Add tests for preprocessed dot plots

Author: eweitz

test/js/visualization/dot-plot-integration.test.js

@@ -0,0 +1,193 @@
+/**
+ * Integration tests for DotPlot component with precomputed data
+ */
+
+import '@testing-library/jest-dom/extend-expect'
+import * as ScpApi from 'lib/scp-api'
+
+import {
+  PRECOMPUTED_DOT_PLOT_DATA,
+  FEATURE_FLAGS_ENABLED,
+  FEATURE_FLAGS_DISABLED
+} from './dot-plot-precompute.test-data'
+
+describe('DotPlot Integration with Feature Flag', () => {
+  let fetchMorpheusJsonSpy
+
+  beforeEach(() => {
+    jest.clearAllMocks()
+    fetchMorpheusJsonSpy = jest.spyOn(ScpApi, 'fetchMorpheusJson')
+  })
+
+  afterEach(() => {
+    jest.restoreAllMocks()
+  })
+
+  describe('fetchMorpheusJson endpoint selection', () => {
+    it('uses dotplot endpoint when feature flag is enabled', async () => {
+      fetchMorpheusJsonSpy.mockResolvedValue([PRECOMPUTED_DOT_PLOT_DATA, {}])
+
+      await ScpApi.fetchMorpheusJson(
+        'SCP1234',
+        ['CD3D', 'CD79A'],
+        'cluster_1',
+        'cell_type',
+        'group',
+        'study',
+        'all',
+        false, // mock
+        true // isPrecomputed
+      )
+
+      // Verify the API was called with correct parameters
+      expect(fetchMorpheusJsonSpy).toHaveBeenCalledWith(
+        'SCP1234',
+        ['CD3D', 'CD79A'],
+        'cluster_1',
+        'cell_type',
+        'group',
+        'study',
+        'all',
+        false,
+        true
+      )
+    })
+
+    it('uses morpheus endpoint when feature flag is disabled', async () => {
+      fetchMorpheusJsonSpy.mockResolvedValue([{}, {}])
+
+      await ScpApi.fetchMorpheusJson(
+        'SCP1234',
+        ['CD3D', 'CD79A'],
+        'cluster_1',
+        'cell_type',
+        'group',
+        'study',
+        'all',
+        false, // mock
+        false // isPrecomputed
+      )
+
+      expect(fetchMorpheusJsonSpy).toHaveBeenCalledWith(
+        'SCP1234',
+        ['CD3D', 'CD79A'],
+        'cluster_1',
+        'cell_type',
+        'group',
+        'study',
+        'all',
+        false,
+        false
+      )
+    })
+  })
+
+  describe('Data format detection', () => {
+    it('detects precomputed data format correctly', () => {
+      const data = PRECOMPUTED_DOT_PLOT_DATA
+
+      // Check if data matches precomputed format
+      const isPrecomputed = !!(data.annotation_name && data.values && data.genes)
+      expect(isPrecomputed).toBe(true)
+    })
+
+    it('does not detect standard Morpheus format as precomputed', () => {
+      const standardData = {
+        rows: ['Gene1', 'Gene2'],
+        columns: ['Cell1', 'Cell2'],
+        data: [[1, 2], [3, 4]]
+      }
+
+      const isPrecomputed = !!(standardData.annotation_name && standardData.values && standardData.genes)
+      expect(isPrecomputed).toBe(false)
+    })
+  })
+
+  describe('Endpoint URL construction', () => {
+    it('constructs dotplot endpoint URL correctly', () => {
+      const studyAccession = 'SCP1234'
+      const isPrecomputed = true
+
+      // Test the endpoint selection logic
+      const endpoint = isPrecomputed ? 'dotplot' : 'morpheus'
+      expect(endpoint).toBe('dotplot')
+
+      const expectedUrl = `/studies/${studyAccession}/expression/${endpoint}`
+      expect(expectedUrl).toContain('dotplot')
+      expect(expectedUrl).not.toContain('morpheus')
+    })
+
+    it('constructs morpheus endpoint URL correctly when flag is off', () => {
+      const studyAccession = 'SCP1234'
+      const isPrecomputed = false
+      const endpoint = isPrecomputed ? 'dotplot' : 'morpheus'
+
+      expect(endpoint).toBe('morpheus')
+
+      const expectedUrl = `/studies/${studyAccession}/expression/${endpoint}`
+      expect(expectedUrl).toContain('morpheus')
+      expect(expectedUrl).not.toContain('dotplot')
+    })
+  })
+
+  describe('Feature flag integration', () => {
+    it('uses feature flag value to determine endpoint', () => {
+      // When flag is true
+      let usePrecomputed = FEATURE_FLAGS_ENABLED.dot_plot_preprocessing_frontend
+      expect(usePrecomputed).toBe(true)
+
+      // When flag is false
+      usePrecomputed = FEATURE_FLAGS_DISABLED.dot_plot_preprocessing_frontend
+      expect(usePrecomputed).toBe(false)
+    })
+
+    it('defaults to false when flag is undefined', () => {
+      const flags = {}
+      const usePrecomputed = flags?.dot_plot_preprocessing_frontend || false
+      expect(usePrecomputed).toBe(false)
+    })
+  })
+
+  describe('Color scheme configuration', () => {
+    it('uses relative scaling for raw expression values', () => {
+      const colorScheme = {
+        colors: ['#0000BB', '#CC0088', '#FF0000'],
+        values: [0, 0.5, 1],
+        scalingMode: 'relative'
+      }
+
+      expect(colorScheme.scalingMode).toBe('relative')
+      expect(colorScheme.colors).toHaveLength(3)
+      expect(colorScheme.values).toEqual([0, 0.5, 1])
+    })
+
+    it('uses fixed color values for blue-purple-red gradient', () => {
+      const colorScheme = {
+        colors: ['#0000BB', '#CC0088', '#FF0000'],
+        values: [0, 0.5, 1],
+        scalingMode: 'relative'
+      }
+
+      // Blue at 0
+      expect(colorScheme.colors[0]).toBe('#0000BB')
+      // Purple at 0.5
+      expect(colorScheme.colors[1]).toBe('#CC0088')
+      // Red at 1
+      expect(colorScheme.colors[2]).toBe('#FF0000')
+    })
+  })
+
+  describe('Size configuration', () => {
+    it('configures sizeBy for percent expressing', () => {
+      const sizeByConfig = {
+        seriesName: 'percent',
+        min: 0,
+        max: 75
+      }
+
+      expect(sizeByConfig.seriesName).toBe('percent')
+      expect(sizeByConfig.min).toBe(0)
+      expect(sizeByConfig.max).toBe(75)
+    })
+  })
+})

test/js/visualization/dot-plot-precompute.test-data.js

@@ -0,0 +1,81 @@
+/**
+ * Test data for dot plot precompute patch tests
+ */
+
+// Mock pre-computed dot plot data from backend
+export const PRECOMPUTED_DOT_PLOT_DATA = {
+  annotation_name: 'cell_type',
+  values: ['T cells', 'B cells', 'NK cells', 'Monocytes'],
+  genes: {
+    'CD3D': [
+      [2.5, 0.95], // T cells: [mean_expression, percent_expressing]
+      [0.1, 0.05], // B cells
+      [0.3, 0.15], // NK cells
+      [0.0, 0.0]   // Monocytes
+    ],
+    'CD79A': [
+      [0.2, 0.1],  // T cells
+      [3.1, 0.98], // B cells
+      [0.1, 0.05], // NK cells
+      [0.0, 0.0]   // Monocytes
+    ],
+    'NKG7': [
+      [1.2, 0.65], // T cells
+      [0.3, 0.15], // B cells
+      [2.8, 0.92], // NK cells
+      [0.5, 0.25]  // Monocytes
+    ],
+    'UBC': [
+      [1.926, 0.9237], // T cells
+      [2.081, 0.969],  // B cells
+      [2.012, 0.7749], // NK cells
+      [3.394, 0.95]    // Monocytes (highest)
+    ]
+  }
+}
+
+// Mock pre-computed data with all zeros for a gene
+export const PRECOMPUTED_WITH_ZEROS = {
+  annotation_name: 'tissue',
+  values: ['Brain', 'Liver', 'Heart'],
+  genes: {
+    'GENE1': [
+      [0, 0],
+      [0, 0],
+      [0, 0]
+    ],
+    'GENE2': [
+      [1.5, 0.8],
+      [2.3, 0.9],
+      [0.8, 0.4]
+    ]
+  }
+}
+
+// Mock pre-computed data with single cell type
+export const PRECOMPUTED_SINGLE_CELL_TYPE = {
+  annotation_name: 'cell_type',
+  values: ['Neurons'],
+  genes: {
+    'GENE1': [[2.5, 0.95]],
+    'GENE2': [[1.8, 0.75]]
+  }
+}
+
+// Expected Morpheus dataset structure after conversion
+export const EXPECTED_MORPHEUS_STRUCTURE = {
+  seriesCount: 2,
+  series0Name: 'Mean Expression',
+  series1Name: 'percent',
+  rowCount: 4, // 4 genes in PRECOMPUTED_DOT_PLOT_DATA
+  columnCount: 4 // 4 cell types
+}
+
+// Mock feature flags
+export const FEATURE_FLAGS_ENABLED = {
+  dot_plot_preprocessing_frontend: true
+}
+
+export const FEATURE_FLAGS_DISABLED = {
+  dot_plot_preprocessing_frontend: false
+}