the overall size of TCGA dataset is approximately 3.87PB, and one single histological slide in svs format is of GB level, so do get hard drives of TB, better a NAS system as well as a blue-ray disc burning system If the size of the files to be downloaded is more than 5Gb, use of...
dataset, aes(x=logFC_limma,y=logFC_edgeR))+geom_point()+ xlab("logFC_limma")+ylab("logFC_edgeR")+ theme(legend.title=element_blank(),axis.title=element_text(size=10),axis.text=element_text(size=10), legend.text=element_text(size=20)) dev.off() cor(dataset$logFC_edgeR, dataset...
说明：TCGA-LUAD数据集中MKI67的ROC分析。MKI67的AUC值为0.958，95%置信区间为0.936-0.976。x轴：FPR，假阳性率；y轴：TPR，真阳性率。Description：ROC analysis of MKI67 in TCGA-LUAD dataset. The AUC value of MKI67 is 0.958 and the 95% confidence interval is 0.936-0.976. X-axis: ...
Description:ROC analysis of MKI67 in TCGA-LUAD dataset. The AUC value of MKI67 is 0.958 and the 95% confidence interval is 0.936-0.976. X-axis: FPR, false positive rate; Y-axis: TPR, true positive rate.方法:使用pROC包进行ROC分析,计算95%置信区间、曲线下总面积及绘制平滑ROC曲线,以评估...
This research delves into breast cancer staging, classification, and diagnosis by leveraging the comprehensive dataset provided by the The Cancer Genome Atlas (TCGA). By integrating advanced machine learning algorithms with bioinformatics analysis, it introduces a cutting-edge methodology for identifying ...
美国东部 2:“https://datasettcga.blob.core.windows.net/dataset” 使用条款 可随意使用该数据。 有关详细信息和引文详情,请参阅TCGA 计划页 联系人 有关TCGA 数据和计划的问题:https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga/contact ...
使用DESeq进行差异表达分析,返回 results可用的DESeqDataSet对象 > dds <- DESeq(dds)#DESeq进行标准化;estimating size factorsestimating dispersionsgene-wise dispersion estimatesmean-dispersion relationshipfinal dispersion estimatesfitting model and testing-- replacing outliers and refitting for 2819 genes-- DE...
dds <- DESeqDataSetFromMatrix(countData = expr, colData = Data, design = ~ group) #第二步:开始差异分析 dds2 <- DESeq(dds) res <- results(dds2, contrast=c("group", "Tumor", "Normal"))#肿瘤在前,对照在后 ##或者res= results(dds) ...
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expr$dataset <- gsub(pattern = ".mRNA", replacement = "", expr$dataset) #paste0函数为字符串连接函数 expr$bcr_patient_barcode <- paste0(expr$dataset, c(1:590, 1:561, 1:154)) print(expr) 1. 2. 3. 4. 5. 6. 结果如下: ...