Data Analysis
Unit 

Director
Dr Guillermo Ortega Rabbione

Address
C/ Diego de León 62 – 7ª planta del Hospital.
28006 Madrid, Spain

Telephone
+34 91 520 22 00 Ext: 17304/17305

E-mail

guillermojose.ortega@salud.madrid.org

The Data Analysis Unit (UAD) at the Instituto de Investigación Sanitaria (Health Research Institute), Hospital de la Princesa (IIS-IP) aims to provide support, advice and active collaboration to basic and clinical researchers in numerical analysis and data display in biological fields.

It acts as a “first consultation” and advice service on many issues, as well as offering concrete support in the fields in which we have the most proven experience. Due to the high degree of computerisation in all medical areas, such as medical records, recordings through external, internal or portable temporary monitors, sequencing, etc., tools and methods specially designed to process ever-increasing amounts of data must be used.

For all these reasons, the UAD is key to the development of our centre’s research projects, in many cases multidisciplinary, multi-centre, national or international projects.

Dr Guillermo J. Ortega
Doctor of Physics

Dr Ancor Sanz-García
Doctor of Neuroscience

Ms Miriam Pérez-Romero
Biomedical Engineer

Computers

The Unit currently has workstations equipped for high-performance intensive numerical calculations (a Hewlett Packard Z600 server and a workstation with 10 cores, 128 GB of RAM memory and an 11 GB NVIDIA GTX 1080 Ti graphics card, which can be used for GPU-based calculations).

Storage

The Unit has storage for up to 80 TB, which makes it particularly suited to processing large amounts of bioinformatics data, especially data obtained through next-generation sequencing (NGS).

  • Time series analysis of biophysical systems

  • Mass analysis of multi-modal neurophysiological recordings (EEG, EKG, PIC, etc.)

  • Analysis by means of chaos theory in telemetric recordings of body temperature and activity in animal models (hamsters, rats, etc.)
  • Time series analysis of spikes from extracellular multi-electrode recordings. Neural code

  • Analysis and classification of cellular images using multi-fractal methods
  • Biophysical models of cellular communication through gap-junctions
  • Biophysical modelling of excitable tissues
  • Use of complex network theory to study human physiological recordings

  • Development of time series analysis methods based on chaos theory
  • Implementation of machine learning techniques
  • Development of algorithms for data normalisation in miRNA expression analysis

  • Development of programs for the analysis of DNA methylation microarrays

  • Analysis of data from mass sequencing, specifically quality control programs.
  • Display methods of complex networks
  • Management of various programming languages (R, Fortran, python, shell scripting, etc.) and operating systems (Windows, Unix)
  • Various data mining techniques

Representative publications in different thematic areas:

Non-invasive monitoring of intracranial pressure

  • Sanz-Garcia et al. (2018) Identifying causal relationships between EEG activity and intracranial pressure levels in neurocritical care patients. J Neural Eng.

Epilepsy

  • Sanz-Garcia et al. (2017) Towards Operational Definition of Postictal Stage: Spectral Entropy as a Marker of Seizure Ending. Entropy. 19:81.
  • Sanz-Garcia et al. (2016) Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients. J. Vis. Exp. 118:e54746.
  • Vega-Zelaya L et al. (2016) Assessing the equivalence between etomidate and seizure network dynamics in temporal lobe epilepsy. Clin Neurophysiol. 127:169-178.
  • Vega-Zelaya L et al. (2015) Disrupted Ipsilateral Network Connectivity in Temporal Lobe Epilepsy. PLoS One. 10:e0140859.
  • Palmigiano A et al. (2012) Stability of Synchronization Clusters and Seizurability in Temporal Lobe Epilepsy. PLoS One 7:e41799.
  • Ortega GJ et al. (2011) Impaired mesial synchronization in temporal lobe epilepsy. Clin Neurophysiol. 122:1106-1116
  • Ortega GJ et al. (2008) Synchronization clusters of interictal activity in the lateral temporal cortex of epileptic patients: Intraoperative electrocorticographic analysis. Epilepsia 49:269-280

Epigenetics

  • Ovejero-Benito et al. (2018) Epigenetic biomarkers associated with anti-TNF drugs response in moderate-to-severe psoriasis. Br J Dermatol. 178:798-800.

miRNAs

  • Martínez-Hernández et al. (2018) A microRNA signature for evaluation of risk and severity of Autoimmune Thyroid Diseases. J Clin Endocrinol Metab. 103:1139-1150.

Time series analysis of spikes from extracellular multi-electrode recordings

  • Ortega GJ et al. (2004) Conditioned Spikes: A simple and fast method to represent rates and temporal patterns in multielectrode recordings. J of Neurosci Methods 133:135-141.

Analysis and classification of cellular images using multi-fractal methods

  • Fernandez E et al. (1999) Are Neurons Multifractals? J Neurosci Methods 89:151.

Biophysical models

  • Boschi CD et al (2001) Triggering synchronized oscillations through arbitrarily weak diversity in close-to-threshold excitable media. Phys Rev E 63:12901
    Andreu E et al. (2000) Role of Architecture in Determining Passive Electrical Properties in Gap-Junction Connected Cells. Pflügers Arch – Eur J Physiol 439:789-97

Development of time series analysis methods based on chaos theory

  • Ortega G et al. (1998) Smoothness Implies Determinism in Time Series: A Measure Based Approach. Phys Rev Lett 81:4345.

RATES

IISHUP STAFF EXTERNAL PERSONNEL
Queries without numerical analysis Free €30/hour
Queries with numerical analysis €21/hour €50/hour
Project consultancy (with numerical analysis and/or preparing reports) €27/hour €70/hour
Training/Courses (R, Linux, Machine Learning) €50/hour €80/hour
Script development €30/hour €70/hour
Use of IT resources (work station, software) €100/year €400/year