M********r
发帖数: 142 | 1 文章说，做了Microarray，通过GO分析，发现70个基因跟binding 有关 50个catalytic
activity 40个enzyme regulator activity 等等。
是不是其实并没有发现特别有意思的基因，往往就弄一幅图，放上这个糊弄了事。
对吧？
还有那个GSEA分析，能得到这样的图，是什么意思啊？ |
K**4
发帖数: 1015 | 2 Gene Ontology term的assignment很多是错误的
还有大多数的sequences 是unassigned，所以分析起来要很小心。
很多时候出来的结果意义性很小。比如你提到的这个。
catalytic
【在 M********r 的大作中提到】
: 文章说，做了Microarray，通过GO分析，发现70个基因跟binding 有关 50个catalytic
: activity 40个enzyme regulator activity 等等。
: 是不是其实并没有发现特别有意思的基因，往往就弄一幅图，放上这个糊弄了事。
: 对吧？
: 还有那个GSEA分析，能得到这样的图，是什么意思啊？
|
w*******d
发帖数: 396 | 3 GSEA的那个图是说，他们array的结果和别人做的p53 down的结果相似。
catalytic
【在 M********r 的大作中提到】
: 文章说，做了Microarray，通过GO分析，发现70个基因跟binding 有关 50个catalytic
: activity 40个enzyme regulator activity 等等。
: 是不是其实并没有发现特别有意思的基因，往往就弄一幅图，放上这个糊弄了事。
: 对吧？
: 还有那个GSEA分析，能得到这样的图，是什么意思啊？
|
l**********1
发帖数: 5204 | 4 So,
better is to perform multiple GSA running,
pls refer attached figure
cited from
PMID 23444143
Väremo L et al., (2013).
Enriching the gene set analysis of genome-wide data by incorporating
directionality of gene expression and combining statistical hypotheses and
methods.
Nucleic Acids Res. 41: 4378-91.
>http://www.ncbi.nlm.nih.gov/pubmed/23444143
【在 K**4 的大作中提到】
: Gene Ontology term的assignment很多是错误的
: 还有大多数的sequences 是unassigned，所以分析起来要很小心。
: 很多时候出来的结果意义性很小。比如你提到的这个。
:
: catalytic
|
m**********2
发帖数: 6568 | 5 I don't like GO. mapman ontology is better.
However, bottom lines those are sort of 糊弄人. In the best cases it gives
you clue about what to focus next. To make any conclusion based those
analysis is shaky.
however again, it is better than nothing. I put those in all my internal
reports (not allowed to pulish any more) |
M********r
发帖数: 142 | 6 how about putting it in supplementary data?
GSEA+GO
However I saw even nature paper, has this GSEA data/figure
see http://www.nature.com/ni/journal/v11/n3/fig_tab/ni.1839_F4.html
nature doesn't have high quality requirement?
【在 m**********2 的大作中提到】
: I don't like GO. mapman ontology is better.
: However, bottom lines those are sort of 糊弄人. In the best cases it gives
: you clue about what to focus next. To make any conclusion based those
: analysis is shaky.
: however again, it is better than nothing. I put those in all my internal
: reports (not allowed to pulish any more)
|
y***i
发帖数: 11639 | 7 你觉得GSEA和go或者IPA比怎么样？
我觉得没有独立于基因表达的数据，用fold change/pValue去推peak，机理上很没道
理。没理由认为它比Go或者IPA强。
【在 m**********2 的大作中提到】
: I don't like GO. mapman ontology is better.
: However, bottom lines those are sort of 糊弄人. In the best cases it gives
: you clue about what to focus next. To make any conclusion based those
: analysis is shaky.
: however again, it is better than nothing. I put those in all my internal
: reports (not allowed to pulish any more)
|
s******a
发帖数: 252 | 8 I have written reviews on pathway analysis. Pathway, GO and gene sets
analyses are
just bunch of statistical methods. Like any statistical method, it depends
on how you use it and interpret it. Scientific publications often demand
evidence beyond a single statistical method.
One should consider both the quality of gene sets and the underlying
statistical method. GO is often less specific than pathway analysis. MsigDB
(database behind GSEA) is getting too big - one should be careful too. The
type of statistical methods in GSEA does offer advantage for weaker signals. |
