Biostatistics & Bioinformatics FAQs

This depends on the data analysis that we will perform.

If data from RayBiotech arrays will be analyzed with our Biostatistics & Bioinformatics service, the data must be provided in the correct format:

1. Analyzed with the Analysis Tool software. The Analysis Tool software is available for free to download on the array's product page. If you have difficulty locating the software, please e-mail [email protected] and provide the array catalog numbers that you used.

2. Normalized inter-array data. The Analysis Tool software normalizes data within a slide, but does not perform across-slide normalization. Learn more about normalizing array data on this page under "How do I normalize my array data?"

   RayBiotech can provide the correct data format for RayBiotech arrays as part of our array full testing services or array analysis services (additional fees apply).

   If other types of data will be analyzed, we will let you know what type of format we will require.

Click on a service below to view an example report:

• Biomarker Selection
• Cluster Analysis
• Data Clean-Up
• Differential Expression Analysis
• Pathway Analysis

This step is important for a couple of reasons.

The first reason is that this service puts the data in a format that is required by the other services. In other words, if a dataset does NOT go through "Data clean-up", none of the other services can be performed.

The second reason is that variability within and across arrays is common with protein arrays, thus data normalization and transformation essentially improve the technical reproducibility. Outlier data can skew results. If this service is NOT used, it is highly probable that any potential differences between sample groups are not identified. Alternatively, differences that are detected may not be reliable (i.e., false positives).

We can identify biomarkers by their differential expression; enrichment of pathway, molecular function, biological process, and protein-protein interactions; and feature selection following predictive modeling.

It depends. We appreciate feedback and suggestions, and may implement your suggestions as part of our standard services. Some figures and analyses may be free depending on the selected package, number of samples and workload that it will take. Please contact us to find out.

For packages using only one service, our turnaround time is 5 - 7 business days. For the other packages including > 2 services, our turnaround time is 10 - 12 business days.

Once we receive all of the information that we need from you, our turnaround time is 5 - 7 business days.

We can. The format needs to be in .csv or Excel. The protein symbol or gene symbol must be listed by row. The sample must be listed by column. For these analyses, you will need to contact us for pricing.

We can do analyses on DNA data and the cost is dependent on several factors. We will need to know sample number, data quality, data size, and data type (i.e., how was it analyzed?). You also need to let us know exactly what type of information you’re looking for, such as peak identity, peak annotation, sample comparison, etc. Our turnaround time is usually 2 - 3 weeks. Please contact us to learn more.

We often do, depending on the number of samples that will be analyzed. Please contact us to find out.

There is a required amount of time to set up the analyses per experiment, and it is one of the most labor-intensive steps of the process. Therefore, we need to charge a minimum amount to cover the labor required for setting up the analyses.

Yes, you can. If an Excel format of your report is not provided to you, just ask us for it ☺

The "Differential expression analysis", "Cluster analysis", and "Pathway analysis" services are interested in finding the statistical or biological relationship of a biomarker with the disease. The "Biomarker selection" service is interested in finding the combination of biomarkers that will classify or predict disease status.

"Data clean-up": 1 sample
"Pathway analysis": 1 sample per group
"Cluster analysis": 1 sample per group
"Differential expression analysis": 3 samples per group
"Biomarker selection": 10 samples per group

Per group refers to a condition to be tested. If healthy versus cancer samples are to be compared, these are two different groups and a minimum of 20 samples would be needed to perform the "Biomarker selection" service.

Low sample numbers for "Differential expression analysis" and "Biomarker selection" services will result in less accurate modeling. Researchers should try to use at least 10 samples per group for the "Differential expression analysis" service and at least 25 samples per group for the "Biomarker selection" service.

For a general, non-math understanding of how many of these analyses work, please click here.