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Microarray - Science topic

A place for discussions about all types of microarrays.
Questions related to Microarray
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I have binary data for gene expression and I want to analyze them by microarray. I don't know exactly how I can do it and with which platform?
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dear @Jochen , sure I'm a beginner , many thanks for comments and help
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I want to download transcriptomes from different types of immune cells T, B, Effector T cells, plasmatic cells, monocytes, macrophages, etc. It can be from microarray, RNAseq or scRNAseq from human tissues
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Has anyone had success using serum samples for either Agilent mRNA or miRNA microarrays? I'm trying to make a plan to analyse serum samples that are biobanked in my lab, but it seems that the Agilent microarrays don't have a recommendation for serum due to the low concentrations of RNA. Any tips or recommendations?
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Dayna R Schultz I think it's depending on what kind of experiment you will do. For sequencing, it's recommended to use agilent chip to characterizing RNA. I don't know for others experiments
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I am planning to use DAVID bioinformatic tools to do a functional analysis for my microarray result which is called microRNAs. one problem I face is choosing a suitable gene identifier for this set of microRNA. without knowing the specific gene identifier DAVID tool cannot do the analysis. Any suggestion from fellow researchers here?
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try miRNet.ca, David uses the gene list, so you might find target genes first.
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I want to extract up-regulated and down regulated genes in Myotonic dystrophy type 1 (DM1) from microarray dataset available at NCBI GEO database (http://www.ncbi.nlm.nih.gov/geo/). I downloaded the datasets from NCBI GEO in “GEO2R’’ format. Kindly, tell me about analysis through R or which method would be easier?
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Have a look at this. You might not need any other thing.
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Hello everyone,
I have been trying to replicate the findings of the research article titled “In-silico analysis of differentially expressed genes and their regulating microRNA involved in lymph node metastasis in invasive breast carcinoma”( PMID: 34396887)This paper included DEGs (differentially expressed genes) based on the cut-off criteria of |log 2FC| ≥0.58 and P value<0.05. According to the results, the number of genes obtained were 7935 (GSE42568, N[1]vs. control), 8298 (GSE42568, N+ vs. control), 221 (GSE42568, N[1]vs. N+), 292 (GSE76275), 333 (GSE23988), and 551 (GSE22093), respectively. I’ve attempted to trim the data obtained from GEO database using the above mentioned cut off criteria. Despite several replications of the results, I have not been able to achieve the exact number of genes.
Could anyone provide me with a possible explanation for this?
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The software version may have an effect, since a new version usually indicates certain changes and such changes may of course also effect the results you obtain from the software.
From what you write, the difference arises already in the DEGs. Which software and algorithms/procedures haven been applied in the paper for determining the DEGs and are you using the same? Even if you apply the same algorithms/procedures (but not the identical software and version), there may be differences e.g. by different default values or differences in the implementation.
Best Matthias
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Please, I have to perform microarray tests on DNA sent from USA to Europe. How should it be sent, at room temperature, at 4 degrees packs or at minus twenty degrees packs ?
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by controlling temp. I think that 2-6°C is enough for few storage
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we have a microarray assay with about 600 thousands markers. Illumina Infinium globalscreening-24v3.0 Bead Chip (48 samples) array. The results are like this "rs364728", how to transform the rs numbers of such a hughe package into concrete gene mutations ? Is there also possibility to add health conditions to concrete mutations ? Do you have any experience with this assay ? Thank you Dana Pokorna
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I agree with @Anita Tripathi
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I tried to remove human sequence from single-cell fastq.gz with bowtie2.
However, when we input R1 and R2 fastq.gz into bowtie2, human sequence was removed.
When we input R2 fastq.gz into bowtie2, human sequence was not removed ( ${base}_host_removed is zero).
Code:
for i in scrEXT003_hg19_S15_ME_L001_R1_001.fastq.gz ; do base=$(basename $i R1_001.fastq.gz) ; bowtie2 -p 2 -x /home/wanglu/CRC/GRCh38_noalt_as/GRCh38_noalt_as -1 ${base}R1_001.fastq.gz -2 ${base}R2_001.fastq.gz --un-conc-gz ${base}_host_removed > ${base}_mapandunm.sam; done
for i in scrEXT026_hg19_S23_ME_L001_R2_001.fastq.gz ; do base=$(basename $i R2_001.fastq.gz) ; bowtie2 -p 2 -x /home/wanglu/CRC/GRCh38_noalt_as/GRCh38_noalt_as -q ${base}R2_001.fastq.gz --un-conc-gz SAMPLE_host_removed > ${base}_mapandunm.sam; done
Result:
9652946 reads; of these:
9652946 (100.00%) were unpaired; of these:
2526574 (26.17%) aligned 0 times
2946266 (30.52%) aligned exactly 1 time
4180106 (43.30%) aligned >1 times
73.83% overall alignment rate
Data Source:
scrEXT003_hg19_S15_L001 ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-8410/scrEXT003_hg19_S15_L001_R1_001.fastq.gz ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-8410/scrEXT003_hg19_S15_L001_R2_001.fastq.gz scrEXT003_hg19_S15_L002 ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-8410/scrEXT003_hg19_S15_L002_R1_001.fastq.gz ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-8410/scrEXT003_hg19_S15_L002_R2_001.fastq.gz scrEXT003_hg19_S15_L003 ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-8410/scrEXT003_hg19_S15_L003_R1_001.fastq.gz ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-8410/scrEXT003_hg19_S15_L003_R2_001.fastq.gz scrEXT003_hg19_S15_L004 ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-8410/scrEXT003_hg19_S15_L004_R1_001.fastq.gz ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-8410/scrEXT003_hg19_S15_L004_R2_001.fastq.gz scrEXT003_hg19_S15_L005 ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-8410/scrEXT003_hg19_S15_L005_R1_001.fastq.gz ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-8410/scrEXT003_hg19_S15_L005_R2_001.fastq.gz scrEXT003_hg19_S15_L006 ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-8410/scrEXT003_hg19_S15_L006_R1_001.fastq.gz ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-8410/scrEXT003_hg19_S15_L006_R2_001.fastq.gz
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You haven't read the manual and using paired-end options for single end reads. --un-conc-gz is a paired-end option.
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I have used RMA algorithm for log2 transformation & calculated fold change. Now i have to select differentially expressed genes so which statistical method can give a better p-value. This method should be compatible with R programm as I am using it.
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Size effect meta-analysis of the datasets will best identify the DEGs
A user-friendly online tool, https://imageo.genyo.es/ can be use for this purpose
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I am doing experiments with a trimer S-protein of SARS-Cov-2. I use this protein to print on a microarray plate. After this we do a neutralization assay with different kinds of serum. I want to know what the pro's and con's are of using trimers. U can also use monomers and dimers. But these don't occur in nature like this. Out of practice these ARE more specific.
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Who should not get the Moderna COVID-19 vaccine?
If you have had a severe allergic reaction (anaphylaxis) or an immediate allergic reaction, even if it was not severe, to any ingredient in an mRNA COVID-19 vaccine (such as polyethylene glycol), you should not get an mRNA COVID-19 vaccine.
The most commonly reported side effects were pain at the injection site, tiredness, headache, muscle pain, chills, joint pain, and fever.
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I am unable to open the raw data files of microarray data
into R program. I contacted
I checked for a few resources online, bioconductor packages
also EBI training courses
etc but it does not open the files.
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Retrieve that list of GEO accession and sample types
for ex:
Sample sample_type
GSM123444 Control
GSM123445 treatment1
GSM123456 treatment2
GSM123457 treatment3
GSM123458 treatment4
store the above list in a text file ex path.txt
Then import into R
data=read.table("path.txt", header=T)
Best!!
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microarray data analysis - exploratory analysis
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I agree with Dr. Fred
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After normalization and summarization of microarray raw data,
when I carried exploratory grouping analysis.
The samples from the two comparison groups don't cluster together, should I exclude the samples that cluster in the wrong group?
or should I continue the analysis to determine the differentially expressed genes without excluding any array?
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Not only normalizing your data, you need to do all the necessary preparations on your data such as selection for training and validation before running the exploratory analysis. Go through your steps carefully,
You may not necessarily have a clear grouping or cluster due to overlap, in this case you should be able to explain such scenario based on your samples or experimental results.
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Hi there,
I would highly appreciate it if someone answered my question?
what is the difference between Microarray (Agilent and Affymetrix) with Chromosomal Microarray? https://www.illumina.com/areas-of-interest/genetic-disease/rare-disease-genomics/cma-constitutional-cytogenetics.html
Kind regards,
Amin
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There are two types of microarrays: Comparative Genomic Hybridization arrays and SNP arrays.
CGH-based arrays (aCGH) measure the quantity of genomic DNA in a patient's sample and compares it with the genomic DNA in a normal control sample. The assay works by enzymatically cutting the patient and control DNA samples into fragments and then labeling each one in a different fluorescent color.
Single nucleotide polymorphism (SNP) arrays use DNA probes that derive from regions in the genome that show differences between individuals at a single base pair site.
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I am working on single cell RNA-seq data for gene co-expression (GCN) network analysis, I read about the WGCNA and I found it is initially used for microarray datasets. I wonder if it is possible to use for single cell data, If it is not, what are the recommended alternatives?
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Initially, it was used with microarray data, but it is an independent method that can be applied to any type of data with a similar aim. In the case of gene expression data, WGCNA aims to create a correlation profile based on expression intensities. It has been used, however, for other high-throughput data, such as transcriptome and metabolome. The same mechanism has been used with gene counts, FPKMs, log fold changes, etc. My personal opinion is that using correlation to GCN is sometimes questionable, especially the values that have been considered when drawing up the correlation profile. However, I still believe it is one of the most effective ways of looking at system-wide insights. Another suggestion would be to use mutual information instead of Pearson's correlation. The Pearsons coefficient often leads to overestimated edges in the GCN. Thanks
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i have to select the differentially expressed miRNA in gastric cancer. so which assay i should preferably choose? which one will give much better and accurate results? or should i go to some online available data set websites like TCGA?
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I agree with Abhijeet Singh that both these platforms cant be compared as such. Khadija Raziq Khan It completely depends on your query. If you just want to do some basic study for your thesis (like exploring just expression levels of some new miRNA in gastric cancer), you can simply use TCGA or other datasets and validate in your sample. But in case you want to do some meaningful study, consider that regardless of the method one uses, all of the current methods have their own drawbacks. Moreover, in case of miRNA, you can not do an immunohistochemical validation for the cell specificity and you may end up just exploring stromal cell-associated transcripts (just one of the many potential pitfalls) instead of exploring tumor cell-associated miRNA.
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i want to find microarray data in special gene.
i want to find expression variation for special gene in any kind of normal and tumor cell line and tissue.
which databank can i use?
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What is the script to do the quantile normalization to do a microarray dataset (GSE70970), by using limma? do i need to create model matrix first before proceeding to normalize it? i'm very new to R
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Yes first you need to create a numeric matrix and store it in A.
then try normalize
normalizeQuantiles(A, ties=TRUE)
ties = T will ties every column of your matrix A and the values will be normalized to the mean of the corresponding pooled quantiles.
Have fun and Happy Research!
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Dear all,
I performed a signature analyses on Genevestigator to compare my RNA-seq data to the Arabidopsis database,
The results are great but then I'd like to take a look at all the genes from similar studies,
I went through the GEO database and that's when it gets tricky, (there are many table with replicates and sometimes the tables contain more genes then initially described in the original paper)
Can someone give a detailed guide ?
Sincerely,
Houda
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Retrieve your microarray data from GEO in .cel file format.
Load it in to R programming.
You may use R packages such as affy, limma to study the trascriptome expression and you can use annotate package to annotate the genes. Let me know if you need any further help.
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This era can rightly be called, the era of "Transcriptome". After the succesful stories of RNA-seq and Microarrays, single cell RNA is a hot line.
As I always belived, if one has funds, one can go for a reasonable research. Same is true here. Since the scRNA-seq cost is really high, and several PIs cannot afford.
The question is: If someone performs scRNA-seq, but the results are contrasting to what was expected. What are the possible ways to value the wasted cost?
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If you do an experiment just to see what was expected anyway: how would you value these costs?
Unexpected findings are always what we (should) go for in research. If the unexpected is because of a bad experimental design, bad experimental practice or a faulty data analysis, then these are things to be critisized.
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I have set out to an in silico analysis which includes different microarray and RNA-seq datasets. A well-known problem here is the batch effect. how can I deal with this? if there is any R package that can be used in such issues, I would be pleased if you introduce it to me.
with best regards.
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For RNA-Seq, batch effect can be removed using Combat-Seq: https://github.com/zhangyuqing/ComBat-seq
For Microarray data you can use the limma package:
The following function should help you : limma::removeBatchEffect()
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To design a device with a microarray for genotyping viral DNA in a blood sample.
The viral DNA sequence of the viral DNA which can be used for PCR-amplification has a length of 320 bp within 3 mutations in a distance of 50 bp. Every mutation stands for one viral genotype.
What are the physico-chemical suppositions of the device and the design of the device within the biofunctional surface and the way to diagnosis?
Thank you
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Hi Isha
I suggest you to align the variants in the sequence and select probes, containing the variant but not the neighboring ones. to be faster, since they are 50 bp away from each other, the best way would be to choose your probes across each variant. it will therefore be a present/absent test.
fred
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For the same condition, and the same gene/protein, the Western blot is showing a significant increase of expression, while the mRNA shows no difference (for other cases, it might showing opposite). I understand that it is possible since translation is a thing. However, if I'm mainly looking at the microarray data, and trying to understand the process, should I trust more on the microarray data or the western blot data? (What if it is single cell mRNA sequencing?)
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It is true that validating with qPCR is very important. If the microarray RNA was not great quality, if the probe for the gene you are examining is not great, or if the mRNA you are focused on is only expressed at very low levels, it might be hard for microarray to detect accurate mRNA levels. One thing you can look at is the signal from your gene of interest compared to other genes in the microarray. Does yours fall on the high end of the total expression spectrum, or the low end? As for which to believe, it sounds like the protein might be more clearly confirmed presently (if you think the blot is really clean and clear) - which one you need to confirm though, probably depends on the bigger picture question in your project.
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for PCR analysis and western blot, is there a minimum required amount of average expression level of DEGs? For example, more than 200 or 500. I have various answers to that question and I am a little confused.
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For PCR an expression value of > 200 is sufficient, I think. Here it is not a question of Fold Change (FC): The same FC with an expression value of 10 vs. 20 would be hardly detectable with PCR or other techniques. With expression values of 200 vs. 400, this is surely much better (identical FC of 2).
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I m a first year student so this question might seem a little dense. but currently I ran PCR analysis of some gene. I got the potential interesting genes from the microarray data from the geo data. first time i run the PCR, the Ct value was high. when i ask my supervisor, he ask me to check the raw expression level that if the raw expression value is low, it could be a possible problem. but there is only comparative expression level(log2FC). could it be that i couldn't find or is there a case that the researcher didn't upload the raw expression level on the geo data?
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Thanks for sending me the GEO accession number. You cannot find CEL or CHP files, because you have Illumina chip data here. I have downloaded the file and have sent you the zipped file some minutes ago. Unzip it and you will find the expression values (Signals). I hope you will find your gene.
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I have a microarray data and for analysing that I am using r software. I have a cancer data for each time pt.
I have 5 samples for to
5 samples for t1
4 samples for t2
3 samples for t3
All r disease model...I want to do microarray analysis to find out the differential gene for each time pt. Compare to t0. I want to take t0 as a reference for each time pt.. s
So how can I make contrast matrix for that.
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What is the software that you are planning to use for the analysis? Depends on that how you write the contrasts would vary. If you plan to use "limma" (https://www.bioconductor.org/packages/devel/bioc/vignettes/limma/inst/doc/usersguide.pdf) read the user manual and it is easy to understand. As long as you have 2 or more replicates you can do differential expression.
Regards,
Shajahan
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I am an undergraduate student trying to determine what features the Affymetrix HG-U133 Plus uses to measure the expression of specific genes for feature extraction. All I have so far is the level of mRNA in a sample compared to a reference sample. Can anyone point me to any resources that could help me determine additional features that are used to classify gene expression data and how the above gene chip is manufactured?
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Hello Christopher Laurens,
it is the question how you have analysed your chips. Using GCOS software and comparing each experiment chip with each baseline chip you could get many more info than only normalizing your chips with RMA, getting the expression values (Signals) only. In both cases it would be better to have as many chips as possible for each group: Then you could select the significant differently expressed probe sets (IDs, genes) using any statistical tests between Signals of both groups or Fold Changes between the average of the Signals for any ID. You could use cluster tools to visualize differences between the two groups of signifícant IDs and you could make a principal component analysis (PCA) of significant IDs to demonstrate if these IDs could separate your both groups more or less correctly. There are a lot more possibilities, as you could find within the publications of my profile (https://www.researchgate.net/profile/Joachim-Gruen).
The chip layout could be found on many Affymetrix websites ( I hope they are further existing, because Affymetrix is sold).
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We have a set of probes for which we need to check if the probe has non-specific targets. But we need to search each probe against a large number of genomes. A large number of hits are coming up and it is very tedious to manually check each hit. Is there a way to make this faster and accurate?
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Hi Arpit, You can install offline NCBI blast tools in your machine. Create the database of microgranisms which you want to check the specificity against your probes. Run the local blast in your machine for probes against this database. Check the output of blast results and select what you want. May be you can write simple script to find the correct hit.
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Hello. I'm a beginner in bioinformatics.
I'm handling Illumina SNP microarray data and got genotyping results of several SNPs. But in some SNP sites, for example, in the case of the SNP site of one individual is "BICF2P1141058", the genotyping result is [A/T] and the other SNP site "BICF2G630601486", the genotyping result is [T/A].
I really do not know the difference between [A/T] and [T/A]. For getting more information, I searched the Illumina SNP genotyping technical note (https://www.illumina.com/documents/products/technotes/technote_topbot.pdf) and found some information that to provide accurate SNP strand and orientation, Illumina offers strand information based on SNP genotype and nucleotide sequences surrounding the SNP.
However, I still do not clearly know why the strand information is needed. The SNP genotype result is already determined by a specific nucleotide pair. Then what kind of additional information does the strand information provide? In the case of my example [A/T] and [T/A], aren't the SNP sites just composed of A and T? Could you please tell me how considering stand information helps in the interpretation of SNP array data?
Thank you!
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The reason is that "+" strand (sense) is considered as the default DNA strand in the most cases, whereas targets for probes for SNP array can be located on any strand, according to convenience for array design. So, you need to take into account strand information in order to avoid mistakes in interpretation of data.
Also, it seems like you confuse complementary base pairs with a couple of allelic variants. In your example ([A/T] and [T/A]) both bases are allelic variants on the same strand (check if the workflow included strand conversion). Their order in the genotype record depends on what allele is considered to be the reference, and what is the alternative. This information is contained in the manifest file of corresponding microarray.
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which platform is better for performing microarray data analysis? agilent, affymetrix or illumina
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here at our university the lab uses Affymetrix and Microarray.
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Dear fellow Researchers,
I am currently trying to analyze Affymetrix microarray data through dChip software and I have the input files - probe sequence and CDF for Rat 230 2, yet facing issues in obtaining expected results. Could anyone please help me out if gene info file is much necessary (as only CDFinput is mentioned as mandatory as per the protocol I have) and where to obtain them?
Thank you in advance
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You can search for your question through the following link:
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Hello everyone,
I would like to check the expression of ALDH2 in Leukemia Stem Cell (LSC), and compare
its expression with other AML cells. Can anybody please suggest some literature that contain
Leukemia stem cell (LSC) RNA-seq dataset or microarray dataset? Thank you
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One of the best websites for your question is "Biostar", you can go there, you can find questions similar to your question, please search the link below:
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I have recently downloaded microarray data from GEO using the following in R (as mentioned in GEO2R)
library(GEOquery)
library(limma)
library(umap)
gset <- getGEO("GSE86971", GSEMatrix =TRUE, getGPL=FALSE)
if (length(gset) > 1) idx <- grep("GPL13684", attr(gset, "names")) else idx <- 1
gset <- gset[[idx]]
ex <- exprs(gset)
the resulted table looks like this
GSM2311294 GSM2311295
mMC002640 10.913068 8.913076
mMC002644 495.899231 678.998352
mMC002645 1803.785520 1582.487300
PH_mM_0000001 1.512415 1.475654
PH_mM_0000002 7.469218 4.426962
The question here is what are the type of identifiers ( for example mMC002640, PH_mM_0000001 ) used for this data? and how can convert those into gene names?
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One of the best websites for your question is "Biostar", you can go there, you can find questions similar to your question, please search the link below:
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Hello, I'm working with Arraystar lncRNA microarray for the first time. I tried to normalize the raw files (.txt) using the following script, which I used to normalize different other Agilent chip versions previously. Can anyone please tell me is this ok for Arraystar chip also? Unfortunately, I'm not being able to get the normalized values similar to that provided by the company. The company performed quantile normalization of the raw data using GeneSpring GX.
Here is my script for quantile normalizaiton:
library(limma)
library(limmaGUI)
targets <- readTargets("./targets.txt")
x <- read.maimages(targets, path="./Raw Data Files/", source="agilent",green.only=TRUE)
y <- backgroundCorrect(x, method="normexp", offset=16)
y <- normalizeBetweenArrays(y, method="quantile")
View(y$E)
y$E<-log2(y$E)
E = new("MAList", list(targets=y$targets, genes=y$genes, source=y$source, M=y$E, A=y$E))
E.avg <- avereps(E, ID=E$genes$ProbeName)
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One of the best websites for your question is "Biostar", you can go there, you can find questions similar to your question, please search the link below:
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Does anyone know of a source for microarrays to study tRNA expression? I was told microarrays.com provided these. I have asked them directly but no reply so far.
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tRNA microarrays were initially developed to assess the aminoacylation status of specific tRNAs. Although it is true that tRNA deep-seq techniques have problems (mainly due to modified bases), microarrays also present serious limitations in terms of detection sensitivity. My advice would be to use one of the several strategies for tRNA library preparation and go for small RNA sequencing.
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I´m trying to analyze the data from Illumina microarray using the Giovanni Coppola guide. I've also used some examples from the lumi package (bioconductor) and always use the targets file. The problem is, when I refer to the GEO page whoch contains the data I want to analyze, I don´t find any targets file available for download. Could you please tell me if that file is always needed? Thanks in advance.
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Hi, the PDF file says the "targets<-read.delim(file="targets.txt", header=T) This command uploads the sample list and stores it in the object named targets (Table 1)." I looked Table 1 and just copied that to Notepad and save it as "targets_copy.txt", of course this is unformatted but I transformed it to a table with the followings commands:
>targets <- read.delim(file = "targets_copy.txt",
header = TRUE)
>targets_1 <- c()
>for (i in targets){
> targets_1 <- strsplit(i, " ")
>}
>x <- as.data.frame(targets_1)
>targets_1_transpose <- transpose(x)
>targets <- subset(targets_1_transpose, select = -V1)
>colnames(targets) <- c("Array", "Batch", "Genotype", "Replicate", "Drug", "ArrayCode")
Then I created the new "targets.txt", read.delim() didn't work for me, but read.table() works fine and you can finish the tutorial with it.
>targets <- read.table("targets.text", header = TRUE)
>str(targets)
'data.frame': 24 obs. of 6 variables:
$ Array : int 1 2 3 4 5 6 7 8 9 10 ...
$ Batch : int 1 1 1 1 1 1 1 1 2 2 ...
$ Genotype : chr "wt" "wt" "wt" "wt" ...
$ Replicate: int 1 2 3 4 1 2 3 4 5 1 ...
$ Drug : chr "ctrl" "ctrl" "ctrl" "ctrl" ...
$ ArrayCode: chr "1412066061_A" "1412066061_B" "1412066061_C" "1412066061_D" ...
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I'm currently dealing with GPL16956 microarray platform ( Arraystar human lncRNA microarray V3 Agilent-045997 (Probe Name Version). How to convert the probe ids to lncRNAs/gene or find the annotation file?
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Since it is a custom designed microarray for the customer, you should either contact the author or try to find the gene/lncRNA/transcripts from the sequences using BLAST/BLAT.
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I have an old slide microarray scanner (VIDAR Revolution 4200) and would like to give it a "second life", as IHC scanner array (fluorescence dyes), FISH over tissue or similar.
Has anyone tried this alternate use for a microarray scanner?
Thanks in advance
Elena
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Hi Elena,
This is a software opportunity, if your system will save an image file (.jpeg, .gif, .gel) it should work.
When @ Molecular Dynamics we scanned many IHC slides, even published once in January 1999, Nature Biotechnology 17(1):53-7. It appears your scanner will allow adjustment of gain (PMT voltage) so have at it.
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I have a list of DEGs from microarray. I want to find gene networks that are included in the, for example, 100 top differentially expressed genes. What is the best approach to do so and draw the gene network interactions? Can you provide some websites for this purpose?
Thanks
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Hello
Try these 2 websites:
I recommend to install Cytoscape software which is great in drawing gene networks: https://cytoscape.org/
Regards
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Hi,
I have raw data from [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array that I want to process using the Expresso function for Affymetrix microarrays.
My samples include tumor tissues and matched adjacent tissues.
I am planning to use the RMA method which includes RMA+Quantiles+pmonly+median polishment, but it would be great if you share your experience with me. Which methods would you prefer to combine according to your statistical experience in this field?
Background correction Options:
  • Affymetrix MicroArray Suite (MAS)
  • Robust Multiarray Analysis (RMA)
  • None
Normalization Options:
  • Quantiles
  • Lowless
  • Cubic Spline (Qspline)
  • Invariant set
Probe match correction Options:
  • Perfect-Match (PM) only ["pmonly"]
  • Subtract with Mismatch (MM) ["subtractmm"]
  • Affymetrix MicroArray Suite (MAS) ["mas"]
Values presentation Options:
  • Average Difference ["avgdiff"]
  • Li & Wong (2001) outlier removal ["liwong"]
  • John Tukey median polishment ["medianpolish"]
Thank you in advance,
Sevcan
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I appreciate Sevcan Atay for the valuable topic. Would be interested to know as well.
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Which data is more useful to start gene mining and BLAST in plants?
mRNA or RNA-seq or Microarray
Which databases are available for finding RNA-seq in Rosaceae family?
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Use the GDR database according to the following articles.
If my answer was helpful, please recommend it.
Best wishes
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Hi all,
Currently, I'm working on a project to analyze gene expression from RNA which is extractred two different samples by microarray. The results showed that almost all gene expression was uniformly up-regulated in one of the samples.
The reason for this result could be that the amount of RNA provided in one sample was simply too high, but apparently this is not the reason.
Please let me know if there are any conditions under which the gene expression of one sample is uniformly higher than that of the other.
Best regards,
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Thank you for your attention and great opinion.
As you said, I tried to correct these data with percentile nomalization before. However, results for RT-qPCR did not support data from microarray after nomalization. If anything, they supported data befor nomalization. Is the data before nomalization absolutely wrong? Or, was the RT-qPCR data not correct? I am seeking the information which supports data I got at first, but I cannnot find such one.
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What i know, microarray data has vsn, loess and quantile normalization . i am dealing with GEO microarray datasets where i have to find the differential expresssed genes for NPC. im planning on using the GEO2r since i have zero knowledge on R language. The question is, are all the normalization types the same, as different datasets have different norml types or they are different so i have to normalized all the datasets I’m using from the GEO before using GEO2R
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The normalization is not same in microarrays. The different normalization methods are available for microarrays (for example RMA, GCRMA, MAS5, quantile). You can find a lot of resources from the web for microarray data analysis. The R packages (for example limma, oligo) are used for normalization and analysis.
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I want to analyze the PRS scores for my microarray datasets, but want to use a platform which is a bit user friendly. Please suggest for any pipelines available.
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Dear Lasse Folkersen , Peymaneh Davoodi ,
Himanshu Joshi
Thankyou for the very useful information and resources.
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I want to label my target miRNA and see the miRNA trafficking. But I don't want to use any plasmid or radioactive label.
I need the fluorescent label that will not affect the length of my miRNA.
I found some kit such as ULYSIS Nucleic acid labelling kit, Label IT, miRVANA, and N Code.
But in many paper, they usually used for microarray. Can they be used in my project?
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Paul Rutland okay Sir. I will discuss it with my Professor. Thankyou very much once again for your helpful suggestion 😊
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Dear colleagues,
We want to profile native human IgA antibodies with peptide microarrays (Immunocytochemistry - like application), but I can't find something that works for me for quite some time already.
Do you know some reliable ones?
Thank you
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Good day. Try to use Dako IgA antibody. It is anti-human. We have used it for detection IgA+ plasmocytes in colon cancer tissues.
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I need to determine the difference in these two probes, which give different results.
RNF121 (UKv4_A_23_P75973)
RNF121 (UKv4_A_24_P235870)
Thanks for any help.
Platform: ag44kcwolf
Species: hs
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Hi
See the attached excel file containing details of the two probes including sequences. This may help in further analysis
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I want to get clear comparison of microarray and deep sequencing technique for transcriptome analysis of cancer gene !
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Unlike arrays, deep-Seq technology does not require species- or transcript-specific probes. It can detect novel transcripts, gene fusions, single nucleotide variants, indels (small insertions and deletions), and other previously unknown changes that arrays cannot detect...
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Hi,
I'm conducting a bacterial microarray expression data analysis on RStudio and have got 3289 and 59 differentially expressed gene ID's while using an FDR value of 0.05 and 0.01respectively. But, I have found only one wet-lab experiment (based on microarray analysis and PCR result) to validate my result. Moreover, When I used the FDR value of 0.01, I got almost the same result as the previous wet-lab experiment. But, as far as I know, most of the researchers prefer the FDR value of 0.05 to screen out differentially expressed genes.
Does anyone have a clear explanation about it?
Thanks
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Hi, I think FDR<0.05 is enough. There maybe some false positives, but it's better than missing them.
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Hello, I would really apreciate if you recommend some free and friendly bionformatic program to determine if a group of genes obtained by microarray are cancer asociated genes? Greetings and thanks in advance.
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Hi Carlos
you can also use GSEA or DAVID to get pathways related to your list of genes. Typically you could use ingenuity pathway analysis but is a license access.
fred
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I am trying to install Bioconductor packages to open CEL files and analyze the raw data files generated by Affymetrix microarrays. I found some workflows on the Bioconductor website but I could not install the packages, maybe due to the different Bioconductor versions. I would greatly appreciate if anyone can give some suggestions about the workflows that I should use and/or how to download the old Bioconductor versions.
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Thank you for your advice!
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Hi,
I am wondering what is the best experiment (cheap and fast) to screen co-expression of a list of genes in different bacteria strains?
Lets say, we have around 100 bacterial strains and for each bacteria we want to study around 20-50 genes to see if they are expressed or not.
PS: the genes are different in different bacterial strain
Thank you,
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You'll want to sequence all the mRNA in all the bacteria. Sounds like a multiplexed trancriptomics project. Old school option would be Chip-Seq.
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Can you recommend a good review on methods for transcriptome analysis? In our lab we expand human T cells and magnetically seperate cells being positive for our target protein. Now, we want to compare transcription status between these cells and control sets. For this, i'm looking for a good review comparing different transcriptomic techniques (eg. singe cell RNA seq., microarrays, ht-RNA seq etc.) with a special focus on costs, time requirement, advantages and limits. Many thanks and kind regars, Marc
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Hi colleague
Find the following URL, may help you:
Regards..
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On a given microarray design there are multiple different probes spotted for many genes. The (normalized) signals of the features (all referring to the same gene) often are quite different (log2 values can vary between 2 and 16, so essentially from "almost undetectable" to "completely saturated").
If a gene set analysis or an over-representation analysis is performed, there should be one value per gene.
How to select which signal to use for the gene? I don't feel good to take the average of all the multiple features, because they are often so different. Taking the highest signal only also seems to be wrong.
Any ideas?
The attached file shows a table with example data (from an Agilent Microarray) with 5 different probes addressing the gene "PRDM". The last 4 colums show the log signal intensities for 4 different samples. The values range from 3 to 10, so there is a more than 100-fold difference in the signal intensities between the probes.
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https://www.networkanalyst.ca/. NetworkAnalyst software may help you.
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hello .. 
I'm trying to analysis two different microarray datasets from different chips using web-based tool. 
i don't know how to do that .. should i use one off them only ? 
or should i combine them using some kind of algorithm ? 
thank you 
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You may use NetworkAnalyst software.
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I'm trying to analyze the microarray dataset GSE38063 on GEO2R to determine which genes are deferentially expressed when comparing muscle of humans on caloric restriction (CR) or Western Diet (WD). However excel output from GEO2R presents 16163 out of the 21179 genes as deferentially expressed when utilizing adjusted p-value < 0.05.
I'm guessing this is because the microarray results are not normalized as shown on the boxplot attached, obtained from GEO2R. WD samples presents higher mean values compared to CR samples.
Is the lack of normalization the cause of this weird result? How could I normalize the data and analyze in a way that does not require a lot of programming expertise?
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@ Thiago Leite Knittel
Dear Thiago,
That is a nice question. Are you still dealing with your mentioned problem or is it solved now?
Sincerely yours,
Amir
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I found that there is no GEO2R option for every expression data on GEO database of NCBI to find the differentially expressed genes in normal and disease tissues. Do anybody know the reason
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I have the same question. Could anyone recommend a web-based tool? R2 recommended by Dr. Fultang does not work with my dataset.
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I'm trying to normalize my Affymetrix microarray data in R using affy package. But, i get a warning Error: cannot allocate vector of size 1.2 Gb. Is there some know how to solve it? Can you tell me the solution please.
Thank you
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Dear All,
This has worked for me with my 16RAM, 64Bit Window 10
memory.size() ### Checking your memory size
memory.limit() ## Checking the set limit
memory.limit(size=56000) ### expanding your memory _ here it goes beyond to your actually memory. This 56000 is proposed for 64Bit.
Thanks
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As a technician I sometime want to compare different lab products. There are a lot of website and I am wondering which websites you use?
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As our respected colleagues stated their expert view on this topic, I think there are some unique websites that particularly developed to compare laboratory chemical products or pieces of equipment. I personally use the following links to do this whenever we're gonna purchase something for our lab.
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I am analyzing a publicly available microarray dataset using GEO2R. I am defining my groups as GROUP-A and GROUP-B. After the analysis is done, I am getting a list of differentially expressed genes based on p value (low to high). How will I understand which genes are up-regulated in GROUP-A? Do the positive logFC value in the GEO2R result indicate that the gene is positively enriched in GROUP-A?
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Hi Sumit,
Usually IlogFCI grater than 1 and P lesser than 0.05 is supposed that micRNAs are differential y expressed.
good luck
Hossein
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I have recently carried out a microarray experiment with human cell lines (Affymetrix, S Clariom chips). I now have the top gene lists and want to carry pathway and biological process analyses. I have asked experts on the field about which genes I should include for these analyses but I get contradictory answers. My options are as follows:
1) Include the top 45 genes which differ by Fold change > 1.5 and p<0.05. When I input this gene list however, the pathway analysis does not have enough genes for each pathway, so I get maximum 3 genes involved in each process. Because of this all the enriched categories might be false positives (FDR>0.05).
2) Include 4,000 genes which have a p<0.05 but not filtering for Fold change. When I input this gene list I get more enriched categories and they are all true positives (FDR<0.05). However, one expert told me that this might not be a good idea as I am not filtering for genes that display a large difference in expression and I might get randomly enriched categories.
What is your opinion over this problem? I could only think of working out the data with both options, start with option 2 and then check which categories are same with option 1 and report these.
Many thanks in advance!
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Hi Dimitra,
This is my opinion:
If you have done your statistical analysis right (you have corrected for multiple hypothesis tests), then it means that most genes in your list are significantly enriched. The expression of some genes simply does not change so dramatically.
Therefore, I would include all significant genes. Of course, there will be some false positive genes in your list but the process of pathway enrichment is also statistically affirmed. Unless your random false positive hits don't randomly occur in the same pathway your results should be reliable. You can always use a more stringent FDR.
Ps. remember that you should do your pathway analysis against a "background" list of genes. The background should be the list of all the genes that you measured - the genes that had the chance of coming out as significantly enriched. David functional enrichment (online tool) allows you to upload a list of significantly enriched genes as well as a background list.
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Need to get a supplier for my research equipment
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Your request may be found in Agro-biotechnology Institute Malaysia (ABI) and you can get training here
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Hello everybody, I am new with the meta-analysis in Genome Wide Data so I have this doubt. I have read METAL documentation, which is by far the most used meta-analysis software in both EWAS and GWAS microarray data, but I cannot figure out how would be the input for EWAS analysis. As METAL was originally designed for GWAS, one of the inputs is to provide both the reference and no reference allele. Therefore as EWAS arrays do not rely in allele frequencies but in a quantitative measure, I would like to know how would be the input in METAL regarding this case. Thank you so much in advance for answering this issue (which may be easy, but I certainly do not know)
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Finally I got the answer and is... just do not provide the allele frequencies , the analysis will run fine, reassuring this, here is a Github manual of how to perform an EWAS meta-analysis (https://github.com/ammegandchips/meta_EWAS/blob/master/metal.md) . As you can see the parameters regarding the frequencies and genomic control are off.
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Dear colleagues
i have an unusual pb : when performing my microarray experiment, after loading my samples in the gasket, then i put my slide on the top, and close the hybridization chamber THEN : when i rotate the chamber i must see the sample moving in the chamber... but since some days, the droplet doesn't move, so i have a very bad repartition of the sample, and so a very bad hybridization... I don't understand what happens. I tried different thing, but without any result... help me please ! :-))
cheers
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Hello my friend! Might be you closed the screws too tight? Also, if gasket slides are old, the elasticity of rhe rubber might change somehow.
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I an currently working on a project that will involve the Horvath epigenetic clock as a quantitative measure of ageing. The clock is based on assessing the methylation of 353 CpG sites. I'm new to this area, but it appears that the standard approach is to use the methylation array chips from illumina. I'v found that the current Illumina chip (850k) is fairly expensive and lacks coverage of 17 CpG sites required for the clock.
Does anyone know of an alternative for assessing these specific 353 CpG sites?
Or
A method to help narrow down which sample are worth analyzing with the chip?
Thanks
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Hi Richard
You may consider sequencing based solutions. Epityper or even targeted bisulfite seq. Im not sure though whether they would be less expensive, depending on the number of samples you plan to profile. You could also imagine to design a custom agilent array, but you would need to work on the chemistry of the assay.
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I am working in R and have micorarray expression matrices from Agilent in log10 format. These matrices have the following colums:
Structure: BrightCorner, E1A, Structural, and blank
Probe: (-)3xSLv1, (+)E1A_..., A_..., DarkCorner, DCP_..., ERCC-..., ETG..., and GE_BrightCorner.
And then Gene Symbol.
Plotting the expression of the first 20 subjects (attached boxplots.pnf file) shows that the data is probably not normalized.
Since I am used to working with data that has already been properly processed, I am not sure of how should I process this information to obtain a proper expression matrix. A very naive approach would be to simply ignore any row that wasn't mapped to a gene symbol, collapse expression by gene symbol, anti-log the values, normalize between arrays and calculate log2. But I don't know what kind of information is stored in all the other rows and how I should use it.
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Hi Dario,
What is the name of the genome you are working with? Usually Agilent has explanation of what the rows and the columns in an array represent. I suggest you refer to the Feature extraction (the software used to process agilent data) manual or contact the support contact of agilent or the distributor from where you got your data. Based on your explanation, it seems the columns
Structure: BrightCorner, E1A, Structural, and blank
Probe: (-)3xSLv1, (+)E1A_..., A_..., DarkCorner, DCP_..., ERCC-..., ETG..., and GE_BrightCorner are control columns. Please refer to the following document for more details:
Good luck with your analysis.
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Hello colleagues, I was wondering if I could ask for general advice or guidance on something?
I tried to submit a paper recently, but it got rejected on this occasion. Essentially, we did a microarray analysis on bone tissues from control and diseased animals, and followed up with some qPCR. The reason we did the microarray in the first place was because we had virtually no idea what genes/pathways/biomarkers were affected in our animal model, so the microarray primarily served as a screening/discovery tool. However, the paper was written with a particular hypothesis and focused on a small number of genes that qPCR showed to be affected.
Based on the feedback from the reviewers, it sounds like they weren't convinced by the hypothesis, and this is one of the main reasons why it was rejected. One reviewer even suggested that the paper could be presented as a screening study that indicates interesting data.
The reason I am here today is because I wanted to ask the community if there are any journals out there that accept screening/discovery papers? That is, papers that maybe don't have a particular hypothesis, but rather show the results of an untargeted analysis and discuss indications that the data offers? I know that untargeted metabolomics papers can be published without targeted LC/MS metabolite analysis, so that they rather show the different pathways and classes of metabolites affected rather than focussing on specific metabolites. I was wondering if this is also possible for microarray experiments, and which journals would be appropriate for this purpose?
Any help that can be given will be massively appreciated, and many thanks in advance to everyone.
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Hi Adam,
I see a good fit with journals from Frontiers, and the MDPI as two pblishers who have 1000s of journals and eventually everything boils down to $$$ and eventual acceptance, no matter how the peer-review goes and not in a negative way, but weaker studies (low power, no hypothesis etc) or studies with weaker hypothesis etc get through their journals and 100s of their so called "Special Issues".
Also, leaving them on a preprint server such as OSF Preprints or bioRxiv can help you get some feedback fro the research community esp. if you are willing to implement them and make a robust paper.
PeerJ and PLoS One, and Scientific Reports are other journals who do only care for correctness of a study and do not look for novelty etc.
Hope it helps.
Thanks again,
Biswa
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I have microarray gene expression datasets, I want to apply anova tests for this large datasets but I didn't find any relevant tool to apply anova for the large datasets together yet, I have used SPSS which is very heavy software and it takes max. 200 variables at a time and takes much more time, RAPIDMINER is having attribute problems, every variable I have to give its attribute name which is very time consuming for the large datasets, I tried EXCEL also but at a time excel also takes less variables.
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You can try R-software packages!. you can use the function SqueezeVar of the library Limma to smooth the specific variances by computing empirical Bayes posterior means. and the library kerfdr has been used to calculate the adjusted p-values.
You can find more details on our recent Microarray Manuscript
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Hello everyone, I am going to prove microarray predicted genes in response to beta-interferon therapy in Multiple sclerosis (with real time PCR) .What features do you consider for selecting a microarray article?
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The features of a very good Microarray article are:
Biological and technical samples
Sample size
Validation of key genes in an independiente experiment
Functional analysis
Upload of cel files in públic databases
For mor info you can look at this link
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Hello hive mind
Does anybody know of any companies still making microarray hybridisation and wash stations ? there seems to be several manyfacturers of spotters and scanners, hybridisation and washing not so much. thanks in advance
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thanks for the response, sadly the washing is probably more important for our application than the hybridisation . the quest continues
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Generally in microarray differential expression analysis studies the lower bound for |logc| is chosen around 1 to make fold change 2 which sounds like a common sense. In other cases, when |logfc| >= 1 gives zero differentally expressed genes, logfc is chosen to get a "reasonable" amount of differentially expressed genes. It stands to reason, that a more rational way of choosing logfc would be to infer it from the microarray platform's accuracy or the quality of the hybridizations in the particular microarray-experiment or some other evidence-based criteria.
How to decide which logfc to choose?
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The fold change or log-fold change can be used as a measure of effect size in
high throughput experiments including gene expression analysis. However, the statistical significance obtaine from repetitions is another important number. One can used multiple testing adjusted p-values or false discovery rate values and ofte it is then advisable by plotting everything as a volcano plot, where log-fold change of each gene is on the x-axis and the log10 p-value (adjusted) is on the y-axis. I recommend the limma package in R to do such an analysis.
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Dear Friends,
I need guidance in initiating work on circulating miRNA - whole blood in PE patients.
What are the requirements and how to collect the blood
Required Protocols and list of experimentation to be conducted (i.e. Microarray, qPCR, RT-PCR, etc)
I am going through the existing literature but unable to design my work. Hope you can guide in this regard
Thanks
Rajkiran
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Dear Dr. Ibrahim,
Thank you for sharing the literature.
Thanks
Rajkiran
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While annotating a agilent microarray, I found that for few Probe IDs the there is no gene symbol, when checked for them manually in Ensembl or NCBI gene database I found that those transcripts are retired ? What does it mean ? Is the transcript is worngly annotated before or is the transcript is not a valid one ?
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Quote from Ensembl: "Changes within the genome sequence assembly or an updated genome annotation may dramatically change a gene model. In these cases, the old set of stable IDs is retired and a new one assigned."
You will find more information by using the Help function:
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I'd like to purchase some custom DNA microarrays, circa 100K spot density and 30mers in length. Would anyone be able to recommend a company?
thanks
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You can visit this website http://www.sciomics.de/services/microarray-printing. Hope this helps
Regards
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Why is qPCR required to validate RNAseq result? Since RNAseq results seems to be more accurate than microarray, and RNAseq should alone should be able to produce a relatively solid result, why is qPCR still needed for validation? Thanks in advance for any help provided!
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I disagree with the need to validate RNAseq with RT-qPCR. We have done a lot of transcriptome analysis and accompanied that with q-PCR validation and what we see empirically is that q-PCR has a higher variance and is harder to reproduce from expt to expt than RNAseq results. RNAseq artifacts can be identified (ie library artifacts) and compensated for and this is now done routinely. With a trend to use more biological replicates (ie 4-5 samples per data point) RNAseq is generally more accurate than q-PCR. I acknowledge that if you have a lot of samples and a small number of candidate genes that qPCR is going to be a more economical way to go. It just may not be more accurate.