各种平台的表达芯片跟mRNA-seq数据比较
文章见：http://journals.plos.org/plosone ... ournal.pone.0078644指定的细胞系是：Human CCR6+ CD4 memory T cell ，测了6个时间点，共12个样本表达芯片用的是Affymetrix GeneChip HT HG-U13...
|
文章见：http://journals.plos.org/plosone ... ournal.pone.0078644 指定的细胞系是：Human CCR6+ CD4 memory T cell ，测了6个时间点，共12个样本 表达芯片用的是Affymetrix GeneChip HT HG-U133+ PM arrays 测序用的是： Illumina HiSeq? 2000 platform，PE，All reads were pair-end sequenced with an average insert size of 160 bp, and typical read-length of 90 bp. 芯片情况介绍：41,796 of the 54,714 probe sets were mapped to 20,741 genes, with 10,837 genes having more than one representative probe set. 比较前先把RPKM值和芯片数值归一化： In summary, RNA-Seq based transcriptome expression was measured as RPKM for 36,004 transcripts, representing 22,300 unique genes. The median RPKM in all 12 samples was 0.49, and 28.6% to 32.5% (average?=?30.3%) of genes had RPKM value of 0 in each sample. In order to make the transcriptome profiling comparable between both platforms (RNA-Seq vs. Microarray), the RPKM values were floored at 0.047, followed by log2 transformation. After the transformation, the difference between the median expression and the floored (minimal) expression by RNA-Seq is equal to the difference between the median expression and the minimal expression by microarray. 文章很有趣，值的细看 RNA-seq: An assessment of technical reproducibility and comparison with gene expression arrays Another paper with a variety of comparisons between Affymetrix Exon arrays, custom NimbleGen arrays, and RNA-seq: Griffith, et al. Alternative expression analysis by RNA sequencing. Nature Methods. 2010 Oct;7(10):843-847. 文章是：https://genomebiology.biomedcent ... 6/s13059-015-0694-1 |
原文地址：https://www.cnblogs.com/wangprince2017/p/9819293.html