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Question: Help for Affymetrix HTA 2.0 [transcript (gene) version] annotation
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gravatar for Biomed
12 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 12 weeks ago by svlachavas620 • written 12 weeks ago by Biomed0
0
gravatar for svlachavas
12 weeks ago by
svlachavas620
Greece/Athens/National Hellenic Research Foundation
svlachavas620 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 12 weeks ago by svlachavas620

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 12 weeks ago • written 12 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 12 weeks ago • written 12 weeks ago by svlachavas620

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 12 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 12 weeks ago • written 12 weeks ago by Biomed0
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