Background:
Analysis of gene expression data using genome-wide microarrays is a technique often used in genomic studies to find coexpression patterns and locate groups of co- transcribed genes. However, most studies done at global “omic” scale are not focused o n human samples and when they correspond to human very often include heterogeneous datasets, mixing normal with disease-altered samples. Moreover, the technical noise present in genome-wide expression microarrays is another well reported problem that many times is not addressed with robust statistical methods, and the estimation of errors in the data is not provided.

Methodology/Principal Findings:
Human genome-wide expression data from a controlled set of normal-healthy tissues is used to build a confident human gene coexpression network avoiding both pathological and technical noise. To achieve this we describe a new method that combines several statistical and computational strategies: robust normalization and expression signal calculation; correlation coefficients obtained by parametric and non-parametric methods; random cross-validations; and estimation of the statistical accuracy and coverage of the data. All these methods provide a series of coexpression datasets where the level of error is measured and can be tuned. To define the errors, the rates of true positives are calculated by assignment to biological pathways. The results provide a confident human gene coexpression network that includes 3327 gene-nodes and 15841 coexpression-links and a comparative analysis shows good improvement over previously published datasets. Further functional analysis of a subset core network, validated by two independent methods, shows coherent biological modules that share common transcription factors. The network reveals a map of coexpression clusters organized in well defined functional constellations. Two major regions in this network correspond to genes involved in nuclear and mitochondrial metabolism and investigations on their functional assignment indicate that more than 60% are house- keeping and essential genes. The network displays new non-described gene associations and it allows the placement in a functional context of some unknown non- assigned genes based on their interactions with known gene families.

Conclusions/Significance:
The identification of stable and reliable human gene to gene coexpression networks is essential to unravel the interactions and functional correlations between human genes at an omic scale. This work contributes to this aim, and we are making available for the scientific community the validated human gene coexpression networks obtained, to allow further analyses on the network or on some specific gene associations.

• View of the subset core of the confident human gene coexpression network. The figure includes 615 gene-nodes and 2190 coexpression-links. This network is provided in Cytoscape format with full annotations about the genes. File name: HumanCoexpNtw-615g.cys download
  
  

  Graphical view of the coexpression network where the nodes correspond to genes and the edges to coexpression links. The network was produced as the intersection of two datasets (MAS5-Spearman and RMA-Pearson datasets with PPV > 0.60) to provide a confident human coexpression network that includes 615 genes and 2190 pairwise coexpression interactions. The most significant regions have been marked with background colors and labels. The color of the nodes (from red to grey) and the shape (circles or diamonds) were obtained following MCODE algorithm, being: circular nodes, the ones found with high cluster coefficient; diamond nodes, the ones with lower cluster coefficient; intensity of red color, degree of clustering changing till pale grey for the most peripheral nodes that only have one link.

  
  
  
• A group of coexpression data sets obtained at different values of correlation (r) and crossvalidation (N) coefficients is also provided. The data sets can be downloaded from the links in the tables, and they include the list of gene name pairs that coexpress for each set. For the not-assigned (NA) genes we include the Affymetrix probeset ID:
  
  

  Color plots of 3 dimensions that represent the Positive Predictive Value (PPV) calculated for each set of gene coexpression data at the different values of correlation coefficient (r) and cross-validation coefficient (N). Calculations are done for data derived from two different methods: MAS5-Spearman without gene filtering and RMA-Pearson with gene filtering.

  
  
  

  PPV	Coefficients		Number Nodes	Number Links
  	N	r
  MAS5-Spearman (non-Filtered)
  0.60	605	0.77	3052	12669
  0.70	645	0.79	2295	7874
  0.80	695	0.81	1762	4910
  RMA-Pearson (pre-Filtered)
  0.60	765	0.85	1672	5945
  0.70	835	0.87	1215	3273
  0.80	925	0.84	983	2423

  The table shows the specific values of correlation and cross-validation for three coexpression datasets derived from each method at three specific PPVs: > 0.60, > 0.70 and > 0.80 (marked as stars within the color plots). The table also shows the number of nodes and links included in each coexpression dataset.

  
  

  PPV	Union			Intersection
  	Number Nodes	Number Links		Number Nodes	Number Links
  0.60	3327	15841	download	731	2249	download
  0.70	2411	9264	download	542	1447	download
  0.80	1863	5935	download	387	1008	download

  The table shows the number of nodes and links included in each coexpression dataset. Each dataset is derived from the intersection and the union of the data obtained with each method at three specific PPVs: > 0.60, > 0.70 and > 0.80.