Do you want State-of-the-Art biostatistical analysis of your gene or protein expression data? Look no further. Our Basic Data Analysis is created to give you an analysis with a full in-depth overview of your data.
Do you want in-depth data analysis with a bioinformatic annotations of the tendencies found in your data? Our advanced data analysis provides you the best biological overview.
Do you have multiple expression dataset, which need to compare as well as interpret individually? Our multiple comparison analysis gives you, what you need, looking for several types of correlation between your datasets.
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|Max. number of datasets||1||1||Unlimited|
|Max. data points||10.000||Unlimited||Unlimited|
|Delivery Time||5 days*||7 days*||Varying|
|Principal Component Analysis|
|Contact us for a non-binding offer|
*In workdays. It might be necessary to add further time if the structure of your data set differs significantly from what we are using - but in this case you will be contacted.
It is only possible to create a bioinformatics analysis if sufficient data. Our guarantee ensures that you do not pay for anything you do not receive. Thus, we downgrade (and repay the difference) to a Basic Data Analysis if the data foundation is not present.
In pre-analysis, we filter your data and test the distribution of the data. If the data doesn’t follow a normal distribution, we test if it can be transformed (e.g. log2 transformation) to an approximate normal distribution. If we can’t achieve a normal distribution none parametric tests are used in the analysis. In pre-analysis, we also test for outliers, and if outliers are detected, we will deliver two analysis one with and one without the outliers, thus ensuring you get most from your data.
We use Principal Component Analysis to convert possibly correlated subjects into a set of linearly uncorrelated subjects and thus, enabling visualization of high dimensional data in fewer dimensions with a minimal loss of information. Furthermore, it is used to emphasize strong patters among subjects and thereby we can identify potential outliers if such should be in the data.
We use clustering methods to investigate the variance and to detect potential sub-groups or sub-studies in larger datasets. Should potential sub-groups or studies appear in the data, we will include statistical analysis between the cluster and sample-groups within, thus bringing the analysis a step towards personalized based analysis. The Ward’s method also known as minimum variance method uses the error sum of squares in its objective function. It is a criterion applied on hierarchical cluster analysis and we used it to investigate how the expression data cluster accordingly to variance. We include this clustering method, as it is more likely to detect clusters with unequally diameter and less dependent of round shaped clusters, when compared to k-means clustering.
Although the Ward method and K-mean uses the same objective function they have different approaches since the Ward method is a agglomerative (bottom-up) approach and divisive (top-down) approach. Furthermore, K-means assumes data to be elliptical arranged. Furthermore, the K-means Clusters can change in arbitrary ways when the amount of clusters are changed.
Depending on the data we used various statistical tests. In factorial studies, we use parametric test such as Student’s t-test and ANOVA, if data is normal distributed. If normal distribution cannot be achieved use nonparametric tests like the Mann–Whitney U test. In Treatments and time series studies, we apply different correlation tests. All p-values are also adjusted for Multiple Comparisons.
In larger studies, we recommend that you buy our Advanced Data Analysis which also includes the bioinformatics. Here we address, which pathways the genes/proteins found significantly regulated can be annotated to through a pathway enrichment analysis. We also investigate if genes/proteins have been found in multiple pathways and suggest which have been found connecting the annotated pathways.
The Jackknife resampling method is used to make robust variance analysis of sub-groups. It is a leave-one-out approach similar to the bootstrap method and thus, makes the variance analysis more robust with regard to study participants.
Currently, we mostly analyze gene and protein expressions but feel free to ask if you have other data to be processed. If it matches our technology, we can deliver.
Gene Expressions: with classical gene identifiers, such as ENTREZID and Gene Name.
Protein Expressions: with classical gene identifiers, such as UniProtKB AC/ID, ENTREZID and Gene name.
Currently, we support the following formats: