输入示例：MKI67 LUAD（MKI67为基因名称；LUAD为肿瘤名。）
Example：MKI67 LUAD（MKI67 refers to gene name；LUAD refers to cancer name.）
图示：ROC评估基因表达对肿瘤组与正常组的诊断效能(TCGA联合GTEx分析)
Figure：ROC shows the prediction efficacy of the gene on tumor/normal group (TCGA-GTEx analysis)
说明：TCGA-LUAD联合GTEx正常组织数据集中MKI67的ROC分析。MKI67的AUC值为0.904，95%置信区间为0.883-0.923。x轴：FPR，假阳性率；y轴：TPR，真阳性率。
Description：ROC analysis of MKI67 in TCGA-LUAD combined with normal tissue dataset in GTEx database. AUC value: 0.904. 95% confidence interval: 0.883 to 0.923. X-axis: FPR, false positive rate; Y-axis: TPR, true positive rate.
方法： 为扩大正常样本量，采用GTEx的正常样本TPM表达量与TCGA肿瘤TPM表达量配对（来自于USCS Xena数据库中的tcga_RSEM_gene_tpm与 gtex_RSEM_gene_tpm数据集）。通过(x-μ)/σ按肿瘤将数据转化为无单位的Z-Score分值，使得数据标准统一化。Z-Score分值可以识别离群值，为预防离群值影响结果的准确性，如果z-score大于3.0或小于-3.0，则该值可归类为离群值予以去除。使用pROC包进行ROC分析，计算95%置信区间、曲线下总面积及绘制平滑ROC曲线，以评估基因的表达对肿瘤组与正常组的诊断性能。
Methods：To expand the normal sample size, TPM expressions of normal GTEx samples are paired with that in TCGA cohort (from the tcga_RSEM_gene_tpm and gtex_RSEM_gene_tpm dataset in the USCS Xena database). The data is standardized by converting the data into unit-free Z-Score values by tumor by (x-μ)/σ. Z-Score can identify outliers. In order to ensure the accuracy, if z-score is greater than 3.0 or less than -3.0, the value can be classified as an outlier and removed. ROC analysis is performed using the "pROC" package to calculate 95% confidence intervals, AUC. ROC curves shows the diagnostic performance of gene expression in the tumor/normal group.
结果： ROC曲线分析提示，采用MKI67的表达预测肺腺癌疾病组与正常组具有很高的准确性，AUC值为0.903，95%置信区间为0.883-0.923
Results： ROC shows the expression of MKI67 has high accuracy in predicting LUAD. AUC value: 0.903. 95% confidence interval: 0.883 to 0.923.
下载链接：tcga_RSEM_gene_tpm | gtex_RSEM_gene_tpm
Download Link：tcga_RSEM_gene_tpm | gtex_RSEM_gene_tpm
图示：基因在肿瘤与正常组表达差异(TCGA联合GTEx分析)
Figure：Differential gene expression between the tumor/normal group (TCGA-GTEx)
说明：TCGA-LUAD联合GTEx正常组织数据集分析MKI67的表达差异。 方框的上端和下端表示值的四分位数范围。方框中的线表示中值。 Wilcoxon Rank Sum Tests比较两组之间的表达量统计学差异。
Description：The expression difference of MKI67 was analyzed in TCGA-LUAD combined with GTEx normal tissue dataset. The top and bottom ends of the box represent the quartile range of values. The line in the box represents the median value. Wilcoxon Rank Sum Tests compare the expression levels between the two groups.
方法： 采用GTEx的正常样本TPM表达量与TCGA肿瘤TPM表达量配对（来自于USCS Xena数据库中的tcga_RSEM_gene_tpm与 gtex_RSEM_gene_tpm数据集）。通过(x-μ)/σ按肿瘤将数据转化为无单位的Z-Score分值，使得数据标准统一化。Z-Score分值可以识别离群值，为预防离群值影响结果的准确性，如果z-score大于3.0或小于-3.0，则该值可归类为离群值予以去除。Wilcoxon Rank Sum Tests比较LUAD数据集中肿瘤组织与TCGA及GTEx正常组织之间的表达量统计学差异。
Methods：TPM expressions of normal GTEx samples are paired with that in TCGA cohort (from the tcga_RSEM_gene_tpm and gtex_RSEM_gene_tpm dataset in the USCS Xena database). The data is standardized by converting the data into unit-free Z-Score values by tumor by (x-μ)/σ. Z-Score can identify outliers. In order to ensure the accuracy, if z-score is greater than 3.0 or less than -3.0, the value can be classified as an outlier and removed. Wilcoxon Rank Sum Tests compare the expression levels between the two groups.
结果：MKI67在肿瘤中表达更高
Results：MKI67 is highly expressed in tumors.
图示： 基因表达对肿瘤组与正常组预测的校准曲线及拟合优度检验(TCGA联合GTEx分析)
Figure. Calibration curve and goodness of fit test on gene expression to predict tumor/normal group (TCGA-GTEx analysis)
说明： x轴表示预测肿瘤组的可能性，y轴表示实际观测为肿瘤组的结果，粉色实线代表MKI67，与对角线虚线的拟合更接近代表理想的预测。
Description： The X-axis represents the likelihood of predicting the tumor group, the Y-axis represents the outcome of the actual observation for the tumor group, the solid pink line represents MKI67, and when the fit to the dashed diagonal line more closely, the prediction more ideal.
方法： 校准曲线描述了拟合模型在预测肿瘤组与实际观察结果之间的一致性方面的校准，拟合优度检验观察是否与理想模型存在差异。
Methods： The calibration curve describes the calibration of the fitted model in terms of the agreement between the predicted tumor group and the actual observations. Goodness of fit tests shows the differences from the ideal model.
结果：MKI67为诊断LUAD的预测因子，且未发现偏离完全拟合（p=0.997），意味着采用MKI67预测是否为肿瘤组织，与理想模型没有显著差异。
Results：MKI67, a predictor for the diagnosis of LUAD, and no deviation from perfect fit was found (p=0.997), suggesting that MKI67 was not significantly different from the ideal model in predicting tumor tissue.
Example：MKI67 LUAD（MKI67 refers to gene name；LUAD refers to cancer name.）
图示：ROC评估基因表达对肿瘤组与正常组的诊断效能(TCGA联合GTEx分析)
Figure：ROC shows the prediction efficacy of the gene on tumor/normal group (TCGA-GTEx analysis)
说明：TCGA-LUAD联合GTEx正常组织数据集中MKI67的ROC分析。MKI67的AUC值为0.904，95%置信区间为0.883-0.923。x轴：FPR，假阳性率；y轴：TPR，真阳性率。
Description：ROC analysis of MKI67 in TCGA-LUAD combined with normal tissue dataset in GTEx database. AUC value: 0.904. 95% confidence interval: 0.883 to 0.923. X-axis: FPR, false positive rate; Y-axis: TPR, true positive rate.
方法： 为扩大正常样本量，采用GTEx的正常样本TPM表达量与TCGA肿瘤TPM表达量配对（来自于USCS Xena数据库中的tcga_RSEM_gene_tpm与 gtex_RSEM_gene_tpm数据集）。通过(x-μ)/σ按肿瘤将数据转化为无单位的Z-Score分值，使得数据标准统一化。Z-Score分值可以识别离群值，为预防离群值影响结果的准确性，如果z-score大于3.0或小于-3.0，则该值可归类为离群值予以去除。使用pROC包进行ROC分析，计算95%置信区间、曲线下总面积及绘制平滑ROC曲线，以评估基因的表达对肿瘤组与正常组的诊断性能。
Methods：To expand the normal sample size, TPM expressions of normal GTEx samples are paired with that in TCGA cohort (from the tcga_RSEM_gene_tpm and gtex_RSEM_gene_tpm dataset in the USCS Xena database). The data is standardized by converting the data into unit-free Z-Score values by tumor by (x-μ)/σ. Z-Score can identify outliers. In order to ensure the accuracy, if z-score is greater than 3.0 or less than -3.0, the value can be classified as an outlier and removed. ROC analysis is performed using the "pROC" package to calculate 95% confidence intervals, AUC. ROC curves shows the diagnostic performance of gene expression in the tumor/normal group.
结果： ROC曲线分析提示，采用MKI67的表达预测肺腺癌疾病组与正常组具有很高的准确性，AUC值为0.903，95%置信区间为0.883-0.923
Results： ROC shows the expression of MKI67 has high accuracy in predicting LUAD. AUC value: 0.903. 95% confidence interval: 0.883 to 0.923.
下载链接：tcga_RSEM_gene_tpm | gtex_RSEM_gene_tpm
Download Link：tcga_RSEM_gene_tpm | gtex_RSEM_gene_tpm
图示：基因在肿瘤与正常组表达差异(TCGA联合GTEx分析)
Figure：Differential gene expression between the tumor/normal group (TCGA-GTEx)
说明：TCGA-LUAD联合GTEx正常组织数据集分析MKI67的表达差异。 方框的上端和下端表示值的四分位数范围。方框中的线表示中值。 Wilcoxon Rank Sum Tests比较两组之间的表达量统计学差异。
Description：The expression difference of MKI67 was analyzed in TCGA-LUAD combined with GTEx normal tissue dataset. The top and bottom ends of the box represent the quartile range of values. The line in the box represents the median value. Wilcoxon Rank Sum Tests compare the expression levels between the two groups.
方法： 采用GTEx的正常样本TPM表达量与TCGA肿瘤TPM表达量配对（来自于USCS Xena数据库中的tcga_RSEM_gene_tpm与 gtex_RSEM_gene_tpm数据集）。通过(x-μ)/σ按肿瘤将数据转化为无单位的Z-Score分值，使得数据标准统一化。Z-Score分值可以识别离群值，为预防离群值影响结果的准确性，如果z-score大于3.0或小于-3.0，则该值可归类为离群值予以去除。Wilcoxon Rank Sum Tests比较LUAD数据集中肿瘤组织与TCGA及GTEx正常组织之间的表达量统计学差异。
Methods：TPM expressions of normal GTEx samples are paired with that in TCGA cohort (from the tcga_RSEM_gene_tpm and gtex_RSEM_gene_tpm dataset in the USCS Xena database). The data is standardized by converting the data into unit-free Z-Score values by tumor by (x-μ)/σ. Z-Score can identify outliers. In order to ensure the accuracy, if z-score is greater than 3.0 or less than -3.0, the value can be classified as an outlier and removed. Wilcoxon Rank Sum Tests compare the expression levels between the two groups.
结果：MKI67在肿瘤中表达更高
Results：MKI67 is highly expressed in tumors.
图示： 基因表达对肿瘤组与正常组预测的校准曲线及拟合优度检验(TCGA联合GTEx分析)
Figure. Calibration curve and goodness of fit test on gene expression to predict tumor/normal group (TCGA-GTEx analysis)
说明： x轴表示预测肿瘤组的可能性，y轴表示实际观测为肿瘤组的结果，粉色实线代表MKI67，与对角线虚线的拟合更接近代表理想的预测。
Description： The X-axis represents the likelihood of predicting the tumor group, the Y-axis represents the outcome of the actual observation for the tumor group, the solid pink line represents MKI67, and when the fit to the dashed diagonal line more closely, the prediction more ideal.
方法： 校准曲线描述了拟合模型在预测肿瘤组与实际观察结果之间的一致性方面的校准，拟合优度检验观察是否与理想模型存在差异。
Methods： The calibration curve describes the calibration of the fitted model in terms of the agreement between the predicted tumor group and the actual observations. Goodness of fit tests shows the differences from the ideal model.
结果：MKI67为诊断LUAD的预测因子，且未发现偏离完全拟合（p=0.997），意味着采用MKI67预测是否为肿瘤组织，与理想模型没有显著差异。
Results：MKI67, a predictor for the diagnosis of LUAD, and no deviation from perfect fit was found (p=0.997), suggesting that MKI67 was not significantly different from the ideal model in predicting tumor tissue.
