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index.html
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<!DOCTYPE html>
<html lang="en">
<head>
<style>
p {
text-align: center;
}
div {
text-align: center;
}
h1 {
text-align: center;
}
svg {
text-align: center;
}
pre {
text-align: center;
}
body {
background-color: mistyrose;
}
.center-block {
display: block;
margin-right: auto;
margin-left: auto;
}
</style>
<script src="https://cdn.jsdelivr.net/npm/[email protected]/lib/bundle.min.js"></script>
<script>
const LIZ = [
20, 40, 60, 80, 100, 114, 120, 140, 160, 180, 200, 214, 220, 240, 260,
280, 300, 314, 320, 340, 360, 380, 400, 414, 420, 440, 460, 480, 500,
514, 520, 540, 560, 580, 600,
];
const tagDict = {
DATA1: { tagName: "DATA", tagNum: 1, typeToReturn: "getShort" },
DATA2: { tagName: "DATA", tagNum: 2, typeToReturn: "getShort" },
DATA3: { tagName: "DATA", tagNum: 3, typeToReturn: "getShort" },
DATA4: { tagName: "DATA", tagNum: 4, typeToReturn: "getShort" },
DATA205: { tagName: "DATA", tagNum: 205, typeToReturn: "getShort" },
};
// https://github.com/eamitchell/ab1ToJSON
const DIR_SIZE = 28;
class abiConverter {
constructor(buffer) {
this.buffer = buffer;
this.dirLocation = buffer.getInt32(26);
this.numElements = buffer.getInt32(18);
this.lastEntry = this.dirLocation + this.numElements * DIR_SIZE;
}
getTagName(inOffset) {
var name = "";
for (
var loopOffset = inOffset;
loopOffset < inOffset + 4;
loopOffset++
) {
name += String.fromCharCode(this.buffer.getInt8(loopOffset));
}
return name;
}
getNumber(inOffset, numEntries) {
var retArray = [];
for (var counter = 0; counter < numEntries; counter += 1) {
retArray.push(this.buffer.getInt8(inOffset + counter));
}
return retArray;
}
getShort(inOffset, numEntries) {
var retArray = [];
for (var counter = 0; counter < numEntries; counter += 2) {
retArray.push(this.buffer.getInt16(inOffset + counter));
}
return retArray;
}
getData(inTag) {
let output;
let curElem = this.dirLocation;
while (curElem < this.lastEntry) {
let currTagName = this.getTagName(curElem);
let tagNum = this.buffer.getInt32(curElem + 4);
if (currTagName == inTag.tagName && tagNum === inTag.tagNum) {
let numEntries = this.buffer.getInt32(curElem + 16);
let entryOffset = this.buffer.getInt32(curElem + 20);
output = this[inTag.typeToReturn](entryOffset, numEntries);
break;
}
curElem += DIR_SIZE;
}
return output;
}
}
function parseAbifFile(file) {
return new Promise((resolve, reject) => {
const reader = new window.FileReader();
reader.onload = (event) => {
const arrayBuffer = event.target.result;
resolve(arrayBuffer);
};
reader.onerror = (event) => {
reject(event.target.error);
};
reader.readAsArrayBuffer(file);
});
}
findPeaks = (data, threshold, windowSize) => {
// handle cases where current === after and before
// handle where i < halfWindow
let peaks = [];
const halfWindow = Math.floor(windowSize / 2);
// start from index 1 (no peaks at 0 anyways)
for (let i = 1; i < data.length; i++) {
let current = data[i];
if (current >= threshold) {
let before = data[i - 1];
let after = data[i + 1];
if (current > before && current > after) {
let windowArray = data.slice(i - halfWindow, i + halfWindow);
if (current === Math.max(...windowArray)) {
let pair = [i, current];
peaks.push(pair);
}
}
}
}
return peaks;
};
function returnMaximumAllowedDistanceBetweenSizeStandardPeaks(peaks, multiplier = 2) {
const distances = [];
for (let i = 0; i < peaks.length - 1; i++) {
distances.push(peaks[i + 1] - peaks[i]);
}
const meanDistance = distances.reduce((sum, distance) => sum + distance, 0) / distances.length;
let diff = Math.floor(meanDistance * multiplier);
console.log("diff:", diff)
return diff;
}
function generateCombinations(a, length, distance) {
const memo = {};
function dfs(start, path) {
const key = `${start},${path.length}`;
if (memo[key]) return memo[key];
if (path.length === length) return [[]];
const result = [];
for (let i = start; i < a.length; i++) {
const newPath = path.concat(a[i]);
if (!path.length || Math.abs(a[i] - path[path.length - 1]) <= distance) {
const combos = dfs(i + 1, newPath);
for (const combo of combos) {
result.push([a[i], ...combo]);
}
}
}
memo[key] = result;
return result;
}
return dfs(0, []);
}
function pearson(x, y) {
const promedio = (l) => l.reduce((s, a) => s + a, 0) / l.length;
const calc = (v, prom) =>
Math.sqrt(v.reduce((s, a) => s + a * a, 0) - n * prom * prom);
let n = x.length;
let nn = 0;
for (let i = 0; i < n; i++, nn++) {
if ((!x[i] && x[i] !== 0) || (!y[i] && y[i] !== 0)) {
nn--;
continue;
}
x[nn] = x[i];
y[nn] = y[i];
}
if (n !== nn) {
x = x.splice(0, nn);
y = y.splice(0, nn);
n = nn;
}
const prom_x = promedio(x),
prom_y = promedio(y);
return (
(x
.map((e, i) => ({ x: e, y: y[i] }))
.reduce((v, a) => v + a.x * a.y, 0) -
n * prom_x * prom_y) /
(calc(x, prom_x) * calc(y, prom_y))
);
}
function findBestMatch(combinations, ladder) {
let highScoreMatch;
let pearsonScore = 0;
for (comb of combinations) {
let score = pearson(comb, LIZ);
if (score > pearsonScore) {
pearsonScore = score;
highScoreMatch = comb;
}
}
return highScoreMatch;
}
// Fit basepairs to best model
function polynomialFit(x, y, degree) {
if (x.length !== y.length) {
throw new Error("Input arrays x and y must have the same length.");
}
if (degree < 1) {
throw new Error("Degree of the polynomial must be at least 1.");
}
const n = x.length;
const matrix = [];
// Build the matrix of equation coefficients
for (let i = 0; i < degree + 1; i++) {
matrix[i] = [];
for (let j = 0; j < degree + 1; j++) {
let sum = 0;
for (let k = 0; k < n; k++) {
sum += Math.pow(x[k], i + j);
}
matrix[i][j] = sum;
}
}
const rhs = [];
// Build the right-hand side of the equation
for (let i = 0; i < degree + 1; i++) {
let sum = 0;
for (let j = 0; j < n; j++) {
sum += y[j] * Math.pow(x[j], i);
}
rhs[i] = sum;
}
// Solve the system of equations using Gaussian elimination
for (let i = 0; i < degree + 1; i++) {
let maxRow = i;
for (let j = i + 1; j < degree + 1; j++) {
if (Math.abs(matrix[j][i]) > Math.abs(matrix[maxRow][i])) {
maxRow = j;
}
}
const temp = matrix[i];
matrix[i] = matrix[maxRow];
matrix[maxRow] = temp;
const rhsTemp = rhs[i];
rhs[i] = rhs[maxRow];
rhs[maxRow] = rhsTemp;
for (let j = i + 1; j < degree + 1; j++) {
const factor = matrix[j][i] / matrix[i][i];
for (let k = i; k < degree + 1; k++) {
matrix[j][k] -= factor * matrix[i][k];
}
rhs[j] -= factor * rhs[i];
}
}
const coefficients = [];
// Back substitution
for (let i = degree; i >= 0; i--) {
let sum = 0;
for (let j = i + 1; j < degree + 1; j++) {
sum += matrix[i][j] * coefficients[j];
}
coefficients[i] = (rhs[i] - sum) / matrix[i][i];
}
return coefficients;
}
function predict(coefficients, x) {
const degree = coefficients.length - 1;
let result = 0;
for (let i = 0; i <= degree; i++) {
result += coefficients[i] * Math.pow(x, i);
}
return result;
}
function plotData(converter, tag, divName) {
let data = converter.getData(tagDict[tag]);
const s = new dfd.Series(data);
s.plot(divName).line();
}
function plotAllData(converter, coefs) {
let df = new dfd.DataFrame({
data1: converter.getData(tagDict["DATA1"]),
// "data2": converter.getData(tagDict["DATA2"]),
// "data3": converter.getData(tagDict["DATA3"]),
// "data4": converter.getData(tagDict["DATA4"]),
});
df = df.addColumn(
"time",
[...Array(df.shape[0]).keys()].map((num) => num + 1)
);
df = df.addColumn(
"bp",
df.time.$data.map((x) => predict(coefs, x))
);
df.setIndex({ column: "bp", inplace: true });
df.drop({ columns: ["time", "bp"], inplace: true });
df.plot("allData").line();
}
function findAndMatchLadder(ladder, threshold, windowSize = 10) {
let peaks = findPeaks(
ladder,
(threshold = threshold),
(windowSize = windowSize)
);
let peaksIndices = peaks.map((x) => x[0]);
if (peaksIndices.length < LIZ.length) {
alert(
`Too few peaks found!, Number of peaks: ${peaksIndices.length}`
);
return;
}
console.log("peak_indices", peaksIndices);
let maximumDiffs = returnMaximumAllowedDistanceBetweenSizeStandardPeaks(peaksIndices)
let combinations = generateCombinations(
peaksIndices, LIZ.length, maximumDiffs
)
console.log("number of combinations found:", combinations.length)
let bestMatch = findBestMatch(combinations, LIZ)
console.log("best", bestMatch);
return bestMatch;
}
async function parseFsaFile() {
return new Promise((resolve, reject) => {
let file = document.querySelector("#fsaFile").files[0];
parseAbifFile(file)
.then((buffer) => {
const dataView = new DataView(buffer);
let converter = new abiConverter(dataView);
resolve(converter);
})
.catch((error) => {
console.error("An error occurred while parsing the file:", error);
reject(error);
});
});
}
async function runFraggler() {
// Clear all the children
let div = document.getElementById("allData");
div.replaceChildren();
let converter = await parseFsaFile();
let ladder = converter.getData(tagDict["DATA205"]);
let threshold = document.querySelector("#peakThreshold").value;
const bestMatch = findAndMatchLadder(ladder, (threshold = threshold));
coefs = polynomialFit(bestMatch, LIZ, 5);
// ladder 205
plotData(converter, "DATA205", "data1");
plotData(converter, "DATA2", "data2");
plotData(converter, "DATA3", "data3");
plotData(converter, "DATA1", "data4");
plotAllData(converter, coefs);
}
document.addEventListener("DOMContentLoaded", function () {
document.querySelector("#run").addEventListener("click", () => {
runFraggler();
});
});
</script>
<title>Fraggler.js</title>
</head>
<body>
<div>
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<p>DATA1</p>
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<p>DATA2</p>
<div id="data2"></div>
<p>DATA3</p>
<div id="data3"></div>
<p>DATA4</p>
<div id="data4"></div>
<p>All Data</p>
<div id="allData"></div>
</body>
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