cuffdiff 和 edgeR 对差异表达基因的描述

ASE又走到了关键的一步  要生成能决定是否有差异表达的table.

准备借鉴一下cuffdiff和edgeR 的结果

cuffdiff对差异表达基因的描述：

一共十四列：

第一列， test_id

a unique identifer describing the transcript, gene, primary transcript, or CDS being tested.

eg XLOC_000003

第二列，gene_id

eg XLOC_000003

第三列， gene

第四列， locus

genomic coordinates for easy browsing to the genes or transcripts being tested.

eg contig_23646:3511-3922

第五列， sample1

label (or number if no labels provided) of the first sample being tested

eg Sample_E

第六列， sample2

label (or number if no labels provided) of the second sample being tested

eg Sample_FHM

第七列， status

can be one of OK(test successful), NOTEST(not enough alignments for testing), LOWDATA（too many fragments in locus）, or FAIL, when an ill-conditioned covariance matrix or other numerical exception prevents testing

eg OK

第八列 value_1

FPKM of the gene in sample 1

eg 339.567

第九列 value_2

FPKM of the gene in sample 2

eg 465.939

第十列 log2(fold change)

the (base 2 ) log of the fold change 1/2

eg 0.456447

第十一列 test stat

the value of the test statistic used to compute significance of the observed change in FPKM

不懂什么意思 估计要去翻统计书的节奏了

eg 0.361712

第十二列 p_value

the uncorrected p-value of the test statistic

eg 0.4849

第十三列 q_value

the FDR-adjusted p-value of the test statistic

eg 0.756741

第十四列 significant

can be either 'yes' or 'no' , depending on whether p is greater than the FDR after Benjamini-Hochberg correction for multiple-testing

eg no

The FPKM value represents the concentration of a transcript in your samples, normalized for observed read counts and gene length. Thus fields 7,8 represent measurements for your samples and field 9 is simply a ratio of the two. You might look up FPKM or RPKM values if you're unsure what they represent. Fields 11 and 12 are p-value and q-value. These are values associated with the measured variation or uncertainty when you make repeated measurements of something. You should look up what a p-value and an "adjusted p-value" are (the adjusted one is important for you to understand if you're going to do any genomic data analysis). The 13th field is simply a flag based on whether the value in field 11 or 12 is less than 0.05 (I forget which one, but you could figure it out by exploring your data).

edge R 结果对差异表达基因的描述：

Differential expression analysis of RNA-seq and digital gene expression profiles with biological replication.  Uses empirical Bayes estimation and exact tests based on the negative binomial distribution.  Also useful for differential signal analysis with other types of genome-scale count data.（貌似两者采用的分布模型是不一样的哦~~）

by freemao

FAFU

free_mao@qq.com