If you know your data, it should be an easy task to do for yourself. Simply put your data into our template as instructed, and it should be ready to upload.
If you would prefer to let us prepare your data, we are happy to do so. Simply, check the box “Purchase data preparation” and upload your datasheet.
1. Download three templates
2. Get an overview of your data
3. Insert your data into the templates
4. Save and upload your data
There are four factors we need to have in mind when setting up the three templates.
If you have unanswered questions after the step-through guide, please find the Frequently Asked Questions section at the bottom of this page.
A unique identifier for each sample.
Reference to the gene/protein, which expression has been measured.
We prefer Entrez gene identifiers or Uniprot identifier, examples: ENSMUSG00000090061, Q6P5U7, respectively. Yet gene names can also be used, like NWD2.
Identify the sample groups you want to compare, like disease state or treatment state. Remember, we do not charge you per sample group, so feel free to include groups like gender. We want to ensure you get the most information from your data.
If some samples are technical or biological replicates.
To begin with open all three .csv-files on your computer. The templates are named as follows:
Get onwards by examining the following steps.
First, insert the gene or protein ID groups in the desired order in row 1.
Insert your sample groups into the desired order in row 1.
Skip to the next step.
If needed, just keep on adding new rows for when reaching the end of our template.
Secondly, add all the Sample IDs from row A2 and below. Use the names of your sample IDs.
Add all the sample IDs from row A2 and below. Use the names of your sample IDs.
Add all the Sample IDs from row A2 and down. Use the name of your sample IDs.
Thirdly, copy and insert all the quantifications into the gene/protein ID according to the Sample ID.
Copy and insert all your specifications into the sample group according to the Sample ID.
Copy and insert all the specifications into the replicator-group according to the Sample ID.
Lastly, make sure that each sample group matches every specification across the row correctly.
Your order will soon be processed.
The answer depends on the size of your data set. You can use commonly known software for small data sets of up to approx. 65,000 rows or 256 columns, such as Microsoft Office, Numbers (Mac), Open Office, etc.
For large data sets that exceed 65.000 rows or 256 columns, we recommend using software capable of handling it, such as pyspread and Delimit.
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:
Prepare data yourself:
Let us handle the data preparation:
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