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Analysis framework and experimental design for evaluating synergy-driving gene expression

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The mechanisms by which genetic risk variants interact with each other, as well as environmental factors, to contribute to complex genetic disorders remain unclear. We describe in detail our recently published approach to resolve distinct additive and synergistic transcriptomic effects after combinatorial manipulation of genetic variants and/or chemical perturbagens. Although first developed for CRISPR-based perturbation studies of isogenic human induced pluripotent stem cell-derived neurons, our methodology can be broadly applied to any RNA sequencing dataset, provided that raw read counts are available. Whereas other differential expression analyses reveal the effect of individual perturbations, here we specifically query interactions between two or more perturbagens, resolving the extent of non-additive (synergistic) interactions between perturbations. We discuss the careful experimental design required to resolve synergistic effects and considerations of statistical power and how to quantify observed synergy between experiments. Additionally, we speculate on potential future applications and explore the obvious limitations of this approach. Overall, by interrogating the effect of independent factors, alone and in combination, our analytic framework and experimental design facilitate the discovery of convergence and synergy downstream of gene and/or treatment perturbations hypothesized to contribute to complex diseases. We think that this protocol can be successfully applied by any scientist with bioinformatic skills and basic proficiency in the R programming language. Our computational pipeline ( is straightforward, does not require supercomputing support and can be conducted in a single day upon completion of RNA sequencing experiments.

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Fig. 1: General overview of the analysis pipeline, experimental design and differential expression contrast design.
Fig. 2: Synergistic effects.
Fig. 3: Differential expression analysis output.
Fig. 4: Synergistic effect analysis output.
Fig. 5: Gene set enrichment analysis output.
Fig. 6: Over-representation analysis output.

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Data availability

RNA sequencing data from our study of schizophrenia risk genes20, including their individual and combined perturbation, are available at!Synapse:syn20502314 and as Supplementary Data 1. Downloading these data requires that you are a registered Synapse user and have agreed to the Synapse terms of use. Figures 3, 4, 5 and 6 were created based on these data. RNA sequencing data from the NRAS-mutant melanoma study (ref. 44) are available as Supplementary Data 2 and were reanalyzed here to generate Extended Data Figs. 14.

Code availability

Code is available at, under the MIT License.


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This work was partially supported by National Institutes of Health grants R56 MH101454 (K.J.B.) and R01 MH106056 (K.J.B.). This work was supported, in part, through the computational resources and staff expertise provided by Scientific Computing at the Icahn School of Medicine at Mount Sinai.

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Authors and Affiliations



N.S., together with K.J.B. and G.H., developed the synergistic analysis. N.S., C.S. and P.J.M.D. independently ran the synergistic analysis on the same dataset and tested the analysis on separate datasets. N.S. and K.J.B. wrote the manuscript.

Corresponding authors

Correspondence to Gabriel Hoffman or Kristen J. Brennand.

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The authors declare no competing interests.

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Peer review information Nature Protocols thanks Yang Zhou and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Key reference using this protocol

Schrode, N. et al. Nat. Genet. 51, 1475–1485 (2019):

Key data used in this protocol

Echevarria-Vargas, I. M. et al. EMBO Mol. Med. 10, e8446 (2018):

Extended data

Extended Data Fig. 1 Differential expression analysis output, related to Fig. 3.

a) Plot showing counts over cpm. Horizontal red line marks 10 counts. Arrow indicates the intersection with the plotted data, which here equals 1.4 cpm (vertical red line). b) MDS plots highlighting two metadata variables respectively. Sample data are separated by treatment (left), but not by replicate (right). c) Voom mean-variance plot. d) Volcano and mean difference (MA) plots of differential expression in the additive (left) and the combinatorial (right) comparisons. Significantly differentially expressed genes are highlighted in blue and red (Volcano plot) and the top 10 significant genes are denoted in blue (MA plot).

Extended Data Fig. 2 Synergistic effect analysis output, related to Fig. 4.

a) Plot visualizing synergistic effect power calculations. X-axis shows synergistic log2FCs. In the current example, 10 samples per condition are required to resolve a synergistic log2FC of 1.6 at 75% power. b) Histogram of synergistic P-values. c) Pie chart showing the proportions of genes that fall into different synergistic differential expression categories. d) Hierarchical clustering of the differential expression log2(fold changes) of all synergy categories, in the additive model versus the combinatorial perturbation comparisons.

Extended Data Fig. 3 Gene set enrichment analysis (GSEA) output, related to Fig. 5.

a) Competitive GSEA of differential expression in the additive (top) and the combinatorial (bottom) comparisons using limma camera, based on two cancer hallmark gene sets. b) Bar chart showing detailed results of the 10 most significant gene sets as in (A). Red lines denote enrichment FDR of 5%.

Extended Data Fig. 4 Over-representation analysis (ORA) output, related to Fig. 6.

a - b) Over-representation analysis, using a hypergeometric test, of 2 publicly available gene sets and those ‘more downregulated’ (A) and ‘more upregulated’ (B) genes with significant synergistic differential expression (FDR < 1%), ranked by adjusted significance. Red lines denote enrichment FDR of 5%.

Supplementary information

Reporting Summary

Supplementary Data 1

R code, count matrix, metadata file and gene annotation file for analysis used in ref. 20.

Supplementary Data 2

R code, count matrix, metadata file, gene annotation file and gene sets for supplementary analysis using data from ref. 44.

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Schrode, N., Seah, C., Deans, P.J.M. et al. Analysis framework and experimental design for evaluating synergy-driving gene expression. Nat Protoc 16, 812–840 (2021).

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