AG-1328: improve handling of target nominations table display strings

src/app/features/genes/components/gene-nominated-targets/gene-nominated-targets.component.spec.ts

@@ -10,13 +10,22 @@ import { HttpClientTestingModule } from '@angular/common/http/testing';
 // -------------------------------------------------------------------------- //
 import { GeneNominatedTargetsComponent } from './';
 import { ApiService, HelperService } from '../../../../core/services';
+import { Gene, GenesResponse } from '../../../../models';
+import { geneMock1, targetNominationMock1 } from '../../../../testing/gene-mocks';
+import { of } from 'rxjs';
 
 // -------------------------------------------------------------------------- //
 // Tests
 // -------------------------------------------------------------------------- //
 describe('Component: Gene Nominated Targets', () => {
   let fixture: ComponentFixture<GeneNominatedTargetsComponent>;
   let component: GeneNominatedTargetsComponent;
+  let element: HTMLElement;
+  let mockApiService: ApiService;
+
+  const COLUMN_INDICES = {
+    'cohort_study': 4
+  };
 
   beforeEach(async () => {
     await TestBed.configureTestingModule({
@@ -29,10 +38,120 @@ describe('Component: Gene Nominated Targets', () => {
   beforeEach(async () => {
     fixture = TestBed.createComponent(GeneNominatedTargetsComponent);
     component = fixture.componentInstance;
-    fixture.detectChanges();
   });
 
+  const setUp = (genes: Gene[]) => {
+    const genesResponse: GenesResponse = {
+      items: genes
+    };
+    mockApiService = TestBed.inject(ApiService);
+    spyOn(mockApiService, 'getNominatedGenes').and.returnValue(
+      of(genesResponse)
+    );
+    fixture.detectChanges();
+    element = fixture.nativeElement;
+
+    expect(mockApiService.getNominatedGenes).toHaveBeenCalled();
+
+    const table = element.querySelector('table');
+    expect(table).not.toBeNull();
+
+    const rows = Array.from(
+      table?.querySelectorAll('tbody tr') || []
+    ) as HTMLTableRowElement[];
+
+    return { rows };
+  };
+
   it('should create', () => {
+    setUp([geneMock1]);
     expect(component).toBeTruthy();
   });
+
+  it('should not show null study values', () => {
+    const gene: Gene = {
+      ...geneMock1,
+      target_nominations: [
+        { ...targetNominationMock1, study: null },
+        { ...targetNominationMock1, study: 'XYZ Study, ABC Study' },
+        { ...targetNominationMock1, study: '' },
+        { ...targetNominationMock1, study: 'Study 123, Study 456' },
+      ],
+    };
+    const { rows } = setUp([gene]);
+    expect(rows.length).toBe(1);
+
+    const cols = rows[0].cells;
+    expect(cols.length).toBeGreaterThan(COLUMN_INDICES.cohort_study);
+
+    expect(cols[COLUMN_INDICES.cohort_study].textContent?.trim()).toEqual(
+      'ABC Study, Study 123, Study 456, XYZ Study'
+    );
+  });
+
+  it('should display sorted, unique study values', () => {
+    const expectedStudyString = 'ACT, Banner, BLSA, Kronos, MSBB, ROSMAP';
+    const { rows } = setUp([geneMock1]);
+
+    expect(rows.length).toBe(1);
+
+    const cols = rows[0].cells;
+    expect(cols.length).toBeGreaterThan(COLUMN_INDICES.cohort_study);
+
+    expect(cols[COLUMN_INDICES.cohort_study].textContent?.trim()).toEqual(
+      expectedStudyString
+    );
+  });
+
+  it('should correctly flatten comma separated arrays', () => {
+    setUp([]);
+
+    expect(component.commaFlattenArray([])).toEqual([]);
+
+    expect(
+      component.commaFlattenArray(['ACT, BLSA, Banner', 'ACT, BLSA, Banner'])
+    ).toEqual(['ACT', 'BLSA', 'Banner', 'ACT', 'BLSA', 'Banner']);
+
+    expect(component.commaFlattenArray(['A, B, C', 'D', 'E, F, G, H'])).toEqual(
+      ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H']
+    );
+
+    expect(component.commaFlattenArray(['A', 'B', 'C'])).toEqual([
+      'A',
+      'B',
+      'C',
+    ]);
+  });
+
+  it('should correctly format display values', () => {
+    setUp([]);
+
+    expect(
+      component.getCommaSeparatedStringOfUniqueSortedValues([])
+    ).toEqual('');
+
+    expect(
+      component.getCommaSeparatedStringOfUniqueSortedValues([
+        'ACT',
+        'BLSA',
+        'Banner',
+        'ACT',
+        'BLSA',
+        'Banner',
+      ])
+    ).toEqual('ACT, Banner, BLSA');
+
+    expect(
+      component.getCommaSeparatedStringOfUniqueSortedValues([
+        'Z',
+        'Y',
+        'X',
+        'A',
+        'B',
+        'C',
+        'B',
+        'C',
+      ])
+    ).toEqual('A, B, C, X, Y, Z');
+  });
 });

src/app/features/genes/components/gene-nominated-targets/gene-nominated-targets.component.ts

@@ -97,9 +97,9 @@ export class GeneNominatedTargetsComponent implements OnInit {
         // First map all entries nested in the data to a new array
         if (de.target_nominations?.length) {
           teamsArray = de.target_nominations.map((nt: TargetNomination) => nt.team);
-          studyArray = de.target_nominations.map(
-            (nt: TargetNomination) => nt.study
-          );
+          studyArray = de.target_nominations
+            .map((nt: TargetNomination) => nt.study)
+            .filter((item) => Boolean(item)) as string[];
           programsArray = de.target_nominations.map(
             (nt: TargetNomination) => nt.source
           );
@@ -120,37 +120,14 @@ export class GeneNominatedTargetsComponent implements OnInit {
         inputDataArray = this.commaFlattenArray(inputDataArray);
 
         // Populate targetNomination display fields
-        de.teams_display_value = '';
-        if (teamsArray.length) {
-          de.teams_display_value = teamsArray
-            .filter(this.getUnique)
-            .sort((a: string, b: string) => a.localeCompare(b))
-            .join(', ');
-        }
-
-        de.study_display_value = '';
-        if (teamsArray.length) {
-          de.study_display_value = studyArray
-            .filter(this.getUnique)
-            .sort((a: string, b: string) => a.localeCompare(b))
-            .join(', ');
-        }
-
-        de.programs_display_value = '';
-        if (programsArray.length) {
-          de.programs_display_value = programsArray
-            .filter(this.getUnique)
-            .sort((a: string, b: string) => a.localeCompare(b))
-            .join(', ');
-        }
-
-        de.input_data_display_value = '';
-        if (inputDataArray.length) {
-          de.input_data_display_value = inputDataArray
-            .filter(this.getUnique)
-            .sort((a: string, b: string) => a.localeCompare(b))
-            .join(', ');
-        }
+        de.teams_display_value =
+          this.getCommaSeparatedStringOfUniqueSortedValues(teamsArray);
+        de.study_display_value =
+          this.getCommaSeparatedStringOfUniqueSortedValues(studyArray);
+        de.programs_display_value =
+          this.getCommaSeparatedStringOfUniqueSortedValues(programsArray);
+        de.input_data_display_value =
+          this.getCommaSeparatedStringOfUniqueSortedValues(inputDataArray);
 
         de.initial_nomination_display_value = initialNominationArray.length
           ? Math.min(...initialNominationArray)
@@ -189,27 +166,29 @@ export class GeneNominatedTargetsComponent implements OnInit {
     return self.indexOf(value) === index;
   }
 
-  commaFlattenArray(array: any[]): any[] {
-    const finalArray: any[] = [];
-    if (array.length) {
-      array.forEach((t) => {
-        if (t) {
-          const i = t.indexOf(', ');
-          if (i > -1) {
-            const tmpArray = t.split(', ');
-            finalArray.push(tmpArray[0]);
-            finalArray.push(tmpArray[1]);
-          } else {
-            finalArray.push(t);
-          }
-        } else {
-          finalArray.push('');
-        }
-      });
-      array = finalArray;
-    }
+  commaFlattenArray(array: string[]): string[] {
+    const finalArray: string[] = [];
+    array.forEach((t) => {
+      const i = t.indexOf(', ');
+      if (i > -1) {
+        const tmpArray = t.split(', ');
+        tmpArray.forEach((val) => finalArray.push(val));
+      } else {
+        finalArray.push(t);
+      }
+    });
+    return finalArray;
+  }
 
-    return array;
+  getCommaSeparatedStringOfUniqueSortedValues(inputArray: string[]) {
+    let display_value = '';
+    if (inputArray.length) {
+      display_value = inputArray
+        .filter(this.getUnique)
+        .sort((a: string, b: string) => a.localeCompare(b))
+        .join(', ');
+    }
+    return display_value;
   }
 
   onSearch(event: any) {

src/app/models/genes.ts

@@ -19,7 +19,7 @@ export interface TargetNomination {
   predicted_therapeutic_direction: string;
   data_used_to_support_target_selection: string;
   data_synapseid: string;
-  study: string;
+  study: string | null;
   input_data: string;
   validation_study_details: string;
   initial_nomination: number;

src/app/testing/gene-mocks.ts

@@ -1,6 +1,24 @@
 /* eslint-disable */
 
-import { Gene, GCTGene } from '../models';
+import { Gene, GCTGene, TargetNomination } from '../models';
+
+export const targetNominationMock1: TargetNomination = {
+  source: 'Treat-AD',
+  team: 'Emory-Sage-SGC',
+  rank: '7',
+  hgnc_symbol: 'MSN',
+  target_choice_justification:
+    'MSN was identified as a potential driver protein based on protein coexpression analysis. The group of proteins coexpressed with MSN is conserved across the 3 datasets considered, is enriched for inflammatory processes, and for protein products of genes near loci previously associated with AD risk. MSN has increased abundance in AD across all 3 cohorts examined, and progressively increases in asymptomatic (prodromal) AD to symptomatic AD, and also correlates with both hallmark AD pathology scores (CERAD for amyloid burden; and Braak for Tau extent of spread). MSN is highly expressed as a marker of disease-associated microglia and/or endothelial cell types.',
+  predicted_therapeutic_direction:
+    'Antagonism predicted to reduce disease progression. Phosphorylation downstream of Rho/Rock influences actin, focal adhesion binding; may have redundancy with EZR and RDX, complicating targeting. MSN-directed therapeutics that improve microglial motility and/or phagocytosis competence would reduce abeta/amyloid plaque burden.',
+  data_used_to_support_target_selection:
+    'Discovery quantitative proteomics of FrCx \r\n WPCNA of multiple and consensus cohorts\r\n ANOVA',
+  data_synapseid: 'syn17008058',
+  study: 'ACT, BLSA, Banner',
+  input_data: 'Protein',
+  validation_study_details: 'validation studies ongoing',
+  initial_nomination: 2018,
+};
 
 export const geneMock1: Gene = {
   _id: '628ea1be0e8d04279fdbaa26',