Before you can start your analysis you have to select or upload a dataset:
In the next step you can customize how the data set is processed. Note:
- In particular for uploaded data sets you need to select which type of information is found in which column. Check "show fileinput options".
- log2 transformation is typically recommended for signal data
- The B-score normalization is ideally suited to address position bias in the data, but is computationally expensive and not selected by default.
- If you would like to analyze a different data set just click on the HiTSeekR logo in the top left corner to get back to the start.
- On the bottom of the page, a preview of the input data is shown. Once you are confident about the selected settings press the "Process raw data" button below.
Below you can select plots that may help to assess potential quality problems with the raw data.
Some quality issues can be accommodated with appropriate normalization. Here you can study the effect of different normalization strategies on the data:
Here you can select a normalization and hit detection strategy to generate a candidate hit list used for down-stream analysis :
Here you can investigate miRNA targets, miRNA family membership and browse the mircancer database.
Here you can find putative drug target proteins of the previously identified hit candidates.
Here you can perform down-stream analysis of genes identified in the previous step:
The High-Throughput Screening kit for R (HiTSeekR) was developed as a joint project between
Contact: Markus List <markus.list=.AT.=mpi-inf.mpg.de>
View on GitHub →
Project page with tutorial →
The following is a list of R packages used in HiTSeekR for annotation and systems biology analysis:
- reactome.db v. 1.54.1
- KEGG.db v. 3.2.2
- GO.db v. 3.2.2
- HTSanalyzeR v. 2.22.0
- mirbase.db v. 1.2.0
- RmiR v. 1.26.0
In addition, HiTSeekR integrates the following external resources: