Spotxel® Microarray Image and Data Analysis Software
Spotxel® provides easy-to-use microarray image and data analysis software tools for protein microarrays, antibody microarrays, and gene microarrays. The software supports microarray image analysis, automatic batch processing of many images, replicate processing, data filtering and normalization, and discovery of important features and samples. Employing powerful spot finding algorithms and an effective batch processing tool, Spotxel® outperforms a market leader of microarray image analysis software on different datasets. Spotxel® works natively on both Windows and Mac OS X platforms.
Download the free trial now and use the software for 14 days for free.
|Spotxel® Values||Your Benefits|
|14-day free trial.||You can try to before buy.|
|Easy-to-use and intuitive with graphical user interface.||See what you get. Have a microarray slide quantified with just a few clicks.|
|Powerful spot finding methods.||Ensures correct detection of spots’ signal even if it slightly deviates from the array (or grid).|
|Effective array alignment and batch processing tool: Spotxel® outperforms a market leader of microarray image analysis software on antibody and protein microarray data.||
|Low cost and long-time support: The software license is affordable according to your need.||
|Easy to plug in your current microarray platform.||The software supports common data formats which enable you to employ Spotxel without change to your current analysis workflow.
|Support many background correction and noise processing methods to handle different kinds of noises and non-specific bindings.||Depending on the source of noise and non-specific binding signal:
|Support replicate processing, data filtering, and data normalization.||
|Include built-in data mining tools such as Principal Component Analysis and Hierarchical Clustering Analysis.||No conversion between different file formats of different software tools. Reduce cost for data analysis tools.|
|Works natively on Windows & Mac OS X.||Run as a native application, no need to install additional libraries, with native look and high-performance execution.|
|Independent from detection methods.||Work with both fluorescent detection and colorimetric detection methods. Applicable for low-cost detection method such as flatbed scanners.|
Spotxel® is the microarray analysis software of choice for your lab when:
- You lose too much time for manual processing of microarray images: Let Spotxel® handle that work for you.
- You need a microarray analysis software solution for your product, service, or an in-house screening system: Spotxel® provides a comprehensive set of high-quality microarray data analysis tools which can be customized to your system. Its low-cost licenses will enable your customers to use your product and service more easily.
- You want an alternative solution or a replacement of the current microarray image analysis software: Spotxel® supports common input formats (TIFF images and GAL files) and output formats (CSV and GPR files). This enables you to employ Spotxel without change to your current analysis workflow.
Image Analysis & Quantification of Microarray Data
Quantification of microarray data with Spotxel® is straightforward. All you need to do are:
- Load the microarray image(s) in TIFF format.
- Load the array file in GAL format.
- Align the array (grids) with the spots in the image (can be done automatically).
- Click the Quantify button or choose that command from the context menu.
Tools that ease your work and save your time
- You can save the quantified data, the aligned array file, and the path to the image into a project file. When working on this analysis again, simply open the project and all the data will be loaded.
- Automatic array alignment: Finds the spots in the image and aligns the array with them.
- Automatic contrast adjustment: Adjust contrast to maximize the spots’ visibility.
Block and Spot Properties - Image & Array Rotation
Spotxel® supports two spot finding methods:
- Fixed-Spot: The software uses the spot’s shape and area as specified in the GAL file to calculate the spot’s intensity value.
- Flex-Spot: This powerful algorithm can detect the spot even though its shape and position are not in accordance with the specification in the GAL file.
In the picture on the right, the spots’ position and shape specified by the GAL file are depicted by the white dashed circle. Note that the printed spots, shown in red, have different shape and position from their specification. Despite that fact, the spots can be detected precisely by the Flex-Spot method with border highlighted in blue.
The microarray images may contain noises that mislead the spot detection procedure and result in wrong quantified data. The noise can be background noise that span across the whole slide (the following picture on the left). It can also be foreground noise like the two large red bands (the following picture on the right). In the case of background noise, we want to “remove” the background layer so that only the meaningful signal remains. Foreground noise like the two red bands should not be part of a valid spot’s signal.
The software can effectively handle this task. The results can be seen in the same pictures: only valid spots’ signal are highlighted with a blue border (by the Flex-Spot method).
The same background correction can be applied to all spots in the entire array or a block. It can be done automatically with local background correction method or finely using background controls.
- Local background correction is the default option. Simply quantify the array data; all spots at the selected level - block or array - will have the same background value.
- By creating a background control located at the image region which best represents the background signal, you can define the background value for the entire array or a block.
The scatter plot depicts the features on a two-dimensional chart according to their quantified values. In addition, you can conveniently examine and select features of interest with the following tools.
- Use the lower and upper threshold bars to select features.
- Clicking on a feature on the plot will show its spot image as well as its annotation data.
- Classify features into groups using K-Means Clustering.
- Export selected features or a classified group of features to a CSV file.
Array Alignment and Batch Processing
The Array Alignment and Batch Processing functions automate the time consuming tasks when processing a large number of microarray images – the array alignment and the data quantification - and thus free the user from this tedious work.
Given a scanned image and the array annotation file of a microarray, the Array Alignment function finds the spots in the image and automatically aligns the array to them.
The Batch Processing function enables the user to specify a set of microarray images and their array annotation file, and then let the software perform the array alignment and data quantification for all the images. The batch processing is fully automated; it does not need any user’s involvement during execution and can be run e.g. at night or during the weekend.
Batch Running Modes
You can configure a batch to perform only the array alignment, or the quantification of the array data, or both of them. Furthermore, you can run the batch in two modes:
- Process all images continuously: The batch is processed continuously from the first image to the last one.
- Stop and review after each image: You can review the processing results for image before proceeding to the next one.
The software creates three data files for each microarray images.
- an array file whose layout is aligned with the spots in the image,
- a CSV file containing only the quantified data, and
- a project file containing the analyzed data for this image. Opening this project file will load the image, the aligned array, and the quantified data.
The software saves the batch data - containing the list of images, the original array file, and running options - to an XML file. The tasks and time points are tracked in a batch log also in the XML format.
Replicate Processing, Data Filtering, and Data Normalization
The software includes a very handy tool to preprocess and normalize microarray data.
- Replicate processing: The signal value of a feature’s replicates can be aggregated into a single value, being calculated either as the mean or the median of the replicates’ value. Replicates can be identified as spots having the same header such as Name, ID, or Peptide.
- Header filtering: Data can be limited to features of interest by means of header filters. These can be like containing one or multiple keywords or excluding some, or matching regular expression search patterns.
- Numeric value filtering: Lower and upper thresholds for the dataset can be interactively set. The user can choose to apply the thresholds to all columns (samples), or only a number of them.
- Microarray data from different samples, whose absolute signal values might be biased and mislead the picture, can be normalized with the following methods:
i) Z-Score: Calculate the relative signal value of a feature as how far, in terms of standard deviations, and in what direction a feature deviates from the central of all features.
ii) Z-Factor with negative controls: Check the screening quality using the standard deviation and the mean value of screening features as well as those of negative controls (Zhang et al., 2000).
iii) Ratio to mean value of controls: This is useful if the study employs some controls as calibration probes. It calculates the normalized vaue as the ratio of the feature’s signal value to the mean of selected controls’ signal value.
Data Mining Tools
Data mining tools assist you to discover useful information from complex data sets. Spotxel® supports K-means Clustering, Hierarchical Clustering Analysis, and Principal Component Analysis.
Assume that you have k microarray images as the result of testing the microarray with k samples. The above batch processing thus generates k project files. You can then directly load these project files and use the data mining tool to discover features that influences your microarray study and the relationship between them. The software also supports data sets stored in a CSV file or compiled from a list of GPR files.
Principal Component Analysis
Hierarchical Clustering Analysis
K-Means Clustering Analysis
- Classify features with similarity into groups.
- Used together with the scatter plot to classify and select features of interest.
12 Dec 2018 - Demo video of trajectory and 3D-reconstruction on autonomous vehicle dataset.
09 Oct 2018 - Demo video of object detection and multi-object tracking on autonomous vehicle...
30 Aug 2018 - Demo video for multi-object tracking on self-driving data is available at YouTube.