使用C++实现lefse分析

LEfSe（Linear discriminant analysis Effect Size）是一种用于发现生物学中不同分类组之间差异的统计方法。下面是使用C++实现LEfSe分析的一个简单示例：

#include <iostream>
#include <fstream>
#include <sstream>
#include <vector>
#include <map>
#include <cmath>

using namespace std;

// 定义一个结构体表示一个样本的特征
struct SampleFeature {
    string classLabel; // 样本的分类标签
    vector<double> features; // 样本的特征向量
};

// 读取样本特征数据
vector<SampleFeature> readSampleFeatures(string filename) {
    vector<SampleFeature> sampleFeatures;
    ifstream file(filename);
    string line;
    while (getline(file, line)) {
        stringstream ss(line);
        SampleFeature sampleFeature;
        string feature;
        getline(ss, sampleFeature.classLabel, ',');
        while (getline(ss, feature, ',')) {
            sampleFeature.features.push_back(stod(feature));
        }
        sampleFeatures.push_back(sampleFeature);
    }
    return sampleFeatures;
}

// 计算两个样本组之间的差异
double computeGroupDifference(vector<SampleFeature>& group1, vector<SampleFeature>& group2) {
    int numFeatures = group1[0].features.size();
    double difference = 0.0;
    for (int i = 0; i < numFeatures; i++) {
        double mean1 = 0.0;
        double mean2 = 0.0;
        for (int j = 0; j < group1.size(); j++) {
            mean1 += group1[j].features[i];
        }
        mean1 /= group1.size();
        for (int j = 0; j < group2.size(); j++) {
            mean2 += group2[j].features[i];
        }
        mean2 /= group2.size();
        difference += pow(mean1 - mean2, 2);
    }
    return sqrt(difference);
}

// 根据特征差异计算特征效果大小
map<string, double> computeEffectSize(vector<SampleFeature>& group1, vector<SampleFeature>& group2) {
    map<string, double> effectSize;
    int numFeatures = group1[0].features.size();
    for (int i = 0; i < numFeatures; i++) {
        double mean1 = 0.0;
        double mean2 = 0.0;
        for (int j = 0; j < group1.size(); j++) {
            mean1 += group1[j].features[i];
        }
        mean1 /= group1.size();
        for (int j = 0; j < group2.size(); j++) {
            mean2 += group2[j].features[i];
        }
        mean2 /= group2.size();
        double std1 = 0.0;
        double std2 = 0.0;
        for (int j = 0; j < group1.size(); j++) {
            std1 += pow(group1[j].features[i] - mean1, 2);
        }
        std1 /= group1.size();
        std1 = sqrt(std1);
        for (int j = 0; j < group2.size(); j++) {
            std2 += pow(group2[j].features[i] - mean2, 2);
        }
        std2 /= group2.size();
        std2 = sqrt(std2);
        double effect = (mean1 - mean2) / sqrt(std1 * std1 / group1.size() + std2 * std2 / group2.size());
        effectSize[to_string(i)] = effect;
    }
    return effectSize;
}

int main() {
    // 读取样本特征数据
    vector<SampleFeature> sampleFeatures = readSampleFeatures("sample_features.csv");

    // 将样本按照分类标签分组
    map<string, vector<SampleFeature>> groups;
    for (int i = 0; i < sampleFeatures.size(); i++) {
        groups[sampleFeatures[i].classLabel].push_back(sampleFeatures[i]);
    }

    // 计算组间差异和特征效果大小
    map<string, double> groupDifferences;
    map<string, map<string, double>> featureEffectSizes;
    for (auto it1 = groups.begin(); it1 != groups.end(); it1++) {
        for (auto it2 = groups.begin(); it2 != groups.end(); it2++) {
            if (it1 != it2) {
                double difference = computeGroupDifference(it1->second, it2->second);
                groupDifferences[it1->first + " vs " + it2->first] = difference;
                map<string, double> effectSize = computeEffectSize(it1->second, it2->second);
                featureEffectSizes[it1->first + " vs " + it2->first] = effectSize;
            }
        }
    }

    // 输出结果
    cout << "Group Differences:" << endl;
    for (auto it = groupDifferences.begin(); it != groupDifferences.end(); it++) {
        cout << it->first << ": " << it->second << endl;
    }
    cout << endl;

    cout << "Feature Effect Sizes:" << endl;
    for (auto it1 = featureEffectSizes.begin(); it1 != featureEffectSizes.end(); it1++) {
        cout << it1->first << ":" << endl;
        for (auto it2 = it1->second.begin(); it2 != it1->second.end(); it2++) {
            cout << "Feature " << it2->first << ": " << it2->second << endl;
        }
        cout << endl;
    }

    return 0;
}

上述代码实现了一个简单的LEfSe分析，包括样本特征数据的读取、组间差异的计算和特征效果大小的计算。你可以根据实际情况修改代码以适应你的数据格式和需求。