Questions tagged [microarray]
DNA microarrays are used to measure the expression levels of large numbers of genes simultaneously.
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Why does SVM always find separating hyperplane in p>>N and avoid overfitting?
I have gene expression data with ~20,000 features and nearly 600 samples, with 5 classes of cancer. I used grid search with 5 fold CV to find the optimal kernel and regularization for SVM, and I found ...
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Scaling different datasets for comparison - Increasing signal 'gain' / upscaling
I have 4 protein microarray datasets that I am trying to compare. I have concocted a method of aligning them all and comparing them all on a continuous scale which seems to have worked well. These ...
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What statistical comparison would I use with data that is sparse, but normally distributed at a low frequency?
Please forgive me if this is a stupid question, I am a novice with larger data sets and statistics in general. I have a matrix of data that is 50 x 429 that reflects "signal" obtained from a peptide ...
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Maximal Information Coefficient (MIC) in practical bioinformatics applications
I am interested in Maximal Information Coefficient (MIC) as an alternative to Pearson correlation when looking at gene co-expression from microarray data. I've read some very good posts on this ...
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What statistics can be used for correlating gene expression on a specific organ with its function
My hypothesis is that a gene whose function is related to an organ will have higher gene expression in that organ compared to other organs.
An example of my hypothesis:
From a published literature, ...
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Correlation analysis on two different groups of continuous heterogeneous variables with different range/scales in R
I would like to perform in R a initial simple correlation analysis, between a gene signature that i have identified, and some continuous clinical parameters, measured on the same patients, to identify ...
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Methods for tri-clustering a 3 dimensional array
I have a 5000 X 32 X 10 3D array of gene expression data that I would like to apply clustering and dimensionality reduction on.
The dimensions represent the following:
I have 5000 genes, measured ...
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Can I use the eigenvectors of a principal component for gene discovery?
I am working on a microarray dataset with n samples x m genes, with metadata including a grouping of interest and other summary statistics, e.g. age, bmi. My objective is to find genes that may be ...
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Microarray t-test in for control and treated group
I am confused with applying student's t-test with my biological experiment (even after checking you-tube videos and blogs). I have microarray data from control and hormone-treated plants with 2 ...
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How to handle identical ranks when performing quantile normalization?
I have been looking at Wikipedia Wikipedia to understand quantile normalization algorithms and I have noticed, on their example, a difference between their results and the ones given by bioconductor's ...
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Measurement for cluster internal quality
I am evaluating a few non-supervised clustering algorithms. One of the questions that I was asked was the internal quality for each algorithms. Any suggestions?
Secondly, what would be an appropriate ...
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Simulating time series microarray data
So, first I have to start with simulating time series microarray data in R
fixed effect-4 time point,
random effect- array,
pid(single observation)
I have to add Gaussian noise and Poisson noise.
...
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Does normalization reduce (or remove) variance or bias?
I'm currently analyzing microarray data.
Background on microarray normalization(not necessary to understand the question)
• Based on a global adjustment
$\log_2 {\frac{R}{G}} \rightarrow \...
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Meta-analysis of hazard ratio of microarray gene expression
Is it possible to a meta-analysis of hazard ratios, where the hazard ratios are from a continuous covariate with different ranges in the different studies? The covariate is a log base 2 transformed ...
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How can I use synthetic data to validate my classification model?
Through R and based on a microarray gene expression dataset (60 samples in total-30 cancer and 30 control samples) and R package caret, i have performed a feature selection regarding a binary ...
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Smooth representation of my data
I was directed here from the R community so I guess it's needless to say that I'm working with R :)
So I'm working with array data, resulting in ~950k x-y value pairs per array which I would like to ...
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Design/Contrast Matrices and Unestimable Coefficients
I am trying to analyse microarray data from samples with the following characteristics: one of two genotypes, a procedure either carried out or not carried out, and, in the case that the procedure is ...
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Collapse FDR value of probes detecting the same gene
I'm very new in Microarray analysis, so I hope my question wouldn't be too irritating to you all.
I have pre-processed and filtered my microarray datasets and perform differential expression analysis. ...
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What is the impact of model accuracy on the reliability of random forest variable importance?
Just for some context, I'm asking this question from a biostastics perspective. Say you have gene expression data from multiple samples that fall under class A or class B. You then choose to build a ...
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Test for significant excess of significant p-values across multiple comparisons
I have what feels like a simple question, but was unable to find answers easily.
The situation
Let's say I have a gene microarray dataset with tens of thousands of genes and small (<100) number ...
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Selecting genes that contribute the most to the principal components [duplicate]
I have done a PCA analysis on genes expressed in cells under different stimulations, and retrieved the eigenvectors for a number of components.
My question is can I use these to determine which ...
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How is it computationally possible to calculate ANOVA of microarrays with interactions of 4000 genes and 7 times
M. Kerr and G. Churchill use ANOVA to separate changes in gene expressions from effects due to the used arrays or dye using the interaction terms time*gene
As they are dealing with yeast which has ...
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Comparing datasets from 2 biological replicates
I have two datasets containing experiment data based on two biological replicates. I wonder what the best statistical methods are to find out and test how similar these two datasets are, and also how ...
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Benjamini-Hochberg and GEO2R
I'm currently using GEO2R to analyze a microarray data set. There is an option to generate the 250 most statistically significant genes, which calculates an adjusted p-value using the Benjamini-...
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Calculating variance for microarray data?
I have microarray data in which there are three biological replicates for each of the conditions. I am interested in a numerical estimate of how well the replicates correspond with one another, so I ...
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Selecting gene list for subsequent analysis in problematic microarray experiement
I have an experimental design problem and I'm not sure which would the best way to proceed.
We have a micro-array experiment in which we compare gene expression profiles between 2 groups of patients. ...
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Simulate microarray technical error
I need to simulate some microarray experiment datasets. I have the levels of expression of a set of synthetic genes in different experimental conditions. For simplicity of the method this levels are ...
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Is this really suited for 2-way ANOVA?
My dataset is a biological analysis of a disease which takes 3 months to develop, and we're looking at treatment of that disease. The data is gene expression data and we want to identify changes in ...
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Dealing with perfectly confounded microarray experiment
I need to compare microarray data, where all of the "cases" were hybridized in one batch and all of the "controls" in another, so I have no way of removing this batch effect.
What would be the best ...
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Suitable analysis for my data...?
everyone, I'm new at the statistical analysis so I need some help from more experienced people.
I'm working with biological data from gene expression analysis. I have 5 treatments (1 parental toxin; 3 ...
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Why my MA plot shows a pattern/trend while the normalized expression values not?
I am doing microarray analysis using bioconductor. I first performed the quality checks which was ok according to recommendations. I created an MA plot after normalizing the AffyBatch data. My MA ...
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A way to test for enrichment of differentially expressed genes in a genomic location
I have an experiment where I expect a certain genomic location to influence gene expression levels of nearby genes. I have data for expression levels (Agilent 4x44 microarrays, Drosophila) in two ...
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Limit of quantile normalization
Is quantile normalization adequate for normalizing
data with very few samples?
For example this microarray data. Typically after normalization
we'd like to compare ...
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Simulated Microarray data [closed]
I would like to generate synthetic microarray data sets for simulation purposes.
The web and literature search so far returned the following:
Microarray Simulator - It provides Matlab skripts to ...
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Denoising of microarray data using PCA
I'm looking for references and comments regarding the validaty of the following method for data denoising, which I found while reading a code doing analysis of some gene expression dataset. The ...
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Should false discovery be controlled at the data acquisition level, or should this be at the data interpretation level?
Should false discovery be controlled at the data acquisition level, or should this be at the data interpretation level?
I have an experiment in which microarrays were used to quantify the expression ...
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Interpret Silhouette plot for large microarray dataset
For a microarray experiment with ~40,000 probes and ~30 samples I used the clara function from R to cluster my expression matrix. How do I interpret this silhouette plot?
Firstly, I don't understand ...
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shorth function in R and microarray analysis
I'm trying to reproduce an analysis (a transcriptomic analysis) that I found in a research paper. The methods section says:
After normalization an expression threshold for each cell line was
...
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Is the overlap between two gene expression samples significant?
I have performed an experiment to study the response of a yeast (that contains 5000 genes) to stress caused by heat shock. I have one list of 48 genes that are overexpressed at 37ºC and another list ...
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Is cross-validation an effective approach for feature/model selection for microarray data?
I've been working with WEKA to build class predictors using this (rather old..) breast cancer dataset. The dataset is divided into a training and a test set. I've been testing different learning ...
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Testing for equality of variance in permutations (microarray analysis with bioconductor)
I have measured whole-genome gene expression in two groups of animals, n=6 in each group. My goal is to detect differentially expressed genes - pretty standard analysis. The typical thing to do, and ...
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How to highlight predefined groups in PCA individual map?
This has a simple answer but it has been eluding me nonetheless.
I have been trying to build a PCA plot from scratch with the ability to plot predefined groups in different colors. I can plot PCA ...
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Using background genes to evaluate gene expression level
I have some gene measurements from a microarray experiment.
On each array, I have a set of "non expressed" background genes.
I have three replicate measurements for some tissues.
How to use the ...
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Calculating the probability of gene list overlap between an RNA seq and a ChIP-chip data set
Hopefully someone on these forums can help me out with this basic problem in gene expression studies.
I did deep sequencing of an experimental and a control tissue. I then obtained fold enrichment ...
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What does the gene set score tell in GSA analysis?
I carrying out a gene set analysis to investigate if a list of genes are differentially expressed before and after a medical treatment (paired analysis).
The list of genes was made (a priori) by a ...
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microRNA analysis statistical methodology review [closed]
I am doing a review of statistical methodology used for microRNA data obtained from Affymetrix platform. I have data from 178 patients and their prognosis information as well as recurrence of disease ...
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Comparing numbers of p-values from many linear models
My current dataset has three conditions, and we've measured the activity levels of 10,000 genes in each condition. Replicated 8 times.
Using 10,000 linear models, we determine for each pair of ...
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How does quantile normalization work?
In gene expression studies using microarrays, intensity data has to be normalized so that intensities can be compared between individuals, between genes. Conceptually, and algorithmically, how does "...
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Adding high-dimensional data to mutivariate Cox model
I have a survival cancer clinical trials dataset from which I have generated Cox models using forward likelihood ratio testing within R. These models are based on 'traditional' cancer variables (eg. ...
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How to do weighted pair hierarchical clustering in R?
Here is an example of hierarchical clustering of genes in the microarray data using the weighted pair gene method in Spotfire. I am not sure how to do this in ...
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