Cel files r

Animation created in the FLIC format; stores a series of frames but does not store parts of previous frames that are not changed; more commonly uses the. FLC or. FLI extensions. The FileInfo. If you would like to suggest any additions or updates to this page, please let us know. CEL File Extension. CEL File Extension 6 file types use the.

Affymetrix Probe Results File 2. Celestia Script File 3. Audition Loop 4. Rename this folder to rawdata. We start by reading in the sample information table. This is usually created by the person who performed the experiment. The last thing to do here is to turn probe-level information into gene-level information. You can preprocess this probe-level information in many ways. Your email address will not be published. Post Comment. On this website, I provide statistics tutorials as well as code in Python and R programming.

YouTube privacy policy If you accept this notice, your choice will be saved and the page will refresh. Leave a Reply Cancel reply Your email address will not be published. Related Tutorials. AffyBatch objects have several slots characteristics.

One of them is called phenoData. It contains labels for the samples. Despite the name, there is no implication that the labels should be phenotypic, in fact they often indicate genotypes such as wild-type or knockout. They can also contain other types of information about the samples e. As you can see, ph is a data frame. You find the names of the columns in varLabels: there is one column named sample. Another way of looking at the sample annotation is using the pData method on the AffyBatch object:.

You can give the samples more accurate names so these can be used in the plots that we are going to create later on. We will see how to do this when we create the plots. When you import CEL-files from GEO or ArrayExpress the phenoData should normally already contain informative names but many submitters skip this step so many data sets are not well annotated. Microarray data sets should include information on the probes.

AffyBatches have a slot called featureData, a data frame that contains labels for the probes. Microarray data sets should also include information on the experiment.

AffyBatches have a slot for this called experimentData. This is an example where we have a data set consisting of two groups of 3 replicates, 3 wild type controls and 3 mutants.

If you have 3 groups of 3 replicates:. This is an example where we have a data set consisting of three groups of 3 replicates, 3 control mice, 3 mice that were treated with a drug and 3 mice were treated by performing physical exercises.

You have to adjust this code according the number of groups and the number of replicates you have and change the sample names to names that are relevant for you. Instead of printing these plots in RStudio or the R editor, we will save the plots to our hard drive.

How to create microarray pictures. How to create chip pseudo-images. Another quality control check is to plot the distribution of log base 2 intensities log2 PMij for array i and probe j of perfect match probes for comparison of probe intensity behavior between different arrays. If you see differences in shape or center of the distributions, it means that normalization is required. How to create histograms of microarray data. Boxplots and histograms show the same differences in probe intensity behavior between arrays.

In order to perform meaningful statistical analysis and inferences from the data, you need to ensure that all the samples are comparable. To examine and compare the overall distribution of log transformed PM intensities between the samples you can use a histogram but you will get a clearer view with a box plot.

How to create boxplots of microarray data. Originally these MA plots were developed for two-color arrays to detect differences between the two color labels on the same array, and for these arrays they became hugely popular. This is why more and more people are now also using them for Affymetrix arrays but on Affymetrix only use a single color label. So people started using them to compare each Affymetrix array to a pseudo-array. The pseudo array consists of the median intensity of each probe over all arrays.

The MA plot shows to what extent the variability in expression depends on the expression level more variation on high expression values? How to create MA plot of microarray data. This is because we assume that the majority of the genes is not DE and that the number of upregulated genes is similar to the number of downregulated genes.

Additionally, the variability of the M values should be similar across different array-medianarray combinations. To remove some of this dependency, we will normalize the data.

Apart from images, you can also calculate simple quality metrics to assess the quality of the arrays. The first quality measure are the average intensities of the background probes on each array. You can retrieve them by using the avbg method. According to Affymetrix guidelines, the average background values of different arrays should be comparable.

The second quality measure are the scale factors: factors used to equalize the mean intensities of the arrays. You can retrieve them by using sfs method. The scale factors should be within 3-fold of each other. The third quality measure are the percent present calls: the percentage of spots that generate a significant signal significantly higher than background according to the Affymetrix detection algorithm. You can retrieve them by using percent. The precent present calls should be similar especially among replicates.

For these housekeeping genes 3 probe sets are available: one at the 5' end of the gene, one at the middle and one at the 3' end of the gene. You can retrieve them by using ratios method. The qc method is implemented in the simpleaffy package but not in the oligo package. This means that you either have to. There are many sources of noise in microarray experiments: different amounts of RNA used for labeling and hybridization imperfections on the array surface imperfect synthesis of the probes differences in hybridization conditions Systematic differences between the samples that are due to noise rather than true biological variability should be removed in order to make biologically meaningfull conclusions about the data.

The standard method for normalization is RMA. RMA is one of the few normalization methods that only uses the PM probes: Background correction to correct for spatial variation within individual arrays : a background-corrected intensity is calculated for each PM probe in such a way that all background corrected intensities are positive Log transformation to improve the distribution of the data : the base-2 logarithm of the background corrected intensity is calculated for each probe.

The log transformation will make the data less skewed and more normally distributed and provide an equal spread of up- and downregulated expression ratios Quantile normalization to correct for variation between the arrays : equalizes the data distributions of the arrays and make the samples completely comparable Probe normalization to correct for variation within probe sets : equalizes the behavior of the probes between the arrays and combines normalized data values of probes from a probe set into a single value for the whole probe set.

AffyBatches will therefore have the same characteristics and behaviour as ExpressionSets but AffyBatches will also have a set of specific characteristics and functions that are not shared by ExpressionSets. Check out this excellent overview of RMA. The difference lies in the background correction, all other steps are the same.

GCRMA uses probe sequence information to estimate probe affinity to non-specific binding. Each nucleotide of the probe contributes to the affinity of the probe. The contributions of each nucleotide on each position of the probes were estimated in a pilot experiment with artificial DNAs that mismatch the probes, done by the people who developed the algorithm. In this experiment there was no specific binding so the only thing was measured was non-specific binding.

The data of this experiment allowed to estimate the affinities of the probes. GCRMA allows you to use the affinities calculated based on this reference experiment or you can let GCRMA compute probe affinities based on the signals of the negative control probes of your own microarray experiment. These probe affinities are stored in an AffyBatch object, called affinity. By default, affinity. However, the user can also choose to compute the affinities based on the data of their own experiment and use these affinities during normalization:.

To see the effect of the background correction you can create a plot of raw versus background corrected data. How to compare raw and background-corrected microarray data. After normalization you can compare raw and normalized data. You can do this for individual genes or for all genes.

We'll start by making the comparison for individual genes. Raw intensities are stored in data, you can retrieve the raw PM intensities by using the pm method. By using the probe set ID as a second argument, you can retrieve the PM intensities of the row with this name:.