전체 글 썸네일형 리스트형 Batch effect 배치 효과 없애는 방법론 Methods that remove batch effects while retaining group differences may lead to exaggerated confidence in downstream analyseshttps://academic.oup.com/biostatistics/article/17/1/29/1744261/Methods-that-remove-batch-effects-while-retaining SVA package https://www.bioconductor.org/packages/devel/bioc/vignettes/sva/inst/doc/sva.pdfhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC3307112/ 더보기 Principal component analysis (PCA, 주성분 분석) http://tongtongsear.tistory.com http://www.cs.otago.ac.nz/cosc453/student_tutorials/principal_components.pdf 더보기 integrative modeling of multi-omics data to identify cancer drivers and infer patient-specific gene activity integrative modeling of multi-omics data to identify cancer drivers and infer patient-specific gene activity 더보기 TMM normalization y=DGEList(counts=matrix)y=calcNormFactors(y)tmm_mat=cpm(y,normalized.lib.sizes=TRUE) https://support.bioconductor.org/p/77193/ I don't think it's clear what you are asking for. Let's assume that y is your DGEList with your count data, which you already called calcNormFactors on. Are you after the TMM normalization factors? These are stored in your y$samples$norm.factors column. Do you just want .. 더보기 htseq-count http://www-huber.embl.de/HTSeq/doc/count.html RNAseq의 mapping read count 구하는 것 htseq-count -s no -a 0 -t gene -i gene_name -f bam hbec30.rnaseq.r1.fastq.bam ../example/reference/gencode.v19.annotation.gtf > hbec30.r1.count -t : gtf 파일의 3번 째 column의 feature 중 무엇을 쓸 것인지 (defalut exon)-s : stranded data인지-f : input format-a : mapping quality filter (skip all reads with alignment quality lower than .. 더보기 GENCODE https://www.gencodegenes.org/releases/19.html# GTFGFF 더보기 PCA tutorial # PCA start # I recommend that you set scale TRUE when doing PCA. Since variance influence the resulting PCs. # Center=TRUE is to make median=0 and scale.=TRUE change unit variance equal nc2=t(nc2) # Transpose the log2-CPM matrix, samples should be row and variables are on column# Row : Samples# Column : Variables such as genes pc=prcomp(nc2,center = T,scale. = T) print(pc) # You will see contri.. 더보기 Computing in Biotechnology: Omics and Beyond http://www.cell.com/trends/biotechnology/fulltext/S0167-7799(17)30060-4 더보기 이전 1 ··· 99 100 101 102 103 104 105 ··· 108 다음