Questions tagged [microarray]

DNA microarrays are used to measure the expression levels of large numbers of genes simultaneously.

Filter by
Sorted by
Tagged with
0 votes
0 answers
15 views

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 ...
user avatar
0 votes
1 answer
20 views

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 ...
user avatar
2 votes
0 answers
142 views

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 ...
user avatar
2 votes
1 answer
1k views

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 ...
user avatar
1 vote
1 answer
81 views

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, ...
user avatar
  • 11
3 votes
0 answers
127 views

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 ...
user avatar
  • 193
3 votes
1 answer
450 views

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 ...
user avatar
  • 767
1 vote
0 answers
84 views

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 ...
user avatar
1 vote
1 answer
491 views

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 ...
user avatar
  • 11
2 votes
0 answers
108 views

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 ...
user avatar
  • 121
0 votes
1 answer
56 views

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 ...
user avatar
1 vote
0 answers
51 views

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. ...
user avatar
  • 11
3 votes
1 answer
3k views

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 \...
user avatar
2 votes
1 answer
55 views

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 ...
user avatar
  • 21
1 vote
1 answer
776 views

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 ...
user avatar
  • 193
2 votes
0 answers
70 views

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 ...
user avatar
0 votes
1 answer
129 views

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 ...
user avatar
  • 1
2 votes
0 answers
37 views

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. ...
user avatar
2 votes
0 answers
90 views

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 ...
user avatar
  • 61
7 votes
2 answers
441 views

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 ...
user avatar
1 vote
1 answer
1k views

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 ...
user avatar
2 votes
0 answers
89 views

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 ...
user avatar
  • 646
1 vote
2 answers
2k views

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 ...
user avatar
  • 123
0 votes
1 answer
241 views

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-...
user avatar
2 votes
1 answer
136 views

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 ...
user avatar
  • 385
1 vote
1 answer
63 views

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. ...
user avatar
  • 21
0 votes
0 answers
54 views

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 ...
user avatar
1 vote
3 answers
648 views

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 ...
user avatar
  • 1,013
1 vote
0 answers
97 views

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 ...
user avatar
  • 21
2 votes
1 answer
123 views

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 ...
user avatar
0 votes
0 answers
1k views

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 ...
user avatar
1 vote
2 answers
322 views

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 ...
user avatar
  • 211
1 vote
1 answer
392 views

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 ...
user avatar
1 vote
0 answers
574 views

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 ...
user avatar
  • 904
2 votes
1 answer
288 views

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 ...
user avatar
  • 2,484
6 votes
2 answers
296 views

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 ...
user avatar
  • 555
2 votes
2 answers
1k views

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 ...
user avatar
  • 697
3 votes
0 answers
199 views

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 ...
user avatar
8 votes
2 answers
9k views

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 ...
user avatar
  • 247
6 votes
2 answers
782 views

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 ...
user avatar
  • 81
5 votes
1 answer
1k views

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 ...
user avatar
  • 211
4 votes
1 answer
10k views

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 ...
user avatar
  • 41
0 votes
0 answers
213 views

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 ...
user avatar
16 votes
2 answers
23k views

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 ...
user avatar
3 votes
0 answers
161 views

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 ...
user avatar
1 vote
0 answers
103 views

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 ...
user avatar
6 votes
1 answer
114 views

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 ...
user avatar
16 votes
1 answer
13k views

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 "...
user avatar
6 votes
2 answers
230 views

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. ...
user avatar
  • 165
2 votes
1 answer
1k views

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 ...
user avatar
  • 6,212