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Fig. 1 | Genome Biology

Fig. 1

From: Robust normalization and transformation techniques for constructing gene coexpression networks from RNA-seq data

Fig. 1

Pipeline for benchmarking the optimal workflow for constructing coexpression networks from RNA-seq data. The main pipeline was executed for the original GTEx and SRA datasets and a large collection of datasets of different sizes resampled from the GTEx datasets. Three key stages—within-sample normalization, between-sample normalization, and network transformation—where we tested method choices are highlighted in different colors. All the other stages were composed of standard selection, filtering, and data transformation operations. The coexpression networks resulting from all the workflows were evaluated using two gold-standards that capture generic (tissue-naive) and tissue-aware gene functional relationships. Finally, all the evaluation results were used to analyze the impact of various aspects of the workflows, methods, and datasets on the accuracy of coexpression networks. Abbreviations: CPM (counts per million), RPKM (reads per kilobase million), TPM (transcripts per million), QNT (quantile), TMM (trimmed mean of M values), UQ (upper quartile), CTF (counts adjusted with TMM factors), CUF (counts adjusted with upper quartile factors), CLR (context likelihood of relatedness), and WTO (weighted topological overlap)

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