Let getter, rather than vocabApi method, handle scge requests

Author: creilly8

client/plots/scatter/model/scatterModel.ts

@@ -15,7 +15,7 @@ import type {
 //icons have size 16x16
 export const shapes = shapesArray
 
-const numberOfSamplesCutoff = 20000 // if map is greater than cutoff, switch from svg to canvas rendering
+const maxSvgSamplesCutoff = 20000 // if map is greater than cutoff, switch from svg to canvas rendering
 const noExpColor = '#F5F5F5' //light gray
 const expColor = '#ff000d' //default color for gene expression
 
@@ -85,16 +85,13 @@ export class ScatterModel {
 			if ('error' in data) throw data.error
 
 			this.charts = []
-			//This is not preferable.
-			if (reqOpts.colorTW?.term.type == SINGLECELL_GENE_EXPRESSION) {
-				this.processGEData(data['plots'])
-			} else {
-				this.range = data.range
-				for (const [key, chartData] of Object.entries(data.result)) {
-					if (!Array.isArray(chartData.samples)) throw 'data.samples[] not array'
-					this.createChart(key, chartData)
-				}
+
+			this.range = data.range
+			for (const [key, chartData] of Object.entries(data.result)) {
+				if (!Array.isArray(chartData.samples)) throw 'data.samples[] not array'
+				this.createChart(key, chartData)
 			}
+
 			this.is3D = this.scatter.config.term0?.q.mode == 'continuous'
 			this.initRanges()
 		} catch (e: any) {
@@ -106,57 +103,19 @@ export class ScatterModel {
 
 	createChart(id: string, data: ScatterDataResult) {
 		const cohortSamples: any[] = data.samples.filter(sample => 'sampleId' in sample)
-		if (cohortSamples.length > numberOfSamplesCutoff) this.is2DLarge = true
+		if (cohortSamples.length > maxSvgSamplesCutoff) this.is2DLarge = true
 		const colorLegend: Map<string, ColorLegendItem> = new Map(data.colorLegend)
 		const shapeLegend: Map<string, ShapeLegendItem> = new Map(data.shapeLegend)
 		this.charts.push({ id, data, cohortSamples, colorLegend, shapeLegend })
 	}
 
-	processGEData(plots: any) {
-		const plotsCopy = structuredClone(plots)
-		let xMin = Infinity,
-			xMax = -Infinity,
-			yMin = Infinity,
-			yMax = -Infinity
-		for (const plot of plotsCopy) {
-			plot.data = plot.data || {}
-			plot.id = plot.name
-			let plotMax = -Infinity,
-				plotMin = Infinity,
-				plotGEMin = Infinity,
-				plotGEMax = -Infinity
-			for (const cell of plot.expCells) {
-				xMin = Math.min(xMin, cell.x)
-				xMax = Math.max(xMax, cell.x)
-				yMin = Math.min(yMin, cell.y)
-				yMax = Math.max(yMax, cell.y)
-				plotMin = Math.min(plotMin, cell.x)
-				plotMax = Math.max(plotMax, cell.y)
-				plotGEMin = Math.min(plotGEMin, cell.geneExp)
-				plotGEMax = Math.max(plotGEMax, cell.geneExp)
-				cell.shape = 'Ref'
-			}
-			plot.min = plotMin
-			plot.max = plotMax
-			plot.geMin = plotGEMin
-			plot.geMax = plotGEMax
-			plot.data.samples = plot.expCells.concat(plot.noExpCells)
-			plot.cohortSamples = plot.expCells.concat(plot.noExpCells)
-			plot.colorLegend = new Map()
-			plot.shapeLegend = new Map()
-			plot.shapeLegend.set('Ref', { shape: 0, key: 'Ref', sampleCount: plot.expCells.length })
-			this.charts.push(plot)
-		}
-		this.range = { xMin, xMax, yMin, yMax }
-	}
-
 	async initRanges() {
 		let samples: any[] = []
 		for (const chart of this.charts) samples = samples.concat(chart.data.samples)
-		if (samples.length > numberOfSamplesCutoff) this.is2DLarge = true
+		if (samples.length > maxSvgSamplesCutoff) this.is2DLarge = true
 		if (samples.length == 0) return
 		const s0 = samples[0] //First sample to start reduce comparisons
-		const [xMin, xMax, yMin, yMax, zMin, zMax, scaleMin, scaleMax] = samples.reduce(
+		const [xMin, xMax, yMin, yMax, zMin, zMax, scaleMin, scaleMax, geMin, geMax] = samples.reduce(
 			(s, d) => [
 				d.x < s[0] ? d.x : s[0],
 				d.x > s[1] ? d.x : s[1],
@@ -165,9 +124,11 @@ export class ScatterModel {
 				d.z < s[4] ? d.z : s[4],
 				d.z > s[5] ? d.z : s[5],
 				'scale' in d ? (d.scale < s[6] ? d.scale : s[6]) : Number.POSITIVE_INFINITY,
-				'scale' in d ? (d.scale > s[7] ? d.scale : s[7]) : Number.NEGATIVE_INFINITY
+				'scale' in d ? (d.scale > s[7] ? d.scale : s[7]) : Number.NEGATIVE_INFINITY,
+				'geneExp' in d ? (d.geneExp < s[8] ? d.geneExp : s[8]) : Number.POSITIVE_INFINITY,
+				'geneExp' in d ? (d.geneExp > s[9] ? d.geneExp : s[9]) : Number.NEGATIVE_INFINITY
 			],
-			[s0.x, s0.x, s0.y, s0.y, s0.z, s0.z, s0.scale, s0.scale]
+			[s0.x, s0.x, s0.y, s0.y, s0.z, s0.z, s0.scale, s0.scale, s0.geneExp, s0.geneExp]
 		)
 		const settings = this.scatter.settings
 		for (const chart of this.charts) {
@@ -179,7 +140,9 @@ export class ScatterModel {
 				zMin,
 				zMax,
 				scaleMin,
-				scaleMax
+				scaleMax,
+				geMin,
+				geMax
 			}
 		}
 	}
@@ -258,7 +221,7 @@ export class ScatterModel {
 		if (this.scatter.config.colorTW?.term.type == SINGLECELL_GENE_EXPRESSION) {
 			let color
 			if (!c.geneExp) color = noExpColor
-			else if (c.geneExp > chart.geMax) color = expColor
+			else if (c.geneExp > chart.ranges.geMax) color = expColor
 			else color = chart.colorGenerator(c.geneExp)
 			return color
 		}
@@ -404,15 +367,14 @@ export class ScatterModel {
 					? expColor
 					: config.colorTW?.term.continuousColorScale?.maxColor || gradientColor.darker().toString()
 		}
-
 		// Handle continuous color scaling when color term wrapper is in continuous mode
 		if (config.colorTW?.q.mode === 'continuous') {
 			// Extract and sort all sample values for our calculations
 			// We filter out any values that are explicitly defined in the term values
 			// This gives us the raw numerical data we need for scaling
 			let colorValues
 			if (config.colorTW.term.type == SINGLECELL_GENE_EXPRESSION) {
-				colorValues = [chart.geMin, chart.geMax]
+				colorValues = [chart.ranges.geMin, chart.ranges.geMax]
 			} else {
 				colorValues = chart.cohortSamples
 					.filter(
@@ -452,7 +414,6 @@ export class ScatterModel {
 					// order just get the first and last values
 					break
 			}
-
 			// Create the color generator using d3's linear scale
 			// This maps our numerical range to a color gradient
 

client/plots/scatter/scatterTypes.ts

@@ -69,6 +69,8 @@ export type ScatterChart = {
 		zMax?: number
 		scaleMin?: number
 		scaleMax?: number
+		geMin?: number
+		geMax?: number
 	}
 	id: any
 	cohortSamples: any

client/plots/scatter/test/scatter.integration.spec.js

@@ -12,15 +12,13 @@ import {
 	sleep
 } from '../../../test/test.helpers'
 import { openSummaryPlot, openPlot, getSamplelstTW } from '../../../mass/groups.js'
-import { rgb } from 'd3-color'
 import { mclass } from '#shared/common.js'
 import {
-	getSamplelstTw,
-	getCategoryGroupsetting,
 	getGenesetMutTw,
 	getGeneVariantTw,
 	getSsgseaTw,
-	getScgeneexpTw
+	getScgeneexpTw,
+	getScctTw
 } from '../../../test/testdata/data.ts'
 
 /* Tests:
@@ -1293,6 +1291,7 @@ tape('singlecell map', function (test) {
 			plots: [
 				{
 					chartType: 'sampleScatter',
+					colorTW: getScctTw(),
 					singleCellPlot: { name: 'scRNA', sample: { sID: '1_patient' } }
 				}
 			]

client/termdb/TermdbVocab.js

@@ -2,7 +2,7 @@ import { Vocab } from './Vocab'
 import { getNormalRoot } from '#filter'
 import { isUsableTerm } from '#shared/termdb.usecase.js'
 import { throwMsgWithFilePathAndFnName } from '../dom/sayerror'
-import { isDictionaryType, SINGLECELL_GENE_EXPRESSION } from '#shared/terms.js'
+import { isDictionaryType } from '#shared/terms.js'
 
 export class TermdbVocab extends Vocab {
 	// migrated from termdb/store
@@ -1041,13 +1041,6 @@ export class TermdbVocab extends Vocab {
 		if (opts.divideByTW) body.divideByTW = this.getTwMinCopy(opts.divideByTW)
 		if (opts.scaleDotTW) body.scaleDotTW = this.getTwMinCopy(opts.scaleDotTW)
 		body.excludeOutliers = opts.excludeOutliers
-		if (opts.singleCellPlot && opts.colorTW?.term.type == SINGLECELL_GENE_EXPRESSION) {
-			// required by legacy singlecell. delete when that's retired
-			body.gene = opts.colorTW.term.gene
-			body.plots = [opts.singleCellPlot.name]
-			body.sample = opts.singleCellPlot.sample
-			return await this.dofetch3('termdb/singlecellData', { headers, body, signal })
-		}
 		return await this.dofetch3('termdb/sampleScatter', { headers, body, signal })
 	}
 

server/routes/termdb.sampleScatter.ts

@@ -22,7 +22,7 @@ import { authApi } from '../src/auth.js'
 import { run_R } from '@sjcrh/proteinpaint-r'
 import { read_file } from '../src/utils.js'
 import { getDescrStats } from '#routes/termdb.descrstats.ts'
-import { SINGLECELL_CELLTYPE, SINGLECELL_GENE_EXPRESSION } from '#shared/terms.js'
+import { SINGLECELL_GENE_EXPRESSION, isSingleCellTerm } from '#shared/terms.js'
 
 export const api: RouteApi = {
 	endpoint: 'termdb/sampleScatter',
@@ -158,12 +158,13 @@ async function getSingleCellScatter(req, res, ds) {
 	const { name, sample } = q.singleCellPlot
 	try {
 		const tw = q.colorTW as any // not using "TermWrapper" due to tsc err
+		if (tw && !isSingleCellTerm(tw.term))
+			throw new Error('colorTW must be a single cell term for single cell scatter plot')
 		const arg: any = { plots: [name], sample }
-		if (tw) {
-			if (tw.term.type == SINGLECELL_GENE_EXPRESSION) arg.gene = tw.term.gene
-			else if (tw.term.type == SINGLECELL_CELLTYPE) arg.colorBy = tw.term.name
-			else throw new Error('unsupported tw')
-		}
+
+		if (tw.term.type == SINGLECELL_GENE_EXPRESSION) arg.gene = tw.term.gene
+		else arg.colorBy = tw.term.name //SINGLECELL_CELLTYPE
+
 		const data = await ds.queries.singleCell.data.get(arg)
 
 		const plot = data.plots[0]
@@ -180,7 +181,8 @@ async function getSingleCellScatter(req, res, ds) {
 				z: 0,
 				category: cell.category,
 				shape: 'Ref',
-				hidden
+				hidden,
+				geneExp: cell.geneExp
 			}
 		})
 		const [xMin, xMax, yMin, yMax] = samples.reduce(
@@ -189,13 +191,16 @@ async function getSingleCellScatter(req, res, ds) {
 		)
 		const categories: any = new Set(samples.map(s => s.category))
 		const colorMap = {}
-		const k2c = getColors(categories.size)
-		for (const category of categories) {
-			const color = k2c(category)
-			colorMap[category] = {
-				sampleCount: samples.filter((s: any) => s.category == category).length,
-				color,
-				key: category
+
+		if (tw.term.type != SINGLECELL_GENE_EXPRESSION) {
+			const k2c = getColors(categories.size)
+			for (const category of categories) {
+				const color = k2c(category)
+				colorMap[category] = {
+					sampleCount: samples.filter((s: any) => s.category == category).length,
+					color,
+					key: category
+				}
 			}
 		}
 		const shapeLegend: ShapeLegendEntry[] = [['Ref', { sampleCount: samples.length, shape: 0, key: 'Ref' }]]