3、Install the Graph Database. Please follow the instructions in the Neo4j operations tutorial, specifically the sections “file locations” and “restoring a backup“. 4、If the standard procedure has been followed, the graph database should be accessible via the Neo4j browser at your localhost....
#the last filtering step I used in the tutorial before: mRNA_exprSet <- mRNA_exprSet[,c('gene_name', condition_table$TCGA_IDs)] #We just don't need to remove 'gene_id' column: mRNA_exprSet <- mRNA_exprSet[,c('gene_name', 'gene_id', condition_table$TCGA_IDs)] expression matr...
(1)es: the pathway score we got from GSVA. (2)condition_table_for_limma: the matrix containing the grouping info. It contains 2 columns (1 for normal, another for cancer) in this tutorial. Samples (recorded by rownames) affilifated to this group should be 1, otherwise they are tagged ...
#the last filtering step I used in the tutorial before: mRNA_exprSet <- mRNA_exprSet[,c('gene_name', condition_table$TCGA_IDs)] #We just don't need to remove 'gene_id' column: mRNA_exprSet <- mRNA_exprSet[,c('gene_name', 'gene_id', condition_table$TCGA_IDs)] expression matr...
expression matrix generated by the 1st tutorial (left)-what I will use now for gene ID conversion (right) 1. 超几何检验GO、KEGG基因富集分析 这是相对简单粗暴一些的基因富集分析方法。不需要输入基因的表达值,只需要通过DESeq2和自己设定的阈值(如|log2FoldChange| > 2 & FDR < 0.05)筛选得到的差异...
expression matrix generated by the 1st tutorial (left)-what I will use now for gene ID conversion (right) 1. 超几何检验GO、KEGG基因富集分析 这是相对简单粗暴一些的基因富集分析方法。不需要输入基因的表达值,只需要通过DESeq2和自己设定的阈值(如|log2FoldChange| > 2 & FDR < 0.05)筛选得到的差异...
expression matrix generated by the 1st tutorial (left)-what I will use now for gene ID conversion (right) 1. 超几何检验GO、KEGG基因富集分析 这是相对简单粗暴一些的基因富集分析方法。不需要输入基因的表达值,只需要通过DESeq2和自己设定的阈值(如|log2FoldChange| > 2 & FDR < 0.05)筛选得到的差异...