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TROM: A Testing-Based Method for Finding Transcriptomic Similarity of Biological Samples

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Abstract

Comparative transcriptomics has gained increasing popularity in genomic research thanks to the development of high-throughput technologies including microarray and next-generation RNA sequencing that have generated numerous transcriptomic data. An important question is to understand the conservation and divergence of biological processes in different species. We propose a testing-based method TROM (Transcriptome Overlap Measure) for comparing transcriptomes within or between different species, and provide a different perspective, in contrast to traditional correlation analyses, about capturing transcriptomic similarity. Specifically, the TROM method focuses on identifying associated genes that capture molecular characteristics of biological samples, and subsequently comparing the biological samples by testing the overlap of their associated genes. We use simulation and real data studies to demonstrate that TROM is more powerful in identifying similar transcriptomes and more robust to stochastic gene expression noise than Pearson and Spearman correlations. We apply TROM to compare the developmental stages of six Drosophila species, C. elegans, S. purpuratus, D. rerio and mouse liver, and find interesting correspondence patterns that imply conserved gene expression programs in the development of these species. The TROM method is available as an R package on CRAN (https://github.com/Vivianstats/TROM) with manuals and source codes available at http://jsb.ucla.edu/trom-transcriptome-overlap-measure.

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Authors and Affiliations

  1. Department of Statistics, University of California, Los Angeles, CA, 90095-1554, USA

    Wei Vivian Li, Yiling Chen & Jingyi Jessica Li

  2. Department of Human Genetics, University of California, Los Angeles, CA, 90095-7088, USA

    Jingyi Jessica Li

Authors
  1. Wei Vivian Li
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  2. Yiling Chen
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  3. Jingyi Jessica Li
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Corresponding author

Correspondence to Jingyi Jessica Li.

Appendix

Appendix

See Tables 2, 3 and Figs. 7, 8, 9, 10, 11, 12, 13, 14, 15, and 16.

Table 2 Selected z-score thresholds for different species
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Table 3 Description of sample labels of different species
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Fig. 7
figure 7

Correspondence maps of within-species and between-species TROM scores (calculated based on the z-score thresholds listed in the table). TROM scores are saturated at 6. The names of the species are marked as row or column labels of the corresponding heatmaps. For the Drosophila species, the stages labels 1–13 refer to Embryo 0–2, 2–4, 4–6, 6–8, 8–10, 10–12, 12–14, 14–16, 16–18, 18–20, 20–22, 22–24 and 24–26 h, respectively

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Fig. 8
figure 8

Outline of the package TROM. The three left (and right) heatmaps illustrate the within-species (and between-species) comparison results by using TROM (with z-score threshold 1.5 for both fly and worm), Pearson correlation and Spearman correlation. Greater similarities are shown in darker colors. The results show that compared to the popular Pearson and Spearman correlations, TROM can find clearer correspondence patterns. TROM takes gene expression matrices and orthologous genes of the species of interest as input. The functions select.associated.genes and select.associated.orthologs select the associated genes of different biological samples among all the genes or only among the genes with orthologs in the other species to be compared with. They also provide graphical summaries of the numbers of selected associated genes and orthologs. The functions ws.trom and ws.trom.orthologs perform the within-species transcriptome comparison, find the overlapping associated genes between every two samples and calculate within-species TROM scores. The function bs.trom performs the between-species transcriptome comparison, find the overlapping associated orthologs between every two samples from different species and calculate the between-species TROM scores. The function heatmap.3 visualizes the TROM scores in a heatmap, with various add-on options for customization. The functions find.top.GO.terms and find.top.GO.slim.terms perform gene set enrichment analysis and find top enriched Gene Ontology (GO) terms and GO slim terms in the associated genes. Instead of using the selected associated genes, users may input customized gene lists representing characteristics of different biological samples into the above functions. Please see the package manual and vignette of TROM for details

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Fig. 9
figure 9

Correlation measures calculated based on the union of associated genes. Pearson correlation (a) and Spearman correlation (b) for every pair of D. melanogaster stages calculated based on the union of associated genes of all stages. Pearson correlation (c) and Spearman correlation (d) for every pair of D. melanogaster and C. elegans stages calculated based on the union of associated ortholog pairs of all stages. These heatmaps show that correlation measures calculated based on associated genes only still cannot lead to clear correspondence patterns

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Fig. 10
figure 10

Comparison of TROM and Pearson/Spearman correlation on simulated data, with a for fly and b for worm. In both panels, the first row gives the true sample relationships (1: high dependence in associated genes; 0: otherwise) defined as in Eq. 6 for varying c. The second row gives the mean receiver operating characteristic (ROC) curves on the 200 simulated gene expression matrices, given the true labels in the first row. The third row gives the mean Neyman–Pearson receiver operating characteristic (NP-ROC) curves, accordingly. The 95 % confidence intervals of the area under the curve (AUC) are marked next to the curves

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Fig. 11
figure 11

Correspondence maps of developmental stages. TROM scores are calculated using the RNA-seq data of D. melanogaster and the microarray data of the other five Drosophila species

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Fig. 12
figure 12

Top 20 enriched biological process GO terms of D.melanogaster. The enrichment scores in the heatmap are calculated based on stage-associated genes identified from the RNA-seq data (with z-score threshold 1.5) and saturated at 6. For each stage, the common enriched GO terms identified from both microarray (Fig. 13) and RNA-seq datasets are marked in red color (Color figure online)

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Fig. 13
figure 13

Top 20 enriched biological process GO terms of D.melanogaster. The enrichment scores in the heatmap are calculated based on the stage-associated genes identified from the microarray data (with z-score threshold 0.5) and saturated at 6. For each stage, the common enriched GO terms identified from both microarray and RNA-seq (Fig. 12) datasets are marked in red color (Color figure online)

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Fig. 14
figure 14

Top 10 enriched biological process GO terms of mouse liver. The enrichment scores in the heatmap are calculated based on the stage-associated genes identified from the microarray data (with z-score threshold 1.5). For each stage, the highly relevant GO terms that have been confirmed in previous studies are marked in red color (Color figure online)

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Fig. 15
figure 15

Developmental trees constructed using stage-associated genes (identified with the z-score thresholds in the table). a–f are for Drosophila species and G is for C. elegans

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Fig. 16
figure 16

Spearman correlations of the developmental stages of D. melanogaster (fly) and C. elegans (worm). a The first panel shows the original Spearman correlations of fly stages, while the rest panels show the Spearman correlations of fly stages under different thresholds. b The first panel shows the original Spearman correlations of worm stages, while the rest panels show Spearman correlations of worm stages under different thresholds. c TROM scores of fly. d TROM scores of worm. All the values under the selected threshold are set to the minimum value of each correlation matrix

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Li, W.V., Chen, Y. & Li, J.J. TROM: A Testing-Based Method for Finding Transcriptomic Similarity of Biological Samples. Stat Biosci 9, 105–136 (2017). https://doi.org/10.1007/s12561-016-9163-y

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