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Question: Help for Affymetrix HTA 2.0 [transcript (gene) version] annotation
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gravatar for Biomed
4 weeks ago by
Biomed0
Biomed0 wrote:

Hi all,

It's my first-time to analysis Affymetrix HTA 2.0 arrays [transcript (gene) version], data source https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi. As a rookie, I have been bothered by its annotation for a long time, really hoping there would be someone teach me something. Thank you in advance!

The data probes like this: At first sight, there are 5 kinds of probes in this data?Sorry for my weak knowledge of probe sets.

AFFX-r2-P1-cre-5_at
ERCC-00171_st
ERCCmix1step1
ERCC-00002_st
JUC01000985.hg.1

Referring to some experience in Bioconductor, I used hta20transcriptcluster.db package to complete annotation, however, there are so many NA in the last result. I must have missed something. Are there someone so kind to help me understand my faults? 

Thank you very much.

 

ADD COMMENTlink modified 4 weeks ago by svlachavas610 • written 4 weeks ago by Biomed0
0
gravatar for svlachavas
4 weeks ago by
svlachavas610
Greece/Athens/National Hellenic Research Foundation
svlachavas610 wrote:

Hi mzcs.csu,

have a detailed check on the following posts, they would be very helpful:

A: Appropriate pre-processing pipeline for Human Transcriptome Array HTA 2.0 with o

A: difference between pd.hugene.2.1.st and hugene21sttranscriptcluster.db

Also, a reproducible code chunk with the exact steps you performed would be very helpful-

Best,

Efstathios

ADD COMMENTlink written 4 weeks ago by svlachavas610

Thank you! Svlachavas.

Obviously, you are experienced in HTA 2.0. I have some other questions. Can you tell me the correct answer?

Q1, because the oligo can only support the pretreatment process of the oligonucleotide microarray, can I start the analysis directly with the GEO series Metrix files? I just want to filter the DEG. Another, I found that if I read the series Metrix files getGeo(), GEOquery packages, load data without probes (both are NA), of course, fData() is nothing. When I load another HTA 2.0 data, GSE93742 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi does not display this status. What are the potential reasons? Due to the strange situation above, I loaded the expression data after processing in Excel.

Q2, does the angle of view filter do use LIMMA? I read an article before that LIMMA can only filter probe level differences.

Looking forward to your reply!

 

ADD REPLYlink modified 4 weeks ago • written 4 weeks ago by Biomed0

Please, try to answer in English with a complete reply, i did not understand anything from your above answer !!

ADD REPLYlink modified 4 weeks ago • written 4 weeks ago by svlachavas610

I'm so sorry for my careless! It's weird that I really add the reply in English! Apologize again!

Here is my formal answer:

Thank you! Svlachavas.

Obviously, you are experienced in HTA 2.0. I have some other questions. Can you tell me the correct answer?

Q1, because the oligo can only support the pretreatment process of the oligonucleotide microarray, can I start the analysis directly with the GEO series Metrix files? I just want to filter the DEG. Another, I found that if I read the series Metrix files getGeo(), GEOquery packages, load data without probes (both are NA), of course, fData() is nothing. When I load another HTA 2.0 data, GSE93742 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi does not display this status. What are the potential reasons? Due to the strange situation above, I loaded the expression data after processing in Excel.

Q2, does the angle of view filter do use LIMMA? I read an article before that LIMMA can only filter probe level differences.

Looking forward to your reply!

Thanks for your precious time again.

Best wishes to you!

 

ADD REPLYlink written 4 weeks ago by Biomed0

eSet=read.delim("GSE73219_series_matrix.txt")

昏暗(eSet)#[1] 70523 13</font></font>

rownames(eSet)=eSet$ID_REF

eSet=eSet[,-1]

ESET=new("ExpressionSet", exprs=as.matrix(eSet))

##limma DEGs filtering

cell_line=factor(rep(c("Hep3B","Huh1"),each=6))

treat=factor(rep(c("scr","RDBP"),each=3,times=2))

combine=paste(cell_line,treat,sep="_")

design=model.matrix(~0+combine)

design=design[,c(2,1,4,3)]

colnames(design)=c("Hep3B_scr","Hep3B_RDBP","Huh1_scr","Huh1_RDBP")

rownames(design)=rownames(p_data)

cont_matrix=makeContrasts(Diff1=Hep3B_RDBP-Hep3B_scr, Diff2=Huh1_RDBP-Huh1_scr, DIFF=(Hep3B_RDBP-Hep3B_scr)-(Huh1_RDBP-Huh1_scr), levels = design)

fit1=lmFit(eSet,design)

fit2=contrasts.fit(fit1,cont_matrix)

result1=decideTests(fit2)

vennDiagram(vennCounts(result1))

result2=topTableF(fit3,number = Inf,p.value = 0.05)

fit3=eBayes(fit2)

ADD REPLYlink modified 4 weeks ago • written 4 weeks ago by Biomed0
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