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ReSurfX effect: Dealing individually and combining
Current (without ReSurfX) real accuracy <<<< Five data sources at
predicted 95% accuracy each - combined
ReSurfX Suite of Solutions
Adaptive Hypersurface Technology

Data source agnostic

Novel solution for highly accurate utilization of multi-parametric data

Associated solutions that adapts to unpredictable error properties

E.g., Normalization, adaptive error correction

Data Analyses AND Technology Design  Superior Accuracy and
Outcome prediction

Repeated application enterprise product with a scalable delivery model
Cloud Product in Market on Customer Request
(customized solutions – currently specific to one tech)
Product at: https://vysen.resurfx.com
Information at: http://resurfx.com/vysen
Affymetrix GeneChip®
(a technology for measuring gene expression in large scale)
Dominant Market Solutions
Too many errors
ReSurfX
Simplified workflow – Affymetrix™ GeneChip® data
Download
Visualize
Upload from your repository/
Local machine
File input
Select included GeneChip®
ChipType
Search/Fetch from public repository
NCBI/GEO
1.
2.
Fetch CEL files
Un-compress
Data Analysis
Extract
1.
2.
3.
CEL file format conversion
Extract to usable format
Normalization
Compare ReSurfX output to a proprietary
(Rosetta Resolver™) solution
Rosetta
1840
ReSurfX
1081/946
In red: 2 fold cut
948
/983
892
/857
189
/89
Publication 1, 2008
An empirical check of the differentials using an independent approach
indicates that most Rosetta specific genes are false positives. These are
either what is estimated to be below the sensitivity of the technology with
this number of replicates by ReSurfX, or errored due to other limitations.
Found by Rosetta
but not by ReSurfX
220
7
ReSurfX
529
213
Fold change vs. p-value of genes
considered error by ReSurfX
316
Rosetta
Publication 2, 2006
Compare ReSurfX output to a widely used
public domain (BioConductor &
GenePattern) tools
Next slide: Note the number of parameters (genes) found by above pipeline but
considered error by ReSurfX
ReSurfX only > 3 fold (30 of 285)
Data of Publication 3 - UP
Ctrl
Treat
207
37
94
Data of Publication 2 - UP
UP  Upregulated
BioC only > 3 fold (99 of 360)
fold change cut problem too
Publication 3 - UP
Ctrl
Treat
Publication 2 - UP
Publication 3, 2012
(used 3 fold cut)
Note the amount of errors now if one uses analysis of
Publication 2 and compare results of Publication 3
(cumulative error in a longitudinal pipeline)
Unpublished customer data, 2013
(powerful way of easily identifying targets without confounding errors)
Among other things:
Ctrl 1
Ctrl 2
Treatment 1
Treatment 2
1. RSX found (BioC missed) an interesting kinase,
that was genetically checked and proved to be
associated with difference in phenotype
between Treatment 1 and Treatment 2
2. Other RSX specific changes corroborated as
gene families, or gene duplicates
BioC & RSX found the kinase against controls
Two different studies : Same treatment but
slightly different controls
(High reprodicibility of ReSurfX analysis)
Study 1: 178
146 of 178 overlap (2 fold change cut)
overlap increases if no fold cut
Study 2: 412
Analysis by ReSurfX
Shouldn’t you be using ReSurfX in your
data analysis for better outcomes?
(save time and money – avoid late stage failures)