move readme endpoint types to api folder

frontend/src/api/api.ts

@@ -3,21 +3,19 @@ import {
   convertAnalysisResults,
   convertConvertIds,
   revertAnalysisInputs,
-  type _AnalysisResults,
-  type _ConvertIds,
   type AnalysisInputs,
-  type Species,
+} from "@/api/convert";
+import {
+  type _AnalysisResults,
+  type _ConvertIdsInputs,
+  type _ConvertIdsResults,
 } from "@/api/types";
 
 /** check input list of genes. convert to entrez, check if in-network, etc. */
-export const checkGenes = async (
-  genes: string[],
-  species: Species = "Human",
-) => {
+export const checkGenes = async (params: _ConvertIdsInputs) => {
   const headers = new Headers();
   headers.append("Content-Type", "application/json");
-  const params = { genes, species };
-  const response = await request<_ConvertIds>(
+  const response = await request<_ConvertIdsResults>(
     `${api}/gpz-convert-ids`,
     undefined,
     { method: "POST", headers, body: JSON.stringify(params) },

frontend/src/api/convert.ts

@@ -0,0 +1,133 @@
+/** handle conversion between frontend and backend data formats */
+
+import type {
+  _AnalysisInputs,
+  _AnalysisResults,
+  _ConvertIdsResults,
+} from "@/api/types";
+
+/** backend format to frontend format */
+export const convertConvertIds = (backend: _ConvertIdsResults) => {
+  /** map "couldn't convert" status to easier-to-work-with value */
+  for (const row of backend.df_convert_out)
+    if (row["Entrez ID"].match(/Could Not be mapped to Entrez/i))
+      row["Entrez ID"] = "";
+
+  return {
+    count: backend.input_count,
+    success: backend.df_convert_out.filter((row) => row["Entrez ID"]).length,
+    error: backend.df_convert_out.filter((row) => !row["Entrez ID"]).length,
+    summary: backend.table_summary.map((row) => ({
+      network: row.Network,
+      positiveGenes: row.PositiveGenes,
+      totalGenes: row.NetworkGenes,
+    })),
+    table: backend.df_convert_out.map((row) => ({
+      input: row["Original ID"],
+      entrez: row["Entrez ID"],
+      name: row["Gene Name"],
+      inNetwork:
+        (row["In BioGRID?"] ?? row["In IMP?"] ?? row["In STRING?"]) === "Y",
+    })),
+  };
+};
+
+/** backend format to frontend format */
+export const convertAnalysisInputs = (backend: _AnalysisInputs) => ({
+  name: backend.name,
+  genes: backend.genes,
+  speciesTrain: backend.sp_trn,
+  speciesTest: backend.sp_tst,
+  network: backend.net_type,
+  genesetContext: backend.gsc,
+  negatives: backend.negatives,
+});
+
+/** ml endpoint params frontend format */
+export type AnalysisInputs = ReturnType<typeof convertAnalysisInputs>;
+
+/** frontend format to backend format */
+export const revertAnalysisInputs = (
+  frontend: AnalysisInputs,
+): _AnalysisInputs => ({
+  name: frontend.name,
+  genes: frontend.genes,
+  sp_trn: frontend.speciesTrain,
+  sp_tst: frontend.speciesTest,
+  net_type: frontend.network,
+  gsc: frontend.genesetContext,
+  negatives: frontend.negatives,
+});
+
+/** backend format to frontend format */
+export const convertAnalysisResults = (backend: _AnalysisResults) => ({
+  inputGenes: backend.df_convert_out_subset.map((row) => ({
+    input: row["Original ID"],
+    entrez: row["Entrez ID"].match(/Could Not be mapped to Entrez/i)
+      ? ""
+      : row["Entrez ID"],
+    name: row["Gene Name"],
+    inNetwork:
+      (row["In BioGRID?"] ?? row["In IMP?"] ?? row["In STRING?"]) === "Y",
+  })),
+  crossValidation: backend.avgps,
+  positiveGenes: backend.positive_genes,
+  predictions: backend.df_probs.map((row) => ({
+    entrez: row.Entrez,
+    symbol: row.Symbol,
+    name: row.Name,
+    knownNovel: row["Known/Novel"],
+    classLabel: expandClass(row["Class-Label"]),
+    probability: row.Probability,
+    zScore: row["Z-score"],
+    pAdjusted: row["P-adjusted"],
+    rank: row.Rank,
+  })),
+  similarities: backend.df_sim.map((row) => ({
+    task: row.Task,
+    id: row.ID,
+    name: row.Name,
+    similarity: row.Similarity,
+    zScore: row["Z-score"],
+    pAdjusted: row["P-adjusted"],
+    rank: row.Rank,
+  })),
+  network: {
+    nodes: backend.df_probs.map((row) => ({
+      entrez: row.Entrez,
+      symbol: row.Symbol,
+      name: row.Name,
+      knownNovel: row["Known/Novel"],
+      classLabel: expandClass(row["Class-Label"]),
+      probability: row.Probability,
+      zScore: row["Z-score"],
+      pAdjusted: row["P-adjusted"],
+      rank: row.Rank,
+    })),
+    edges: backend.df_edge.map((row) => ({
+      source: row.Node1,
+      target: row.Node2,
+      weight: row.Weight,
+    })),
+  },
+  neutralInfo: (() => {
+    const { "All Neutrals": all, ...sets } = backend.neutral_gene_info;
+    return {
+      all: Array.isArray(all) ? all : [],
+      sets: Object.entries(sets).flatMap(([Id, value]) =>
+        Array.isArray(value) ? [] : { Id, ...value },
+      ),
+    };
+  })(),
+});
+
+/** ml endpoint params frontend format */
+export type AnalysisResults = ReturnType<typeof convertAnalysisResults>;
+
+/** convert class label abbreviation to full text */
+const expandClass = (
+  abbrev: _AnalysisResults["df_probs"][number]["Class-Label"],
+) => (({ P: "Positive", N: "Negative", U: "Neutral" }) as const)[abbrev];
+
+/** full analysis */
+export type Analysis = { inputs: AnalysisInputs; results: AnalysisResults };