     var ids = null;
     var output;
 
diff --git a/js/labelCells.js b/js/labelCells.js
index 4ad5d785..6508d0e6 100644
--- a/js/labelCells.js
+++ b/js/labelCells.js
@@ -225,7 +225,10 @@ export function intersectFeatures(testFeatures, referenceFeatures) { // exported
  *
  * @return {TrainedLabelCellsReference} Object containing the built reference dataset.
  */
-export function trainLabelCellsReference(testFeatures, loadedReference, referenceFeatures, { top = 20, numberOfThreads = null } = {}) {
+export function trainLabelCellsReference(testFeatures, loadedReference, referenceFeatures, options = {}) {
+    const { top = 20, numberOfThreads = null, ...others } = options;
+    utils.checkOtherOptions(others);
+
     var test_id_buffer;
     var ref_id_buffer;
     var output;
@@ -301,7 +304,9 @@ class LabelCellsResults {
      * @return {Int32Array|Int32WasmArray} Array of length equal to the number of cells,
      * containing the index of the best label for each cell.
      */
-    predicted({ copy = true } = {}) {
+    predicted(options = {}) {
+        const { copy = true, ...others } = options;
+        utils.checkOtherOptions(others);
         return utils.possibleCopy(this.#results.best(), copy);
     }
 
@@ -316,9 +321,11 @@ class LabelCellsResults {
      * @return {Float64Array|Float64WasmArray} Array containing the scores for this cell across all labels.
      * If `buffer` is supplied, the function returns `buffer` if `asTypedArray = false`, or a view on `buffer` if `asTypedArray = true`.
      */
-    scoreForCell(i, { asTypedArray = true, buffer = null } = {}) {
-        let tmp = null;
+    scoreForCell(i, options = {}) {
+        let { asTypedArray = true, buffer = null, ...others } = options;
+        utils.checkOtherOptions(others);
 
+        let tmp = null;
         try {
             if (buffer == null) {
                 tmp = utils.createFloat64WasmArray(this.#results.num_labels());
@@ -340,7 +347,9 @@ class LabelCellsResults {
      * Only used if `buffer` is not supplied.
      * @return {Float64Array|Float64WasmArray} Array containing the scores across all cells for this label.
      */
-    scoreForLabel(i, { copy = true, buffer = null } = {}) {
+    scoreForLabel(i, options = {}) {
+        const { copy = true, ...others } = options;
+        utils.checkOtherOptions(others);
         return utils.possibleCopy(this.#results.score_for_label(i), copy);
     }
 
@@ -350,7 +359,9 @@ class LabelCellsResults {
      * @return {Float64Array|Float64WasmArray} Array of length equal to the number of cells,
      * containing the difference in scores between the best and second-best labels.
      */
-    delta({ copy = true } = {}) {
+    delta(options = {}) {
+        const { copy = true, ...others } = options;
+        utils.checkOtherOptions(others);
         return utils.possibleCopy(this.#results.delta(), copy);
     }
 
@@ -382,7 +393,10 @@ class LabelCellsResults {
  *
  * @return {LabelCellsResults} Labelling results for each cell in `x`.
  */
-export function labelCells(x, reference, { numberOfFeatures = null, numberOfCells = null, quantile = 0.8, numberOfThreads = null } = {}) {
+export function labelCells(x, reference, options = {}) {
+    const { numberOfFeatures = null, numberOfCells = null, quantile = 0.8, numberOfThreads = null, ...others } = options;
+    utils.checkOtherOptions(others);
+
     var output = null;
     var matbuf;
     var tempmat;
@@ -461,7 +475,7 @@ class IntegratedLabelCellsReferences {
  * Prepare a classifier that integrates multiple reference datasets.
  * This allows users to choose the best label for a test cell based on its classifications in multiple references.
  *
- * @param {Array} features - An array of feature identifiers (usually strings) of length equal to the number of rows in the test matrix.
+ * @param {Array} testFeatures - An array of feature identifiers (usually strings) of length equal to the number of rows in the test matrix.
  * Each entry should contain a single identifier for the corresponding row of the test matrix.
  * Any `null` entries are considered to be incomparable.
  * If any entries are duplicated, only the first occurrence is used and the rest are ignored.
@@ -471,17 +485,20 @@ class IntegratedLabelCellsReferences {
  * Each entry may also be an array of synonymous identifiers, in which case the first identifier that matches to an entry of `testFeatures` is used.
  * Contents of `referenceFeatures` are expected to exhibit some overlap with identifiers in `testFeatures`.
  * Any `null` entries are considered to be incomparable.
- * If multiple entries of `referenceFeatures` match to the same feature in `features`, only the first matching entry is used and the rest are ignored.
+ * If multiple entries of `referenceFeatures` match to the same feature in `testFeatures`, only the first matching entry is used and the rest are ignored.
  * @param {Array} trainedReferences - Array of {@linkplain TrainedLabelCellsReference} objects, typically generated by calling {@linkcode trainLabelCellsReference} 
  * on the same `testFeatures` and the corresponding entries of `loadedReferences` and `referenceFeatures`.
- * This should have length equal to that of `loaded`.
+ * This should have length equal to that of `loadedReferences`.
  * @param {object} [options={}] - Optional parameters.
  * @param {?number} [options.numberOfThreads=null] - Number of threads to use.
  * If `null`, defaults to {@linkcode maximumThreads}.
  *
  * @return {IntegratedLabelCellsReference} Object containing the integrated references.
  */
-export function integrateLabelCellsReferences(testFeatures, loadedReferences, referenceFeatures, trainedReferences, { numberOfThreads = null } = {}) {
+export function integrateLabelCellsReferences(testFeatures, loadedReferences, referenceFeatures, trainedReferences, options = {}) {
+    const { numberOfThreads = null, ...others } = options;
+    utils.checkOtherOptions(others);
+
     let interlen_arr;
     let test_id_arr = [];
     let test_id_ptr_arr;
@@ -605,7 +622,9 @@ class IntegrateLabelCellsResults {
      * @return {Int32Array|Int32WasmArray} Array of length equal to the number of cells,
      * containing the index of the best reference for each cell.
      */
-    predicted({ copy = true } = {}) {
+    predicted(options = {}) {
+        const { copy = true, ...others } = options;
+        utils.checkOtherOptions(others);
         return utils.possibleCopy(this.#results.best(), copy);
     }
 
@@ -620,7 +639,10 @@ class IntegrateLabelCellsResults {
      * @return {Float64Array|Float64WasmArray} Array containing the scores for this cell across all labels.
      * If `buffer` is supplied, the function returns `buffer` if `asTypedArray = false`, or a view on `buffer` if `asTypedArray = true`.
      */
-    scoreForCell(i, { asTypedArray = true, buffer = null } = {}) {
+    scoreForCell(i, options = {}) {
+        let { asTypedArray = true, buffer = null, ...others } = options;
+        utils.checkOtherOptions(others);
+
         let tmp;
         try {
             if (buffer == null) {
@@ -642,7 +664,9 @@ class IntegrateLabelCellsResults {
      *
      * @return {Float64Array|Float64WasmArray} Array containing the scores across all cells for this label.
      */
-    scoreForReference(i, { copy = true } = {}) {
+    scoreForReference(i, options = {}) {
+        const { copy = true, ...others } = options;
+        utils.checkOtherOptions(others);
         return utils.possibleCopy(this.#results.score_for_reference(i), copy);
     }
 
@@ -653,7 +677,9 @@ class IntegrateLabelCellsResults {
      * @return {Float64Array|Float64WasmArray} Array of length equal to the number of cells,
      * containing the difference in scores between the best and second-best references.
      */
-    delta({ copy = true } = {}) {
+    delta(options = {}) {
+        const { copy = true, ...others } = options;
+        utils.checkOtherOptions(others);
         return utils.possibleCopy(this.#results.delta(), copy);
     }
 
@@ -672,12 +698,12 @@ class IntegrateLabelCellsResults {
 /**
  * Integrate cell labels across multiple reference datasets.
  *
- * @param {(ScranMatrix|Float64WasmArray)} x - The count matrix, or log-normalized matrix, containing features in the rows and cells in the columns.
+ * @param {ScranMatrix|Float64WasmArray} x - The count matrix, or log-normalized matrix, containing features in the rows and cells in the columns.
  * If a Float64WasmArray is supplied, it is assumed to contain a column-major dense matrix.
  * @param {IntegratedLabelCellsReferences} integrated - An integrated set of reference datasets, typically generated by {@linkcode integrateLabelCellsReferences}.
  * @param {Array} assigned - An array of length equal to the number of references in `integrated`.
  * This should contain the result of classification of `x` with each individual reference via {@linkcode labelCells}.
- * Each element should be a {@linkplain LabelCells} object; or an Array, TypedArray or Int32WasmArray of length equal to the number of cells in `x`.
+ * Each element should be a {@linkplain LabelCellsResults} object; or an Array, TypedArray or Int32WasmArray of length equal to the number of cells in `x`.
  * @param {object} [options={}] - Optional parameters.
  * @param {?number} [options.numberOfFeatures=null] - Number of features, used when `x` is a Float64WasmArray.
  * @param {?number} [options.numberOfCells=null] - Number of cells, used when `x` is a Float64WasmArray.
@@ -687,7 +713,10 @@ class IntegrateLabelCellsResults {
  *
  * @return {IntegrateLabelCellsResults} Integrated labelling results for each cell in `x`.
  */
-export function integrateLabelCells(x, assigned, integrated, { numberOfFeatures = null, numberOfCells = null, quantile = 0.8, numberOfThreads = null } = {}) { 
+export function integrateLabelCells(x, assigned, integrated, options = {}) {
+    const { numberOfFeatures = null, numberOfCells = null, quantile = 0.8, numberOfThreads = null, ...others } = options;
+    utils.checkOtherOptions(others);
+
     let nrefs = integrated.numberOfReferences();
     if (assigned.length != nrefs) {
         throw new Error("length of 'assigned' should be equal to the number of references in 'integrated'");
diff --git a/js/logNormCounts.js b/js/logNormCounts.js
index 21cbf217..1d377a59 100644
--- a/js/logNormCounts.js
+++ b/js/logNormCounts.js
@@ -21,7 +21,10 @@ import * as wasm from "./wasm.js";
  *
  * @return {ScranMatrix} A matrix of the same type as `x` containing log-transformed normalized expression values.
  */
-export function logNormCounts(x, { sizeFactors = null, allowZeros = false, allowNonFinite = false } = {}) {
+export function logNormCounts(x, options = {}) {
+    const { sizeFactors = null, allowZeros = false, allowNonFinite = false, ...others } = options;
+    utils.checkOtherOptions(others);
+
     var sf_data;
     var output;
 
@@ -69,7 +72,10 @@ export function logNormCounts(x, { sizeFactors = null, allowZeros = false, allow
  * @return {Float64Array|Float64WasmArray} Array containing the centered size factors.
  * If `buffer` is supplied, the function returns `buffer` if `asTypedArray = false`, or a view on `buffer` if `asTypedArray = true`.
  */
-export function centerSizeFactors(sizeFactors, { block = null, asTypedArray = true, buffer = null, toLowestBlock = true } = {}) {
+export function centerSizeFactors(sizeFactors, options = {}) {
+    let { block = null, asTypedArray = true, buffer = null, toLowestBlock = true, ...others } = options;
+    utils.checkOtherOptions(others);
+
     let local_buffer = null;
     let block_data;
 
diff --git a/js/mnnCorrect.js b/js/mnnCorrect.js
index d3ea425e..57b3bd9c 100644
--- a/js/mnnCorrect.js
+++ b/js/mnnCorrect.js
@@ -40,19 +40,22 @@ import * as wasm from "./wasm.js";
  * Corrected values are organized using the column-major layout, where rows are dimensions and columns are cells.
  * If `buffer` is supplied, the function returns `buffer` if `asTypedArray = false`, or a view on `buffer` if `asTypedArray = true`.
  */
-export function mnnCorrect(x, block, { 
-    asTypedArray = true,
-    buffer = null, 
-    numberOfDims = null,
-    numberOfCells = null,
-    k = 15,
-    numberOfMADs = 3, 
-    robustIterations = 2, 
-    robustTrim = 0.25,
-    referencePolicy = "max-rss",
-    approximate = true,
-    numberOfThreads = null
-} = {}) {
+export function mnnCorrect(x, block, options = {}) {
+    let { 
+        asTypedArray = true,
+        buffer = null, 
+        numberOfDims = null,
+        numberOfCells = null,
+        k = 15,
+        numberOfMADs = 3, 
+        robustIterations = 2, 
+        robustTrim = 0.25,
+        referencePolicy = "max-rss",
+        approximate = true,
+        numberOfThreads = null,
+        ...others
+    } = options;
+    utils.checkOtherOptions(others);
 
     let local_buffer = null;
     let x_data;
diff --git a/js/modelGeneVariances.js b/js/modelGeneVariances.js
index 2e0ed706..0522f6a0 100644
--- a/js/modelGeneVariances.js
+++ b/js/modelGeneVariances.js
@@ -26,7 +26,9 @@ export class ModelGeneVariancesResults {
      * containing the mean log-expression across all cells in the specified `block`
      * (or the average across all blocks, if `block = null`).
      */
-    means({ block = null, copy = true } = {}) {
+    means(options = {}) {
+        const { block = null, copy = true, ...others } = options;
+        utils.checkOtherOptions(others);
         return utils.possibleCopy(this.#results.means(block == null ? -1 : block), copy);
     }
 
@@ -42,7 +44,9 @@ export class ModelGeneVariancesResults {
      * containing the variance of log-expression across all cells in the specified `block`
      * (or the average across all blocks, if `block = null`).
      */
-    variances({ block = null, copy = true } = {}) {
+    variances(options = {}) {
+        const { block = null, copy = true, ...others } = options;
+        utils.checkOtherOptions(others);
         return utils.possibleCopy(this.#results.variances(block == null ? -1 : block), copy);
     }
 
@@ -59,7 +63,9 @@ export class ModelGeneVariancesResults {
      * (or the average across all blocks, if `block = null`).
      * Alternatively `null`, if `fillable = false` and the array was not already filled.
      */
-    fitted({ block = null, copy = true } = {}) {
+    fitted(options = {}) {
+        const { block = null, copy = true, ...others } = options;
+        utils.checkOtherOptions(others);
         return utils.possibleCopy(this.#results.fitted(block == null ? -1 : block), copy);
     }
 
@@ -75,7 +81,9 @@ export class ModelGeneVariancesResults {
      * containing the residuals from the mean-variance trend for the specified `block`
      * (or the average across all blocks, if `block = null`).
      */
-    residuals({ block = null, copy = true } = {}) {
+    residuals(options = {}) {
+        const { block = null, copy = true, ...others } = options;
+        utils.checkOtherOptions(others);
         return utils.possibleCopy(this.#results.residuals(block == null ? -1 : block), copy);
     }
 
@@ -129,7 +137,10 @@ export class ModelGeneVariancesResults {
  *
  * @return {ModelGeneVariancesResults} Object containing the variance modelling results.
  */
-export function modelGeneVariances(x, { block = null, span = 0.3, blockWeightPolicy = "variable", numberOfThreads = null } = {}) {
+export function modelGeneVariances(x, options = {}) {
+    const { block = null, span = 0.3, blockWeightPolicy = "variable", numberOfThreads = null, ...others } = options;
+    utils.checkOtherOptions(others);
+
     var block_data;
     var output;
     let nthreads = utils.chooseNumberOfThreads(numberOfThreads);
diff --git a/js/perCellAdtQcMetrics.js b/js/perCellAdtQcMetrics.js
index 104e08e5..913fb276 100644
--- a/js/perCellAdtQcMetrics.js
+++ b/js/perCellAdtQcMetrics.js
@@ -10,7 +10,7 @@ export class PerCellAdtQcMetricsResults {
     #id;
     #results;
 
-    constructor(id, raw, filled = true) {
+    constructor(id, raw) {
         this.#id = id;
         this.#results = raw;
         return;
@@ -26,7 +26,9 @@ export class PerCellAdtQcMetricsResults {
      * @param {boolean} [options.copy=true] - Whether to copy the results from the Wasm heap, see {@linkcode possibleCopy}.
      * @return {Float64Array|Float64WasmArray} Array containing the total ADT count for each cell.
      */
-    sum({ copy = true } = {}) {
+    sum(options = {}) {
+        const { copy = true, ...others } = options;
+        utils.checkOtherOptions(others);
         return utils.possibleCopy(this.#results.sum(), copy);
     }
 
@@ -35,7 +37,9 @@ export class PerCellAdtQcMetricsResults {
      * @param {boolean} [options.copy=true] - Whether to copy the results from the Wasm heap, see {@linkcode possibleCopy}.
      * @return {Int32Array|Int32WasmArray} Array containing the total number of detected ADT features for each cell.
      */
-    detected({ copy = true } = {}) {
+    detected(options = {}) {
+        const { copy = true, ...others } = options;
+        utils.checkOtherOptions(others);
         return utils.possibleCopy(this.#results.detected(), copy);
     }
 
@@ -45,7 +49,9 @@ export class PerCellAdtQcMetricsResults {
      * @param {boolean} [options.copy=true] - Whether to copy the results from the Wasm heap, see {@linkcode possibleCopy}.
      * @return {Float64Array|Float64WasmArray} Array containing the total count in the ADT subset `i` for each cell.
      */
-    subsetSum(i, { copy = true } = {}) {
+    subsetSum(i, options = {}) {
+        const { copy = true, ...others } = options;
+        utils.checkOtherOptions(others);
         return utils.possibleCopy(this.#results.subset_sum(i), copy);
     }
 
@@ -95,7 +101,9 @@ export class PerCellAdtQcMetricsResults {
  *
  * @return {PerCellAdtQcMetricsResults} Object containing the ADT-based QC metrics.
  */
-export function perCellAdtQcMetrics(x, subsets, { numberOfThreads = null } = {}) {
+export function perCellAdtQcMetrics(x, subsets, options = {}) {
+    const { numberOfThreads = null, ...others } = options;
+    utils.checkOtherOptions(others);
     let nthreads = utils.chooseNumberOfThreads(numberOfThreads);
     return internal.computePerCellQcMetrics(
         x, 
diff --git a/js/perCellCrisprQcMetrics.js b/js/perCellCrisprQcMetrics.js
index f243fd7f..abf341bc 100644
--- a/js/perCellCrisprQcMetrics.js
+++ b/js/perCellCrisprQcMetrics.js
@@ -25,7 +25,9 @@ export class PerCellCrisprQcMetricsResults {
      * @param {boolean} [options.copy=true] - Whether to copy the results from the Wasm heap, see {@linkcode possibleCopy}.
      * @return {Float64Array|Float64WasmArray} Array containing the total count across guides for each cell.
      */
-    sum({ copy = true } = {}) {
+    sum(options = {}) {
+        const { copy = true, ...others } = options;
+        utils.checkOtherOptions(others);
         return utils.possibleCopy(this.#results.sum(), copy);
     }
 
@@ -34,7 +36,9 @@ export class PerCellCrisprQcMetricsResults {
      * @param {boolean} [options.copy=true] - Whether to copy the results from the Wasm heap, see {@linkcode possibleCopy}.
      * @return {Int32Array|Int32WasmArray} Array containing the total number of detected guides for each cell.
      */
-    detected({ copy = true } = {}) {
+    detected(options = {}) {
+        const { copy = true, ...others } = options;
+        utils.checkOtherOptions(others);
         return utils.possibleCopy(this.#results.detected(), copy);
     }
 
@@ -43,7 +47,9 @@ export class PerCellCrisprQcMetricsResults {
      * @param {boolean} [options.copy=true] - Whether to copy the results from the Wasm heap, see {@linkcode possibleCopy}.
      * @return {Float64Array|Float64WasmArray} Array containing the count of the most abundant guide for each cell.
      */
-    maxValue({ copy = true } = {}) {
+    maxValue(options = {}) {
+        const { copy = true, ...others } = options;
+        utils.checkOtherOptions(others);
         return utils.possibleCopy(this.#results.max_value(), copy);
     }
 
@@ -62,7 +68,9 @@ export class PerCellCrisprQcMetricsResults {
      * @param {boolean} [options.copy=true] - Whether to copy the results from the Wasm heap, see {@linkcode possibleCopy}.
      * @return {Int32Array|Int32WasmArray} Array containing the index of the most abundant guide for each cell.
      */
-    maxIndex({ copy = true } = {}) {
+    maxIndex(options = {}) {
+        const { copy = true, ...others } = options;
+        utils.checkOtherOptions(others);
         return utils.possibleCopy(this.#results.max_index(), copy);
     }
 
@@ -96,7 +104,9 @@ export class PerCellCrisprQcMetricsResults {
  *
  * @return {PerCellCrisprQcMetricsResults} Object containing the QC metrics.
  */
-export function perCellCrisprQcMetrics(x, { numberOfThreads = null } = {}) {
+export function perCellCrisprQcMetrics(x, options = {}) {
+    const { numberOfThreads = null, ...others } = options;
+    utils.checkOtherOptions(others);
     let nthreads = utils.chooseNumberOfThreads(numberOfThreads);
     return gc.call(
         module => module.per_cell_crispr_qc_metrics(x.matrix, nthreads),
diff --git a/js/perCellRnaQcMetrics.js b/js/perCellRnaQcMetrics.js
index 28bb3842..a7eecb63 100644
--- a/js/perCellRnaQcMetrics.js
+++ b/js/perCellRnaQcMetrics.js
@@ -26,7 +26,9 @@ export class PerCellRnaQcMetricsResults {
      * @param {boolean} [options.copy=true] - Whether to copy the results from the Wasm heap, see {@linkcode possibleCopy}.
      * @return {Float64Array|Float64WasmArray} Array containing the total count across genes for each cell.
      */
-    sum({ copy = true } = {}) {
+    sum(options = {}) {
+        const { copy = true, ...others } = options;
+        utils.checkOtherOptions(others);
         return utils.possibleCopy(this.#results.sum(), copy);
     }
 
@@ -35,7 +37,9 @@ export class PerCellRnaQcMetricsResults {
      * @param {boolean} [options.copy=true] - Whether to copy the results from the Wasm heap, see {@linkcode possibleCopy}.
      * @return {Int32Array|Int32WasmArray} Array containing the total number of detected genes for each cell.
      */
-    detected({ copy = true } = {}) {
+    detected(options = {}) {
+        const { copy = true, ...others } = options;
+        utils.checkOtherOptions(others);
         return utils.possibleCopy(this.#results.detected(), copy);
     }
 
@@ -45,7 +49,9 @@ export class PerCellRnaQcMetricsResults {
      * @param {boolean} [options.copy=true] - Whether to copy the results from the Wasm heap, see {@linkcode possibleCopy}.
      * @return {Float64Array|Float64WasmArray} Array containing the proportion of counts in the subset `i` for each cell.
      */
-    subsetProportion(i, { copy = true, fillable = false } = {}) {
+    subsetProportion(i, options = {}) {
+        const { copy = true, ...others } = options;
+        utils.checkOtherOptions(others);
         return utils.possibleCopy(this.#results.subset_proportion(i), copy);
     }
 
@@ -95,9 +101,11 @@ export class PerCellRnaQcMetricsResults {
  *
  * @return {PerCellRnaQcMetricsResults} Object containing the QC metrics.
  */
-export function perCellRnaQcMetrics(x, subsets, { numberOfThreads = null } = {}) {
+export function perCellRnaQcMetrics(x, subsets, options = {}) {
+    const { numberOfThreads = null, ...others } = options;
+    utils.checkOtherOptions(others);
     let nthreads = utils.chooseNumberOfThreads(numberOfThreads);
-     return internal.computePerCellQcMetrics(
+    return internal.computePerCellQcMetrics(
         x, 
         subsets, 
         (matrix, nsubsets, subset_offset) => gc.call(
diff --git a/js/rds.js b/js/rds.js
index e48296ed..851c4612 100644
--- a/js/rds.js
+++ b/js/rds.js
@@ -101,7 +101,9 @@ export class RdsIntegerVector extends RdsVector {
      *
      * @return {Int32Array|Int32WasmArray} Values of the integer vector.
      */
-    values({ copy = true } = {}) {
+    values(options = {}) {
+        const { copy = true, ...others } = options;
+        utils.checkOtherOptions(others);
         return utils.possibleCopy(this.object.numeric_vector(), copy);
     }
 }
@@ -124,7 +126,9 @@ export class RdsBooleanVector extends RdsVector {
      * @return {Int32Array|Int32WasmArray} Values of the logical vector.
      * Zero values are falsey and values of 1 are truthy.
      */
-    values({ copy = true } = {}) {
+    values(options = {}) {
+        const { copy = true, ...others } = options;
+        utils.checkOtherOptions(others);
         return utils.possibleCopy(this.object.numeric_vector(), copy);
     }
 }
@@ -146,7 +150,9 @@ export class RdsDoubleVector extends RdsVector {
      *
      * @return {Float64Array|Float64WasmArray} Values of the double vector.
      */
-    values({ copy = true } = {}) {
+    values(options = {}) {
+        const { copy = true, ...others } = options;
+        utils.checkOtherOptions(others);
         return utils.possibleCopy(this.object.numeric_vector(), copy);
     }
 }
diff --git a/js/realizeFile.js b/js/realizeFile.js
index 1b9b157e..d2465b8d 100644
--- a/js/realizeFile.js
+++ b/js/realizeFile.js
@@ -1,4 +1,5 @@
 import * as methods from "./file.js";
+import * as utils from "./utils.js";
 import * as areal from "./abstract/realize.js";
 
 /**
@@ -10,7 +11,9 @@ import * as areal from "./abstract/realize.js";
  *
  * @return {string} Temporary file path.
  */
-export function chooseTemporaryPath({ extension = "" } = {}) {
+export function chooseTemporaryPath(options = {}) {
+    const { extension = "", ...others } = options;
+    utils.checkOtherOptions(others);
     return areal.temp("temp_", extension);    
 }
 
@@ -30,7 +33,10 @@ export function chooseTemporaryPath({ extension = "" } = {}) {
  * - `flush`: a function to be called to remove any temporary file created by this function.
  *   For Node.js, this will be a no-op if `file` is already a path. 
  */
-export function realizeFile(file, { extension = "" } = {}) {
+export function realizeFile(file, options = {}) {
+    const { extension = "", ...others } = options;
+    utils.checkOtherOptions(others);
+
     if (typeof file == "string") {
         return areal.handleString(file);
     }
diff --git a/js/runPca.js b/js/runPca.js
index 9f6bdd81..206d8b4d 100644
--- a/js/runPca.js
+++ b/js/runPca.js
@@ -21,7 +21,9 @@ export class RunPcaResults {
      * @return {Float64Array|Float64Wasmarray} Array containing the principal components for all cells.
      * This should be treated as a column-major array where the rows are the PCs and columns are the cells.
      */
-    principalComponents({ copy = true } = {}) {
+    principalComponents(options = {}) {
+        const { copy = true, ...others } = options;
+        utils.checkOtherOptions(others);
         return utils.possibleCopy(this.#results.components(), copy);
     }
 
@@ -31,7 +33,9 @@ export class RunPcaResults {
      * @return {Float64Array|Float64Wasmarray} Array containing the rotation matrix for all cells.
      * This should be treated as a column-major array where the rows are the genes and the columns are the PCs.
      */
-    rotation({ copy = true } = {}) {
+    rotation(options = {}) {
+        const { copy = true, ...others } = options;
+        utils.checkOtherOptions(others);
         return utils.possibleCopy(this.#results.pcs(), copy);
     }
 
@@ -40,7 +44,9 @@ export class RunPcaResults {
      * @param {boolean} [options.copy=true] - Whether to copy the results from the Wasm heap, see {@linkcode possibleCopy}.
      * @return {Float64Array|Float64WasmArray} Array containing the variance explained for each requested PC.
      */
-    varianceExplained({ copy = true } = {}) {
+    varianceExplained(options = {}) {
+        const { copy = true, ...others } = options;
+        utils.checkOtherOptions(others);
         return utils.possibleCopy(this.#results.variance_explained(), copy);
     }
 
@@ -120,16 +126,19 @@ export class RunPcaResults {
  *
  * @return {RunPcaResults} Object containing the computed PCs.
  */
-export function runPca(x, { 
-    features = null,
-    numberOfPCs = 25,
-    scale = false,
-    block = null,
-    blockMethod = "regress",
-    blockWeightPolicy = "variable",
-    realizeMatrix = null,
-    numberOfThreads = null 
-} = {}) {
+export function runPca(x, options = {}) {
+    let { 
+        features = null,
+        numberOfPCs = 25,
+        scale = false,
+        block = null,
+        blockMethod = "regress",
+        blockWeightPolicy = "variable",
+        realizeMatrix = null,
+        numberOfThreads = null,
+        ...others
+    } = options;
+    utils.checkOtherOptions(others);
 
     var feat_data;
     var block_data;
diff --git a/js/runTsne.js b/js/runTsne.js
index 92c5b35e..6275bc70 100644
--- a/js/runTsne.js
+++ b/js/runTsne.js
@@ -68,7 +68,9 @@ export class TsneStatus {
      * @return The algorithm status in `x` is advanced up to the requested number of iterations,
      * or until the requested run time is exceeded, whichever comes first.
      */
-    run({ maxIterations = 1000, runTime = null } = {}) {
+    run(options = {}) {
+        let { maxIterations = 1000, runTime = null, ...others } = options;
+        utils.checkOtherOptions(others);
         if (runTime === null) {
             runTime = -1;
         }
@@ -113,7 +115,10 @@ export function perplexityToNeighbors(perplexity) {
  *
  * @return {TsneStatus} Object containing the initial status of the t-SNE algorithm.
  */
-export function initializeTsne(x, { perplexity = 30, checkMismatch = true, numberOfThreads = null } = {}) {
+export function initializeTsne(x, options = {}) {
+    const { perplexity = 30, checkMismatch = true, numberOfThreads = null, ...others } = options;
+    utils.checkOtherOptions(others);
+
     var my_neighbors;
     var raw_coords;
     var output;
@@ -174,7 +179,9 @@ export function initializeTsne(x, { perplexity = 30, checkMismatch = true, numbe
  *
  * @return {object} Object containing coordinates of the t-SNE embedding, see {@linkcode TsneStatus#extractCoordinates TsneStatus.extractCoordinates} for more details.
  */
-export function runTsne(x, { perplexity = 30, checkMismatch = true, numberOfThreads = null, maxIterations = 1000 } = {}) {
+export function runTsne(x, options = {}) {
+    const { perplexity = 30, checkMismatch = true, numberOfThreads = null, maxIterations = 1000, ...others } = options;
+    utils.checkOtherOptions(others);
     let tstat = initializeTsne(x, { perplexity, checkMismatch, numberOfThreads });
     tstat.run({ maxIterations });
     return tstat.extractCoordinates();
diff --git a/js/runUmap.js b/js/runUmap.js
index d18a03f2..87b4e26c 100644
--- a/js/runUmap.js
+++ b/js/runUmap.js
@@ -64,7 +64,9 @@ export class UmapStatus {
      * @return The algorithm status in `x` is advanced up to the total number of epochs used to initialize `x`,
      * or until the requested run time is exceeded, whichever comes first.
      */
-    run({ runTime = null } = {}) {
+    run(options = {}) {
+        let { runTime = null, ...others } = options;
+        utils.checkOtherOptions(others);
         if (runTime === null) {
             runTime = -1;
         }
@@ -111,7 +113,10 @@ export class UmapStatus {
  *
  * @return {UmapStatus} Object containing the initial status of the UMAP algorithm.
  */
-export function initializeUmap(x, { neighbors = 15, epochs = 500, minDist = 0.01, numberOfThreads = null } = {}) {
+export function initializeUmap(x, options = {}) {
+    const { neighbors = 15, epochs = 500, minDist = 0.01, numberOfThreads = null, ...others } = options;
+    utils.checkOtherOptions(others);
+
     var my_neighbors;
     var raw_coords;
     var output;
@@ -163,7 +168,9 @@ export function initializeUmap(x, { neighbors = 15, epochs = 500, minDist = 0.01
  *
  * @return {object} Object containing coordinates of the UMAP embedding, see {@linkcode UmapStatus#extractCoordinates UmapStatus.extractCoordinates} for more details.
  */
-export function runUmap(x, { neighbors = 15, epochs = 500, minDist = 0.01, numberOfThreads = null } = {}) {
+export function runUmap(x, options = {}){
+    const { neighbors = 15, epochs = 500, minDist = 0.01, numberOfThreads = null, ...others } = options;
+    utils.checkOtherOptions(others);
     let ustat = initializeUmap(x, { neighbors, epochs, minDist, numberOfThreads });
     ustat.run();
     return ustat.extractCoordinates();
diff --git a/js/scaleByNeighbors.js b/js/scaleByNeighbors.js
index 773b9f6a..4f5f2a43 100644
--- a/js/scaleByNeighbors.js
+++ b/js/scaleByNeighbors.js
@@ -31,7 +31,10 @@ import { buildNeighborSearchIndex, BuildNeighborSearchIndexResults } from "./fin
  * @return {Float64Array|Float64WasmArray} Array containing the combined embeddings in column-major format, i.e., dimensions in rows and cells in columns.
  * If `buffer` is supplied, the function returns `buffer` if `asTypedArray = false`, or a view on `buffer` if `asTypedArray = true`.
  */
-export function scaleByNeighbors(embeddings, numberOfCells, { neighbors = 20, indices = null, asTypedArray = true, buffer = null, approximate = true, weights = null, numberOfThreads = null } = {}) {
+export function scaleByNeighbors(embeddings, numberOfCells, options = {}) {
+    let { neighbors = 20, indices = null, asTypedArray = true, buffer = null, approximate = true, weights = null, numberOfThreads = null, ...others } = options;
+    utils.checkOtherOptions(others);
+
     let embed_ptrs;
     let index_ptrs;
     let holding_weights;
diff --git a/js/scoreGsdecon.js b/js/scoreGsdecon.js
index f4b38ae5..0015ad65 100644
--- a/js/scoreGsdecon.js
+++ b/js/scoreGsdecon.js
@@ -31,7 +31,10 @@ import * as wa from "wasmarrays.js";
  * - `weights`, a Float64Array containing per-gene weights for each feature in the set.
  * - `scores`, a Float64Array containing the per-cell scores for each column of `x`.
  */
-export function scoreGsdecon(x, features, { block = null, scale = false, blockWeightPolicy = "variable", numberOfThreads = null } = {}) {
+export function scoreGsdecon(x, features, options = {}) {
+    const { block = null, scale = false, blockWeightPolicy = "variable", numberOfThreads = null, ...others } = options;
+    utils.checkOtherOptions(others);
+
     let temp;
     let output = {};
     let feature_data, block_data;
diff --git a/js/scoreMarkers.js b/js/scoreMarkers.js
index fa9bd4de..f3f63659 100644
--- a/js/scoreMarkers.js
+++ b/js/scoreMarkers.js
@@ -35,7 +35,9 @@ export class ScoreMarkersResults {
      * @return {?(Float64Array|Float64WasmArray)} Array of length equal to the number of genes,
      * containing the mean expression for the requested group in the requested block.
      */
-    mean(group, { copy = true } = {}) {
+    mean(group, options = {}) {
+        const { copy = true, ...others } = options;
+        utils.checkOtherOptions(others);
         return utils.possibleCopy(this.#results.mean(group), copy);
     }
 
@@ -48,7 +50,9 @@ export class ScoreMarkersResults {
      * @return {Float64Array|Float64WasmArray} Array of length equal to the number of genes,
      * containing the proportion of cells with detectable expression for the requested group in the requested block.
      */
-    detected(group, { copy = true } = {}) {
+    detected(group, options = {}) {
+        const { copy = true, ...others } = options;
+        utils.checkOtherOptions(others);
         return utils.possibleCopy(this.#results.detected(group), copy);
     }
 
@@ -70,7 +74,9 @@ export class ScoreMarkersResults {
      * @return {Float64Array|Float64WasmArray} Array of length equal to the number of genes,
      * containing the summarized Cohen's d for the comparisons between `group` and all other groups.
      */
-    cohensD(group, { summary = "mean", copy = true } = {}) {
+    cohensD(group, options = {}) {
+        const { summary = "mean", copy = true, ...others } = options;
+        utils.checkOtherOptions(others);
         this.#check_forbidden(summary);
         return utils.possibleCopy(wasm.call(_ => this.#results.cohens_d(group, summary)), copy);
     }
@@ -90,7 +96,9 @@ export class ScoreMarkersResults {
      * @return {Float64Array|Float64WasmArray} Array of length equal to the number of genes,
      * containing the summarized AUC for the comparisons between `group` and all other groups.
      */
-    auc(group, { summary = "mean", copy = true } = {}) {
+    auc(group, options = {}) {
+        const { summary = "mean", copy = true, ...others } = options;
+        utils.checkOtherOptions(others);
         this.#check_forbidden(summary);
         return utils.possibleCopy(wasm.call(_ => this.#results.auc(group, summary)), copy);
     }
@@ -108,7 +116,9 @@ export class ScoreMarkersResults {
      * containing the summarized delta-mean for the comparisons between `group` and all other groups.
      * This can be interpreted as the log-fold change if log-expression values are used in {@linkcode scoreMarkers}.
      */
-    deltaMean(group, { summary = "mean", copy = true } = {}) {
+    deltaMean(group, options = {}) {
+        const { summary = "mean", copy = true, ...others } = options;
+        utils.checkOtherOptions(others);
         this.#check_forbidden(summary);
         return utils.possibleCopy(wasm.call(_ => this.#results.delta_mean(group, summary)), copy);
     }
@@ -125,7 +135,9 @@ export class ScoreMarkersResults {
      * @return {Float64Array|Float64WasmArray} Array of length equal to the number of genes,
      * containing the summarized delta-detected for the comparisons between `group` and all other groups.
      */
-    deltaDetected(group, { summary = "mean", copy = true } = {}) {
+    deltaDetected(group, options = {}) {
+        const { summary = "mean", copy = true, ...others } = options;
+        utils.checkOtherOptions(others);
         this.#check_forbidden(summary);
         return utils.possibleCopy(wasm.call(_ => this.#results.delta_detected(group, summary)), copy);
     }
@@ -168,7 +180,10 @@ export class ScoreMarkersResults {
  *
  * @return {ScoreMarkersResults} Object containing the marker scoring results.
  */
-export function scoreMarkers(x, groups, { block = null, threshold = 0, computeAuc = true, computeMedian = false, computeMaximum = false , numberOfThreads = null} = {}) {
+export function scoreMarkers(x, groups, options = {}) {
+    const { block = null, threshold = 0, computeAuc = true, computeMedian = false, computeMaximum = false , numberOfThreads = null, ...others } = options;
+    utils.checkOtherOptions(others);
+
     var output;
     var block_data;
     var group_data;
diff --git a/js/subset.js b/js/subset.js
index 6655cf12..d8fb9f5c 100644
--- a/js/subset.js
+++ b/js/subset.js
@@ -16,7 +16,10 @@ import { MultiMatrix } from "./MultiMatrix.js";
  * A ScranMatrix containing the subset of rows from `mat` specified by `indices`.
  * If `inPlace = true`, this is a reference to `x`, otherwise it is a new ScranMatrix.
  */
-export function subsetRows(x, indices, { inPlace = false } = {}) {
+export function subsetRows(x, indices, options = {}) {
+    const { inPlace = false, ...others } = options;
+    utils.checkOtherOptions(others);
+
     let xcopy;
     let target;
     let wasm_indices;
@@ -57,7 +60,10 @@ export function subsetRows(x, indices, { inPlace = false } = {}) {
  * A new ScranMatrix containing the subset of columns from `mat` specified by `indices`.
  * If `inPlace = true`, this is a reference to `x`, otherwise it is a new ScranMatrix.
  */
-export function subsetColumns(x, indices, { inPlace = false } = {}) {
+export function subsetColumns(x, indices, options = {}) {
+    const { inPlace = false, ...others } = options;
+    utils.checkOtherOptions(others);
+
     let xcopy;
     let target;
     let wasm_indices;
@@ -98,7 +104,10 @@ export function subsetColumns(x, indices, { inPlace = false } = {}) {
  * @return {object|MultiMatrix} Object with the same keys as `split` where each value is a ScranMatrix for the corresponding subset of rows.
  * Alternatively, this is wrapped in a MultiMatrix if `createMultiMatrix = true`.
  */
-export function splitRows(matrix, split, { singleNull = false, createMultiMatrix = false } = {}) { 
+export function splitRows(matrix, split, options = {}) {
+    const { singleNull = false, createMultiMatrix = false, ...others } = options;
+    utils.checkOtherOptions(others);
+
     let output = {};
     let tkeys = Object.keys(split);
 
diff --git a/js/suggestAdtQcFilters.js b/js/suggestAdtQcFilters.js
index 8842f5f0..652c333c 100644
--- a/js/suggestAdtQcFilters.js
+++ b/js/suggestAdtQcFilters.js
@@ -23,7 +23,9 @@ export class SuggestAdtQcFiltersResults {
      *
      * @return {?(Float64Array|Float64WasmArray)} Array containing the filtering threshold on the number of detected ADTs for each batch.
      */
-    detected({ copy = true } = {}) {
+    detected(options = {}) {
+        const { copy = true, ...others } = options;
+        utils.checkOtherOptions(others);
         return utils.possibleCopy(this.#results.detected(), copy);
     }
 
@@ -35,7 +37,9 @@ export class SuggestAdtQcFiltersResults {
      *
      * @return {?(Float64Array|Float64WasmArray)} Array containing the filtering threshold on the total counts for subset `i` in each batch.
      */
-    subsetSum(i, { copy = true } = {}) {
+    subsetSum(i, options = {}) {
+        const { copy = true, ...others } = options;
+        utils.checkOtherOptions(others);
         return utils.possibleCopy(this.#results.subset_sum(i), copy);
     }
 
@@ -74,7 +78,9 @@ export class SuggestAdtQcFiltersResults {
      * Each entry is truthy if the corresponding cell is deemed to be of high-quality based on its values in `metrics`.
      * If `buffer` is supplied, the returned array is a view on `buffer`.
      */
-    filter(metrics, { block = null, buffer = null } = {}) {
+    filter(metrics, options = {}) {
+        const { block = null, buffer = null, ...others } = options;
+        utils.checkOtherOptions(others);
         if (!(metrics instanceof PerCellAdtQcMetricsResults)) {
             throw new Error("'metrics' should be a PerCellAdtQcMetricsResults object");
         }
@@ -109,10 +115,14 @@ export class SuggestAdtQcFiltersResults {
  *
  * @return {SuggestAdtQcFiltersResults} Object containing the filtering results.
  */
-export function suggestAdtQcFilters(metrics, { numberOfMADs = 3, minDetectedDrop = 0.1, block = null } = {}) {
+export function suggestAdtQcFilters(metrics, options = {}) {
+    const { numberOfMADs = 3, minDetectedDrop = 0.1, block = null, ...others } = options;
+    utils.checkOtherOptions(others);
+
     if (!(metrics instanceof PerCellAdtQcMetricsResults)) {
         throw new Error("'metrics' should be a PerCellAdtQcMetricsResults object");
     }
+
     return internal.computePerCellQcFilters(
         metrics, 
         block,
diff --git a/js/suggestCrisprQcFilters.js b/js/suggestCrisprQcFilters.js
index 857b3947..eec13f5f 100644
--- a/js/suggestCrisprQcFilters.js
+++ b/js/suggestCrisprQcFilters.js
@@ -23,17 +23,12 @@ export class SuggestCrisprQcFiltersResults {
      *
      * @return {?(Float64Array|Float64WasmArray)} Array containing the filtering threshold on the maximum count in each batch.
      */
-    maxValue({ copy = true } = {}) {
+    maxValue(options = {}) {
+        const { copy = true, ...others } = options;
+        utils.checkOtherOptions(others);
         return utils.possibleCopy(this.#results.max_value(), copy);
     }
 
-    /**
-     * @ignore
-     */
-    thresholdsMaxCount({ copy = true } = {}) {
-        return this.thresholdsMaxValue({ copy });
-    }
-
     /**
      * @return {number} Number of blocks in this object.
      */
@@ -55,7 +50,9 @@ export class SuggestCrisprQcFiltersResults {
      * Each entry is truthy if the corresponding cell is deemed to be of high-quality based on its values in `metrics`.
      * If `buffer` is supplied, the returned array is a view on `buffer`.
      */
-    filter(metrics, { block = null, buffer = null } = {}) {
+    filter(metrics, options = {}) {
+        const { block = null, buffer = null, ...others } = options;
+        utils.checkOtherOptions(others);
         if (!(metrics instanceof PerCellCrisprQcMetricsResults)) {
             throw new Error("'metrics' should be a PerCellCrisprQcMetricsResults object");
         }
@@ -88,10 +85,14 @@ export class SuggestCrisprQcFiltersResults {
  *
  * @return {SuggestCrisprQcFiltersResults} Object containing the filtering results.
  */
-export function suggestCrisprQcFilters(metrics, { numberOfMADs = 3, block = null } = {}) {
+export function suggestCrisprQcFilters(metrics, options = {}) {
+    const { numberOfMADs = 3, block = null, ...others } = options;
+    utils.checkOtherOptions(others);
+
     if (!(metrics instanceof PerCellCrisprQcMetricsResults)) {
         throw new Error("'metrics' should be a PerCellCrisprQcMetricsResults object");
     }
+
     return internal.computePerCellQcFilters(
         metrics,
         block,
diff --git a/js/suggestRnaQcFilters.js b/js/suggestRnaQcFilters.js
index 12d71d87..b824e92b 100644
--- a/js/suggestRnaQcFilters.js
+++ b/js/suggestRnaQcFilters.js
@@ -23,7 +23,9 @@ export class SuggestRnaQcFiltersResults {
      *
      * @return {Float64Array|Float64WasmArray} Array containing the filtering threshold on the sums for each batch.
      */
-    sum({ copy = true } = {}) {
+    sum(options = {}) {
+        const { copy = true, ...others } = options;
+        utils.checkOtherOptions(others);
         return utils.possibleCopy(this.#results.sum(), copy);
     }
 
@@ -34,7 +36,9 @@ export class SuggestRnaQcFiltersResults {
      *
      * @return {Float64Array|Float64WasmArray} Array containing the filtering threshold on the number of detected genes for each batch.
      */
-    detected({ copy = true } = {}) {
+    detected(options = {}) {
+        const { copy = true, ...others } = options;
+        utils.checkOtherOptions(others);
         return utils.possibleCopy(this.#results.detected(), copy);
     }
 
@@ -46,7 +50,9 @@ export class SuggestRnaQcFiltersResults {
      *
      * @return {Float64Array|Float64WasmArray} Array containing the filtering threshold on the proportions for subset `i` in each batch.
      */
-    subsetProportion(i, { copy = true } = {}) {
+    subsetProportion(i, options = {}) {
+        const { copy = true, ...others } = options;
+        utils.checkOtherOptions(others);
         return utils.possibleCopy(this.#results.subset_proportion(i), copy);
     }
 
@@ -83,7 +89,9 @@ export class SuggestRnaQcFiltersResults {
      * Each entry is truthy if the corresponding cell is deemed to be of high-quality based on its values in `metrics`.
      * If `buffer` is supplied, the returned array is a view on `buffer`.
      */
-    filter(metrics, { block = null, buffer = null } = {}) {
+    filter(metrics, options = {}) {
+        const { block = null, buffer = null, ...others } = options;
+        utils.checkOtherOptions(others);
         if (!(metrics instanceof PerCellRnaQcMetricsResults)) {
             throw new Error("'metrics' should be a PerCellRnaQcMetricsResults object");
         }
@@ -116,10 +124,14 @@ export class SuggestRnaQcFiltersResults {
  *
  * @return {SuggestRnaQcFiltersResults} Object containing the filtering results.
  */
-export function suggestRnaQcFilters(metrics, { numberOfMADs = 3, block = null } = {}) {
+export function suggestRnaQcFilters(metrics, options = {}) {
+    const { numberOfMADs = 3, block = null, ...others } = options;
+    utils.checkOtherOptions(others);
+
     if (!(metrics instanceof PerCellRnaQcMetricsResults)) {
         throw new Error("'metrics' should be a PerCellRnaQcMetricsResults object");
     }
+
     return internal.computePerCellQcFilters(
         metrics,
         block,
diff --git a/js/testGeneSetEnrichment.js b/js/testGeneSetEnrichment.js
index 52daba3f..184ad88a 100644
--- a/js/testGeneSetEnrichment.js
+++ b/js/testGeneSetEnrichment.js
@@ -26,7 +26,10 @@ import * as utils from "./utils.js";
  * - `size`: Int32Array containing the size of each set.
  * - `pvalue`: Float64Array containing the p-value for enrichment in each set.
  */
-export function testGeneSetEnrichment(markers, geneSets, totalGenes, { numberOfThreads = null } = {}) {
+export function testGeneSetEnrichment(markers, geneSets, totalGenes, options = {}) {
+    const { numberOfThreads = null, ...others } = options;
+    utils.checkOtherOptions(others);
+
     for (const j of markers) {
         if (j >= totalGenes) {
             throw new Error("'markers' contains out-of-range indices (" + String(j) + ")");
diff --git a/js/utils.js b/js/utils.js
index 2f9f7856..1ce26425 100644
--- a/js/utils.js
+++ b/js/utils.js
@@ -153,12 +153,6 @@ export function matchOptions(name, value, choices) {
     }
 }
 
-export function spawnArray(n, fill) {
-    let output = new Array(n);
-    output.fill(fill);
-    return output;
-}
-
 export function toTypedArray(buffer, wasSupplied, asTypedArray) {
     if (asTypedArray) {
         if (!wasSupplied) {
@@ -172,3 +166,9 @@ export function toTypedArray(buffer, wasSupplied, asTypedArray) {
         return buffer;
     }
 }
+
+export function checkOtherOptions(options) {
+    for (const key of Object.keys(options)) {
+        throw new Error("unknown option '" + key + "'");
+    }
+}
diff --git a/js/writeSparseMatrixToHdf5.js b/js/writeSparseMatrixToHdf5.js
index 6eff9c22..48873bf3 100644
--- a/js/writeSparseMatrixToHdf5.js
+++ b/js/writeSparseMatrixToHdf5.js
@@ -1,5 +1,6 @@
 import * as h5 from "./hdf5.js";
 import * as wasm from "./wasm.js";
+import * as utils from "./utils.js";
 
 /**
  * Write a sparse {@linkplain ScranMatrix} into HDF5 file, in the form of its compressed sparse components.
@@ -23,7 +24,9 @@ import * as wasm from "./wasm.js";
  *
  * @return `x` is written to `path` at `name`.
  */
-export function writeSparseMatrixToHdf5(x, path, name, { format = "tenx_matrix", forceInteger = false } = {}) {
+export function writeSparseMatrixToHdf5(x, path, name, options = {}) {
+    const { format = "tenx_matrix", forceInteger = false, ...others } = options;
+    utils.checkOtherOptions(others);
     wasm.call(module => module.write_sparse_matrix_to_hdf5(x.matrix, path, name, format, forceInteger));
 
     let handle = new h5.H5Group(path, name);
diff --git a/tests/initializeScranMatrixFromHdf5.test.js b/tests/initializeScranMatrixFromHdf5.test.js
index 927c0f36..c7918f8d 100644
--- a/tests/initializeScranMatrixFromHdf5.test.js
+++ b/tests/initializeScranMatrixFromHdf5.test.js
@@ -275,7 +275,7 @@ test("initialization from HDF5 works correctly with forced integers", () => {
     }
 
     // Using raw access.
-    var mat3 = scran.initializeSparseMatrixFromHdf5(path, "foobar", { numberOfRows: nr, numberOfColumns: nc, sparseByRow: false, forceInteger: true, layered: true });
+    var mat3 = scran.initializeSparseMatrixFromHdf5Group(path, "foobar", nr, nc, false, { forceInteger: true, layered: true });
     expect(mat3.numberOfRows()).toBe(nr); 
     expect(mat3.numberOfColumns()).toBe(nc);
     expect(compare.equalArrays(mat3.row(0), mat1.row(0))).toBe(true);
diff --git a/tests/initializeSparseMatrixFromMatrixMarket.test.js b/tests/initializeSparseMatrixFromMatrixMarket.test.js
index 91365615..0708b709 100644
--- a/tests/initializeSparseMatrixFromMatrixMarket.test.js
+++ b/tests/initializeSparseMatrixFromMatrixMarket.test.js
@@ -94,7 +94,7 @@ test("initialization from Gzipped MatrixMarket works correctly with Gzip", () =>
     expect(compare.equalArrays(mat.column(2), [0, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0])).toBe(true);
     
     // Just checking that the it's actually compressed.
-    var mat2 = scran.initializeSparseMatrixFromMatrixMarket(buffer, { compressed: true });
+    var mat2 = scran.initializeSparseMatrixFromMatrixMarket(buffer, { compression: "gzip" });
     expect(mat2.numberOfRows()).toBe(11);
     expect(mat2.numberOfColumns()).toBe(5);
     expect(compare.equalArrays(mat2.row(4), mat.row(4))).toBe(true);
diff --git a/tests/labelCells.test.js b/tests/labelCells.test.js
index d420be02..4a50ede6 100644
--- a/tests/labelCells.test.js
+++ b/tests/labelCells.test.js
@@ -276,12 +276,15 @@ test("labelCells ignores nulls correctly", () => {
     for (var i = 0; i < until; i++) {
         copy[i] = null;
     }
+
     let built = scran.trainLabelCellsReference(copy, refinfo, mockids);
     expect(built.numberOfFeatures() > 0).toBe(true);
+
     let results = scran.labelCells(mat, built); 
     let labels = results.predicted();
-    expect(compare.equalArrays(labels, refresults.predicted({ copy: false }))).toBe(false);
-    let firstscore = results.scoreForCell(0, { copy: false });
+    expect(compare.equalArrays(labels, refresults.predicted({ copy: false }))).toBe(false); // should have some diff!
+    let firstscore = results.scoreForCell(0); 
+    expect(compare.equalFloatArrays(firstscore, refresults.scoreForCell(0))).toBe(false);
 
     // Manually removing the first gene from the test matrix.
     {
diff --git a/tests/rds/init.test.js b/tests/rds/init.test.js
index 333d6d1c..4cfd03fe 100644
--- a/tests/rds/init.test.js
+++ b/tests/rds/init.test.js
@@ -92,21 +92,6 @@ maybe("works for dgCMatrix", () => {
         expect_all(x, y => y%1 == 0); // forced integers.
         test_okayish(x, 70, 30, layered);
 
-        // Same results in consumption mode.
-        {
-            let stuff2 = scran.readRds(rpath);
-            let vals2 = stuff2.value();
-            let x2 = scran.initializeSparseMatrixFromRds(vals2, { layered: layered, consume: true });
-
-            for (var c = 0; c < x.numberOfColumns(); c++) {
-                expect(compare.equalArrays(x.column(c), x2.column(c))).toBe(true);
-            }
-
-            x2.free();
-            vals2.free();
-            stuff2.free();
-        }
-
         x.free();
     }
 
@@ -131,21 +116,6 @@ maybe("works for dgTMatrix", () => {
         test_okayish(x, 30, 70, layered);
         expect_all(x, y => y%1 == 0); // forced integers.
 
-        // Same results in consumption mode.
-        {
-            let stuff2 = scran.readRds(rpath);
-            let vals2 = stuff2.value();
-            let x2 = scran.initializeSparseMatrixFromRds(vals2, { layered: layered, consume: true });
-
-            for (var c = 0; c < x.numberOfColumns(); c++) {
-                expect(compare.equalArrays(x.column(c), x2.column(c))).toBe(true);
-            }
-
-            x2.free();
-            vals2.free();
-            stuff2.free();
-        }
-
         x.free();
     }