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Guidance for RNA-seq co-expression network construction and analysis: safety in numbers
Open accessAbstract
Motivation: RNA-seq co-expression analysis is in its infancy and reasonable practices remain poorly defined. We assessed a variety of RNA-seq expression data to determine factors affecting functional connectivity and topology in co-expression networks. Results: We examine RNA-seq co-expression data generated from 1970 RNA-seq samples using a Guilt-By-Association framework, in which genes are assessed for the tendency of co-expression to reflect shared function. Minimal experimental criteria to obtain performance on par with microarrays were >20 samples with read depth >10 M per sample. While the aggregate network constructed shows good performance (area under the receiver operator characteristic curve ∼0.71), the dependency on number of experiments used is nearly identical to that present in microarrays, suggesting thousands of samples are required to obtain ‘gold-standard’ co-expression. We find a major topological difference between RNA-seq and microarray co-expression in the form of low overlaps between hub-like genes from each network due to changes in the correlation of expression noise within each technology. Contact: jgillis@cshl.edu or sballouz@cshl.edu Supplementary information: Networks are available at: http://gillislab.labsites.cshl.edu/supplements/rna-seq-networks/ and supplementary data are available at Bioinformatics online.
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TL;DR
This paper benchmarks RNA-seq co-expression network construction against microarrays and finds that simple correlation-based networks, especially when aggregated across many datasets, recover known gene function comparably to microarrays. However, RNA-seq and microarray networks differ sharply in topology, largely because low-expression noise creates different hub genes.
Key findings
- Individual RNA-seq co-expression networks performed modestly, with an average AUROC of 0.585; only 0.7% of GO terms averaged above 0.8 AUROC, and only 2% ever exceeded 0.9 AUROC in any assessed network.
- Aggregating networks improved functional connectivity to about 0.71 AUROC for both RNA-seq and microarray data; roughly 20 experiments were enough for AUROC >0.6, while capturing unknown functional information may require more than 50 experiments.
- RNA-seq and microarray aggregate networks had negatively correlated node degrees, and hub-like genes were significantly dissimilar between technologies (P < 1.4e-9), even though both captured individual GO functions similarly.
- Low-expressing transcripts that failed SEQC reliability thresholds in at least 80% of experiments had high node degree in microarrays but low node degree in RNA-seq, explaining much of the topological mismatch.
Methods
- The authors assembled 50 RNA-seq experiments totaling 1970 samples from SRA, Gemma, BrainSpan, and ReCount, plus 43 microarray experiments totaling 5134 arrays from GEO.
- Expression was estimated with Cufflinks, RSEQtools, and RSEM; co-expression networks used rank-transformed correlation edge weights, with comparisons to mutual information, ARACNE, CLR, MRNET, and WGCNA thresholding.
- Functional connectivity was assessed by guilt-by-association using neighbor voting and other machine-learning classifiers, with 3-fold cross-validation and AUROC against GO, KEGG, and Reactome annotations; aggregate networks were built by summing rank-standardized edges across datasets.
Limitations
- Stated: the authors note that their use of GO as the definition of function is the most easily critiqued aspect, although KEGG and Reactome gave nearly identical results; they also state that they cannot readily assess RNA-seq in its most novel applications, such as novel or more diverse transcript assessment.
- Stated: they do not claim to identify an ideal RNA-seq co-expression method, and they note that focused uses such as tissue-specific or developmental analyses may not be higher performing in their framework.
- Evident: the study is limited to human datasets and existing annotations, ignores alternate isoforms in aggregates and performance measures, and uses median values when multiple measures exist, so isoform-level or cross-species generalization is not established.
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In the paper
“Network topology was not fully robust to the methods used (Supplementary Fig. S1A), with the correlations of node degree between the networks created ranging between q ¼ À0.39 and q ¼ 0.88.”
§ Gene expression Guidance for RNA-seq co-expression network construction and analysis: safety in numbers > 3.1 RNA-seq expression estimates and co-expression measures affect network topology but not functional connectivity
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“Samples assayed by microarrays show a linear relationship between sample size and network performance (Fig. 3A, q ¼ 0.66)”
§ Gene expression Guidance for RNA-seq co-expression network construction and analysis: safety in numbers > 3.2 Many samples and greater read depth increases the functional connectivity of the networks
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In the paper
“A similar, albeit weaker trend is visible with increasing read depth (Supplementary Fig. S2C)”
§ Gene expression Guidance for RNA-seq co-expression network construction and analysis: safety in numbers > 3.2 Many samples and greater read depth increases the functional connectivity of the networks
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In the paper
“A notable point is that both RNA-seq and microarray networks appear to converge to similar performance measures (average AUROC $0.7).”
§ Gene expression Guidance for RNA-seq co-expression network construction and analysis: safety in numbers > 3.3 Aggregating across many networks improves performance and removes noise
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In the paper
“We observe a particularly strong role for number of samples and aggregation (Sections 3.2 and 3.3) in GBA assessments while mapping and machine learning methods (Sections 3.1 and 3.4) affect primarily topology and not GBA.”
§ Gene expression Guidance for RNA-seq co-expression network construction and analysis: safety in numbers > 1 Introduction
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In the paper
“Networks constructed using more samples were generally higher performing, and the trend is surprisingly consistent in strength between microarrays and RNA-seq (Fig. 3A and B).”
§ Gene expression Guidance for RNA-seq co-expression network construction and analysis: safety in numbers > 3.2 Many samples and greater read depth increases the functional connectivity of the networks
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In the paper
“While in theory it is possible that individual networks work well for different functions, this did not occur to any substantial extent.”
§ Gene expression Guidance for RNA-seq co-expression network construction and analysis: safety in numbers > 3.2 Many samples and greater read depth increases the functional connectivity of the networks
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In the paper
“Networks constructed using more samples were generally higher performing, and the trend is surprisingly consistent in strength between microarrays and RNA-seq (Fig. 3A and B).”
§ Gene expression Guidance for RNA-seq co-expression network construction and analysis: safety in numbers > 3.2 Many samples and greater read depth increases the functional connectivity of the networks
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In the paper
“Similarly, for any given experiment, the fraction of its GO terms which outperform the aggregate is very small, reaching a maximum of 17% only in very large experiments and averaging only 5% of GO terms (again, those with low performance, Fig. 5B).”
§ Gene expression Guidance for RNA-seq co-expression network construction and analysis: safety in numbers > 3.3 Aggregating across many networks improves performance and removes noise
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In the paper
“The top group had the highest performance (average AUROC ¼ 0.71), only just above the range of the random sampled”
§ Gene expression Guidance for RNA-seq co-expression network construction and analysis: safety in numbers > A B
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- It may state two separate claims as one. 33
- Probably not a substantial claim. 6
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In the paper
“Taking all the protein complex subgroups in GO in our analyses, their performance on average in the aggregate was AUROC $0.77, significantly greater than the remaining terms (Wilcoxon test, p $ 5.423eÀ7)”
§ Gene expression Guidance for RNA-seq co-expression network construction and analysis: safety in numbers > A B
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Similarly, for any given experiment, the fraction of its GO terms which outperform the aggregate is very small, reaching a maximum of 17% only in very large experiments and averaging only 5% of GO terms (again, those with low performance, Fig. 5B).”
§ Gene expression Guidance for RNA-seq co-expression network construction and analysis: safety in numbers > 3.3 Aggregating across many networks improves performance and removes noise
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“The MI network performs below the correlation coefficients, and closer to the networks generated including the negative correlation (average AUROC ¼ 0.56).”
§ Gene expression Guidance for RNA-seq co-expression network construction and analysis: safety in numbers > 3.1 RNA-seq expression estimates and co-expression measures affect network topology but not functional connectivity
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“## 3.3 Aggregating across many networks improves performance and removes noise”
§ Gene expression Guidance for RNA-seq co-expression network construction and analysis: safety in numbers > 3.3 Aggregating across many networks improves performance and removes noise
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In the paper
“Inset shows the correlation between the two GO AUROCs distributions plotted (q ¼ 0.84, P < 1eÀ16), showing that GO groups that perform well in one, perform equally well in the other network”
§ Gene expression Guidance for RNA-seq co-expression network construction and analysis: safety in numbers > A B
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“The average AUROC varied slightly between methods (Supplementary Table S4), with the neighbor voting, logistic regres- sion and stochastic gradient descent giving the highest performance (average AUROC ¼ 0.69), and the random forest performing the worst of the six methods (average AUROC ¼ 0.63).”
§ Gene expression Guidance for RNA-seq co-expression network construction and analysis: safety in numbers > 3.4 Machine learning methods do not confound network performance results
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- Rests on an assumption: The GO reference data adequately represents biological function for evaluating co-expression network performance.
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In the paper
“However, all the methods perform well on similar GO groups as neighbor voting (performances correlated at q ¼ 0.87-0.96, P < 2.2eÀ16)”
§ Gene expression Guidance for RNA-seq co-expression network construction and analysis: safety in numbers > 3.4 Machine learning methods do not confound network performance results
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- Rests on an assumption: The GO reference data adequately represents biological function for evaluating co-expression network performance.
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In the paper
“In the aggregate, almost half (47%) of the gene pairs exhibit high ranking (top 10%) co-expression, while a proportionately smaller fraction ($2%) exhibit very highly ranked (top 1%) co-expression.”
§ Gene expression Guidance for RNA-seq co-expression network construction and analysis: safety in numbers > A B
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- Rests on an assumption: The GO reference data adequately represents biological function for evaluating co-expression network performance.
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In the paper
“we verified by removing the connections ranked in the top 1% and seeing no significant drop in performance whereas removal of the connections in the top 10% had a large effect (AUROC $0.6).”
§ Gene expression Guidance for RNA-seq co-expression network construction and analysis: safety in numbers > A B
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- Rests on an assumption: The GO reference data adequately represents biological function for evaluating co-expression network performance.
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In the paper
“In co-expression analysis, increasing depth past 100 M aligned reads per sample and even thousands of samples continue to add value if our focus is on genome-wide functional inference for a given dataset.”
§ Gene expression Guidance for RNA-seq co-expression network construction and analysis: safety in numbers > 4 Discussion
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Rests on an assumption: The GO reference data adequately represents biological function for evaluating co-expression network performance.
- Rests on an assumption: The 50 RNA-seq and 30 microarray datasets are representative of their respective technologies.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“RNA-seq coexpression and microarray co-expression are very similar in their functional properties but very dissimilar in their overall topology (Fig. 6A).”
§ Gene expression Guidance for RNA-seq co-expression network construction and analysis: safety in numbers > 3.6 Noisy expression variation dominates topology outside of functional connectivity
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Rests on an assumption: The GO reference data adequately represents biological function for evaluating co-expression network performance.
- Rests on an assumption: The 50 RNA-seq and 30 microarray datasets are representative of their respective technologies.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“a simple rank-andsummate aggregation across multiple datasets generates a more robust network, capturing known biology with high efficacy.”
§ Gene expression Guidance for RNA-seq co-expression network construction and analysis: safety in numbers > 4 Discussion
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- Rests on an assumption: The GO reference data adequately represents biological function for evaluating co-expression network performance.
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In the paper
“Our study suggests that 1 M reads per sample (with many samples) is sufficient to build a network that is slightly divergent from random and adding such networks has no negative impact on the performance of the aggregate network.”
§ Gene expression Guidance for RNA-seq co-expression network construction and analysis: safety in numbers > 4 Discussion
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- Rests on an assumption: The 50 RNA-seq and 30 microarray datasets are representative of their respective technologies.
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In the paper
“simple, basic approaches were among the highest performing, from measuring network connectivity (Spearman correlation) to functional inference (neighbor voting).”
§ Gene expression Guidance for RNA-seq co-expression network construction and analysis: safety in numbers > 4 Discussion
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- Rests on an assumption: The GO reference data adequately represents biological function for evaluating co-expression network performance.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Here, again, performances are similar in microarray and RNA-seq (mean GO AUROCs of 0.64 and 0.65, respectively), but topology is starkly different with a correlation of q ¼ À0.66 between the node degrees of the two networks constructed.”
§ Gene expression Guidance for RNA-seq co-expression network construction and analysis: safety in numbers > 3.6 Noisy expression variation dominates topology outside of functional connectivity
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Rests on an assumption: The GO reference data adequately represents biological function for evaluating co-expression network performance.
- Rests on an assumption: The 50 RNA-seq and 30 microarray datasets are representative of their respective technologies.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In summary, the computationally simplest and most transparent method for calculating co-expression-simple correlation-was also the highest performing.”
§ Gene expression Guidance for RNA-seq co-expression network construction and analysis: safety in numbers > 3.1 RNA-seq expression estimates and co-expression measures affect network topology but not functional connectivity
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In the paper
“These include dependencies on library preparation, sequencing depth, gene lengths, GC content and transcript lengths, as well as other laboratory-specific practices (Hitzemann et al., 2013).”
§ Gene expression Guidance for RNA-seq co-expression network construction and analysis: safety in numbers > 1 Introduction
What the sources say
- Not checked yetcites Hitzemann et al., 2013 source
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In the paper
“It is characteristic of groups benefiting from aggregation in exhibiting moderate performance even before aggregation and, interestingly, in being a protein complex.”
§ Gene expression Guidance for RNA-seq co-expression network construction and analysis: safety in numbers > A B
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In the paper
“Altogether, our examination of the collagen complex supports the view that aggregation will work best in functions where pairwise relationships are expected to be distributed (e.g. protein complexes rather than dynamic pathways) and weak aggregate effects can summate to provide a strong total signal.”
§ Gene expression Guidance for RNA-seq co-expression network construction and analysis: safety in numbers > A B
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- Rests on an assumption: The GO reference data adequately represents biological function for evaluating co-expression network performance.
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In the paper
“Node degree is central to network topology and a major factor in numerous other properties (Gillis and Pavlidis, 2011b;Newman, 2003).”
§ Gene expression Guidance for RNA-seq co-expression network construction and analysis: safety in numbers > 1 Introduction
What the sources say
- Not checked yetcites Gillis and Pavlidis, 2011b
- Not checked yetcites Newman, 2003 source
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In the paper
“Even though this is lower than the random sets of experiments, it still outperforms almost all of the individual networks (equal to the best performing), indicating that even the weakest networks are useful in aggregate (Supplementary Fig. S5B).”
§ Gene expression Guidance for RNA-seq co-expression network construction and analysis: safety in numbers > A B
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In the paper
“The apparent performance dependencies suggest that to move from an AUROC ¼ 0.55-0.6 requires either thousands of samples, or millions of more sequenced reads, which is likely not feasible in a single experiment.”
§ Gene expression Guidance for RNA-seq co-expression network construction and analysis: safety in numbers > 3.2 Many samples and greater read depth increases the functional connectivity of the networks
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In the paper
“We also resampled across datasets to ensure the aggregated network exhibited robust properties.”
§ Gene expression Guidance for RNA-seq co-expression network construction and analysis: safety in numbers > 2.4 Co-expression networks and network aggregation
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In the paper
“In summary, RNA-seq offers unique virtues but is not a technical panacea.”
§ Gene expression Guidance for RNA-seq co-expression network construction and analysis: safety in numbers > 4 Discussion
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In the paper
“Users of co-expression data cannot naively apply those recommendations because the minimum number of samples is already relatively high, and robustly measuring correlations may require an even greater increase.”
§ Gene expression Guidance for RNA-seq co-expression network construction and analysis: safety in numbers > 4 Discussion
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Rests on an assumption: The 50 RNA-seq and 30 microarray datasets are representative of their respective technologies.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This indicates that the negative correlation between microarrays and RNA-seq data is a recurring feature of the individual networks, and not just the aggregates, and suggests a feature due to technological biases (in conjunction with network construction methods, Supplementary Fig. S5C).”
§ Gene expression Guidance for RNA-seq co-expression network construction and analysis: safety in numbers > 3.5 Microarray and RNA-seq-derived networks have different hub genes
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- Rests on an assumption: The 50 RNA-seq and 30 microarray datasets are representative of their respective technologies.
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In the paper
“This dissimilarity suggests again that while inference methods average away topological noise, the actual networks exhibit it to a substantial degree and this could be easily altered by varying methodology in other ways.”
§ Gene expression Guidance for RNA-seq co-expression network construction and analysis: safety in numbers > 3.5 Microarray and RNA-seq-derived networks have different hub genes
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- Rests on an assumption: The 50 RNA-seq and 30 microarray datasets are representative of their respective technologies.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“this may reflect our reliance on GO as a reference, even for narrowly defined functions.”
§ Gene expression Guidance for RNA-seq co-expression network construction and analysis: safety in numbers > 4 Discussion
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Rests on an assumption: The GO reference data adequately represents biological function for evaluating co-expression network performance.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Unsupervised interpretation of RNA-seq data connectivity is unlikely to be robust to underlying methodological choices without careful filtering, while methods that extract signals using training data (supervised or semi-supervised methods) appear to safely recover known information.”
§ Gene expression Guidance for RNA-seq co-expression network construction and analysis: safety in numbers > 4 Discussion
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Rests on an assumption: The GO reference data adequately represents biological function for evaluating co-expression network performance.
- Rests on an assumption: The 50 RNA-seq and 30 microarray datasets are representative of their respective technologies.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Further, the low impact of variation in node degree on semisupervised learning (e.g. through GBA) suggests that even without the appropriate filtering, many methods adequately identify the absence of learnable features in noisy expression.”
§ Gene expression Guidance for RNA-seq co-expression network construction and analysis: safety in numbers > 4 Discussion
Strength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Rests on an assumption: The GO reference data adequately represents biological function for evaluating co-expression network performance.
- Rests on an assumption: The 50 RNA-seq and 30 microarray datasets are representative of their respective technologies.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“the machine learning algorithm selected has little influence on the results, which also implies that the features being selected to make predictions, determine connectivity and assess the networks are adequate for the task at hand.”
§ Gene expression Guidance for RNA-seq co-expression network construction and analysis: safety in numbers > 4 Discussion
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Rests on an assumption: The GO reference data adequately represents biological function for evaluating co-expression network performance.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“So profound is the performance difference between aggregate and individual networks that it is difficult to recommend any minimum depth or sample count which is adequate; certainly none in our case come close to producing the same apparent performance.”
§ Gene expression Guidance for RNA-seq co-expression network construction and analysis: safety in numbers > 4 Discussion
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Rests on an assumption: The 50 RNA-seq and 30 microarray datasets are representative of their respective technologies.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Our analysis suggests that the reordering reflects the tendency for low-expressing genes to be correlated in microarray data, but that RNA-seq allows us to identify which genes are 'too' low to be trusted with much greater specificity.”
§ Gene expression Guidance for RNA-seq co-expression network construction and analysis: safety in numbers > 4 Discussion
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Rests on an assumption: The 50 RNA-seq and 30 microarray datasets are representative of their respective technologies.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“and in not generating spurious correlation among these noisy values (low node degree).”
§ Gene expression Guidance for RNA-seq co-expression network construction and analysis: safety in numbers > 3.6 Noisy expression variation dominates topology outside of functional connectivity
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Rests on an assumption: The 50 RNA-seq and 30 microarray datasets are representative of their respective technologies.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Co-expression analyses will still require many samples and a reliable quality reference network as a control and comparative measure.”
§ Gene expression Guidance for RNA-seq co-expression network construction and analysis: safety in numbers > 4 Discussion
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence. It may state two separate claims as one.
- Rests on an assumption: The GO reference data adequately represents biological function for evaluating co-expression network performance.
- Rests on an assumption: The 50 RNA-seq and 30 microarray datasets are representative of their respective technologies.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Because of our reliance on pre-existing knowledge (GO) or data (microarray) to provide reference knowledge, we cannot readily assess RNA-seq where its application is likely to prove most novel and exciting, e.g.novel or more diverse transcript assessment.”
§ Gene expression Guidance for RNA-seq co-expression network construction and analysis: safety in numbers > 4 Discussion
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
- Rests on an assumption: The GO reference data adequately represents biological function for evaluating co-expression network performance.
- Rests on an assumption: The 50 RNA-seq and 30 microarray datasets are representative of their respective technologies.
The paper relies on this without support.
RNA-seq co-expression analysis is in its infancy and reasonable practices remain poorly defined.
“RNA-seq co-expression analysis is in its infancy and reasonable practices remain poorly defined.”
The paper relies on this without support.
The GO, KEGG, and Reactome annotations used as labels accurately reflect gene function and are sufficiently complete for evaluating functional connectivity.
“By using known annotations as labels for the genes (here we used GO, KEGG and Reactome labeled gene sets), one can determine how well the network recapitulates this information”
The paper relies on this without support.
The 10 RNA-seq experiments selected for method comparisons are representative of the broader corpus.
“We used a sample of 10 representative experiments, comparing the estimation expressions of Cufflinks, RSEQtools and RSEM and constructed co-expression networks using our default procedures”
The paper relies on this without support.
The aggregate network's performance on GO terms is a valid proxy for capturing unknown functional information.
“to comprehensively capture unknown functional information may require >50 separate experiments.”
The paper relies on this without support.
The RNA-seq and microarray datasets used are comparable enough that observed differences in network topology and sample-size dependency reflect technology rather than dataset composition.
“In a similar fashion, we generated an aggregate of the microarrays on a random sample of 30 datasets using 3320 individual microarrays.”
The paper relies on this without support.
GO slim's 109 terms are broadly representative of the ontology overall.
“For our machine learning analysis, we used GO slim, a version of GO that filters analyses to 109 GO terms, broadly representative of the ontology overall.”
The paper relies on this without support.
The GO reference data adequately represents biological function for evaluating co-expression network performance.
“our use of the GO as a way of defining 'function' is perhaps the most easily critiqued, albeit hard to avoid and common.”
The paper relies on this without support.
The 50 RNA-seq and 30 microarray datasets are representative of their respective technologies.
“We used the 30 networks from the microarray aggregate, and the 50 networks of the RNA-seq aggregates”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“While differential co-expression methods are sensitive to noise [21], they are becoming more effective with the increase in RNA-seq data quantity and quality.”
From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [21]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Co-expression networks constructed using this cut-off have been suggested to have a similar quality to microarray-based co-expression networks if constructed from the same number of samples [21], but decreasing in quality with fewer reads.”
From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [21]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“A higher total read depth for RNA-seq samples increases the accuracy of the expression measurements, especially for genes with low expression [21, 59].”
From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [21]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“To create co-expression networks from RNA-seq data, a 20-sample minimum has been suggested [21, 54], and increased sample sizes produce networks with a higher functional connectivity [21, 59].”
From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [21]
The cited source says what the claim attributes to it.
In the citing paper
“To create co-expression networks from RNA-seq data, a 20-sample minimum has been suggested [21, 54], and increased sample sizes produce networks with a higher functional connectivity [21, 59].”
From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [21]
The cited source says what the claim attributes to it.
In the citing paper
“Not surprisingly, higher quality data tend to result in more accurate co-expression networks [21, 59].”
From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [21]
In the citing paper
“At currently available sample sizes, networks are most reproducible at a module level35,38,39, then at the hub gene level41,223 and, last, at the level of precise gene connectivity rankings or precise module memberships of genes40,49.”
From Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders (2015) · cites this paper as [40]
In the citing paper
“To obtain module-level reproducibility, 20 independent samples are usually sufficient40, but systematic and accurate reconstruction of specific edges, particularly for systematic regulatory relationship discovery, may require hundreds of samples49.”
From Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders (2015) · cites this paper as [40]
In the citing paper
“For DGE and network analysis, there are many studies with guidelines that are based on theoretical models and empirical assessments40,173–176, but most studies use data from experiments that do not have the spatial, temporal or disease-relevant complexities that occur in studies of the CNS or post-mortem tissue.”
From Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders (2015) · cites this paper as [40]
In the citing paper
“To obtain module-level reproducibility, 20 independent samples are typically considered adequate; however, systematic consistency at the more granular hub and module membership levels may require hundreds of samples [61, 93, 94].”
From Systems-based proteomics to resolve the biology of Alzheimer’s disease beyond amyloid and tau (2021) · cites this paper as [93]
In the citing paper
“To obtain module-level reproducibility, 20 independent samples are typically considered adequate; however, systematic consistency at the more granular hub and module membership levels may require hundreds of samples [61, 93, 94].”
From Systems-based proteomics to resolve the biology of Alzheimer’s disease beyond amyloid and tau (2021) · cites this paper as [93]
In the citing paper
“Network analyses of AD brain tissue demonstrate the most reproducibility at the module level, followed by the hub protein level, and then finally at the level of precise protein connectivity rankings and module protein membership [61, 89–94].”
From Systems-based proteomics to resolve the biology of Alzheimer’s disease beyond amyloid and tau (2021) · cites this paper as [93]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Conflicting minimum read-depth guidance
MajorThe abstract gives more than 10 million reads per sample as a minimum for performance comparable to microarrays. The results section says that even modest performance requires close to 100 million reads per sample, a substantially higher threshold.
“Minimal experimental criteria to obtain performance on par with microarrays were >20 samples with read depth >10 M per sample.”
“suggests using RNA-seq co-expression to obtain even modest performance (AUROC >0.55) requires sample sizes >20 and read depth per sam- ple close to 100 M reads.”
Machine-learning method dependence is described in opposite ways
MinorThe results say the findings are not strongly independent of method, whereas the discussion says the selected algorithm has little influence on them.
“implying that the results are not strongly method independent.”
“the machine learning algorithm selected has little influence on the results”
Figure 4 correlation is labeled as a P-value in the text
MinorThe text assigns P = 0.84 to the similarity shown in Figure 4B, but the figure caption identifies 0.84 as the correlation and gives its P-value as less than 1e−16.
“Even so, both networks capture individual GO functions in a similar way (P ¼ 0.84, Fig. 4B and inset).”
“Inset shows the correlation between the two GO AUROCs distributions plotted (q ¼ 0.84, P < 1eÀ16)”
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Cited by
Sorted by citations
Gene co-expression analysis for functional classification and gene–disease predictions
van Dam, S., Võsa, U., van der Graaf, A., Franke, L., & de Magalhães, J. P. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbw139 · Cited by 995
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Self-citationCEMiTool: a Bioconductor package for performing comprehensive modular co-expression analyses
Russo, P. S. T., Ferreira, G. R., Cardozo, L. E., Bürger, M. C., Arias‐Carrasco, R., Maruyama, S. R., Hirata, T. D. C., de Lima, D. S., Passos, F. M., Fukutani, K. F., et al. (2018) · BMC Bioinformatics · doi:10.1186/s12859-018-2053-1 · Cited by 313
“around 20 samples (which is in accordance to previous findings [26]), indicating that the β x R(2) curve, and thus”
ResultLearning from Co-expression Networks: Possibilities and Challenges
Serin, E. A. R., Nijveen, H., Hilhorst, H. W. M., & Ligterink, W. (2016) · Frontiers in Plant Science · doi:10.3389/fpls.2016.00444 · Cited by 311
“RNA-seq based co-expression network construction is still in its infancy (Iancu et al., 2012; Ballouz et al., 2015) but the foreseen predominance”
BackgroundMethodThe United Kingdom Brain Expression Consortium, Botía, J. A., Vandrovcova, J., Forabosco, P., Guelfi, S., D’Sa, K., Hardy, J., Lewis, C. M., Ryten, M., & Weale, M. E. (2017) · BMC Systems Biology · doi:10.1186/s12918-017-0420-6 · Cited by 289
Advanced Applications of RNA Sequencing and Challenges
Han, Y., Gao, S., Muegge, K., Zhang, W., & Zhou, B. (2015) · Bioinformatics and Biology Insights · doi:10.4137/bbi.s28991 · Cited by 303
Wisecaver, J. H., Borowsky, A., Tzin, V., Jander, G., Kliebenstein, D. J., & Rokas, A. (2017) · The Plant Cell · doi:10.1105/tpc.17.00009 · Cited by 248
“…global coexpression networks that we have constructed accurately capture the spatial and temporal regulation of BGCs in response to the diverse ecological conditions plants experience, which is at least partially dependent on the number and types of the conditions sampled (Ballouz et al., 2015).”
BackgroundObayashi, T., Aoki, Y., Tadaka, S., Kagaya, Y., & Kinoshita, K. (2017) · Plant and Cell Physiology · doi:10.1093/pcp/pcx191 · Cited by 250
“The function scores were correlated with the logarithmic number of samples for each species (PCC = 0.44), as reported previously (Ballouz et al. 2015).”
MethodInfluentialCo-expression networks reveal the tissue-specific regulation of transcription and splicingPreprint
Saha, A., Kim, Y., Gewirtz, A. D., Jo, B., Gao, C., McDowell, I. C., Engelhardt, B. E., & Battle, A. J. (2017) · Genome Research · doi:10.1101/gr.216721.116 · Cited by 173
ATTED-II in 2016: A Plant Coexpression Database Towards Lineage-Specific Coexpression
Aoki, Y., Okamura, Y., Tadaka, S., Kinoshita, K., & Obayashi, T. (2015) · Plant and Cell Physiology · doi:10.1093/pcp/pcv165 · Cited by 167
“The quality of coexpression data is primarily based on the number of samples (Ballouz et al. 2015), and this characteristic limits application of coexpression analysis in non-model species.”
Evaluating measures of association for single-cell transcriptomics
Skinnider, M. A., Squair, J. W., & Foster, L. J. (2019) · Nature Methods · doi:10.1038/s41592-019-0372-4 · Cited by 145
Obayashi, T., Kagaya, Y., Aoki, Y., Tadaka, S., & Kinoshita, K. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky1155 · Cited by 137
“Importantly, the quality of coexpression data strongly depends on the sample size (1,2).”
BackgroundPredictability of human differential gene expression
Crow, M., Lim, N., Ballouz, S., Pavlidis, P., & Gillis, J. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1802973116 · Cited by 117
“We first asked whether they formed coexpressed gene modules in a high-quality coexpression network (27) (Fig.”
BackgroundMethodSelf-citationSun, Y.-C., Chen, X., Fischer, S., Lu, S., Zhan, H., Gillis, J., & Zador, A. M. (2021) · Nature Neuroscience · doi:10.1038/s41593-021-00842-4 · Cited by 124
Self-citationCorrelation AnalyzeR: functional predictions from gene co-expression correlations
Miller, H. E., & Bishop, A. J. R. (2021) · BMC Bioinformatics · doi:10.1186/s12859-021-04130-7 · Cited by 122
“Tissue-disease groups with fewer than 30 distinct samples were removed to limit the effects of bias from individual samples and improve the performance of co-expression calculations [17].”
BackgroundMethodObayashi, T., Hibara, H., Kagaya, Y., Aoki, Y., & Kinoshita, K. (2022) · Plant and Cell Physiology · doi:10.1093/pcp/pcac041 · Cited by 127
Systems-based proteomics to resolve the biology of Alzheimer’s disease beyond amyloid and tau
Rayaprolu, S., Higginbotham, L., Bagchi, P., Watson, C. M., Zhang, T., Levey, A. I., Rangaraju, S., & Seyfried, N. T. (2021) · Neuropsychopharmacology · doi:10.1038/s41386-020-00840-3 · Cited by 118
Johnson, K. A., & Krishnan, A. (2022) · Genome biology · doi:10.1186/s13059-021-02568-9 · Cited by 111
“Supporting this hypothesis, other studies have noted an association between larger sample size and more accurate coexpression networks [18, 27] and subsequent network transformation with CLR [31].”
BackgroundResultFu, C., Zhang, X., Veri, A. O., Iyer, K. R., Lash, E., Xue, A., Yan, H., Revie, N. M., Wong, C. J., Lin, Z., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-26850-3 · Cited by 90
“02 rev.0) against the C . albicans RefSeq database (version 68), as previously described 90,91 .”
MethodIntegrating gene regulatory pathways into differential network analysis of gene expression data
Grimes, T., Potter, S. S., & Datta, S. (2019) · Scientific Reports · doi:10.1038/s41598-019-41918-3 · Cited by 77
“This is often referred to as the guilt-by-association principle, and it provides support for the use of gene expression data in reconstructing the underlying gene-gene association network 3,4 .”
MethodShahan, R., Zawora, C., Wight, H., Sittmann, J., Wang, W., Mount, S. M., & Liu, Z. (2018) · PLANT PHYSIOLOGY · doi:10.1104/pp.18.00086 · Cited by 76
“Our use of multiple, comprehensive RNA-seq libraries provides extensive gene expression information for network construction and therefore increases the likelihood of identifying genetic correlations (Lee et al., 2004; Wren, 2009; Ballouz et al., 2015).”
MethodstandR: spatial transcriptomic analysis for GeoMx DSP data
Liu, N., Bhuva, D. D., Mohamed, A., Bokelund, M., Kulasinghe, A., Tan, C. W., & Davis, M. J. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad1026 · Cited by 76
Meta Gene Regulatory Networks in Maize Highlight Functionally Relevant Regulatory Interactions
Zhou, P., Li, Z., Ellison, E. L., Gómez-Cano, F., Crisp, P. A., Noshay, J. M., Grotewold, E., Hirsch, C. N., Briggs, S. P., & Springer, N. M. (2020) · The Plant Cell · doi:10.1105/tpc.20.00080 · Cited by 70
“Increasing sample size was reported to generally have a positive effect on network performance (Ballouz et al., 2015; Huang et al., 2017), consistent with our comparison of tissue-specific or genotype-specific (i.e., developmental atlas) networks of different sizes.”
ResultMulti‐omics network‐based functional annotation of unknown Arabidopsis genes
Depuydt, T., & Vandepoele, K. (2021) · The Plant Journal · doi:10.1111/tpj.15507 · Cited by 75
(Differential) Co-Expression Analysis of Gene Expression: A Survey of Best Practices
Chowdhury, H. A., Bhattacharyya, D. K., & Kalita, J. K. (2019) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2019.2893170 · Cited by 78
“b) More than 10 million reads per sample are ideal for co-expression analysis [45].”
BackgroundMethodInfluentialLiesecke, F., Daudu, D., de Bernonville, R. D., Besseau, S., Clastre, M., Courdavault, V., Craene, J. D., Crèche, J., Giglioli‐Guivarc’h, N., Glévarec, G., et al. (2018) · Scientific Reports · doi:10.1038/s41598-018-29077-3 · Cited by 70
Big data: Historic advances and emerging trends in biomedical research
Cremin, C. J., Dash, S., & Huang, X. (2022) · Current Research in Biotechnology · doi:10.1016/j.crbiot.2022.02.004 · Cited by 91
Kim, M., Haney, J. R., Zhang, P., Hernandez, L. M., Wang, L.-K., Pérez‐Cano, L., Loohuis, L. M. O., de la Torre-Ubieta, L., & Gandal, M. J. (2021) · Nature Neuroscience · doi:10.1038/s41593-021-00847-z · Cited by 76
“Even after stratifying samples by imputed C4A copy number, this sample size was sufficient 82,83 to detect significant gene co-expression, as we observed.”
Exploiting single-cell expression to characterize co-expression replicability
Crow, M., Paul, A., Ballouz, S., Huang, Z. J., & Gillis, J. (2016) · Genome Biology · doi:10.1186/s13059-016-0964-6 · Cited by 76
“Our lab has done extensive testing and benchmarking of this algorithm against both more sophisticated machine learning methods, and using varying methods for network generation (e.g. partial correlation, mutual information, etc.), and found that results were robust [21, 26].”
BackgroundMethodInfluentialSelf-citationB chromosomes: from cytogenetics to systems biology
Valente, G. T., Nakajima, R. T., Fantinatti, B. E. A., Marques, D. F., Almeida, R. O., Simões, R. P., & Martins, C. (2016) · Chromosoma · doi:10.1007/s00412-016-0613-6 · Cited by 67
“The inference of coexpression networks (Spearman’s rank correlation) (Ballouz et al. 2015) using RNA-Seq from the B+ and B − genotypes of A.”
MethodOvens, K. L., Eames, B. F., & McQuillan, I. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.695399 · Cited by 68
“Another possible limitation is the sample size of the dataset used to construct a network (Ballouz et al., 2015; Ovens et al., 2020) and finding multi-species studies to make evolutionary inferences.”
BackgroundMethodUtility and Limitations of Using Gene Expression Data to Identify Functional Associations
Uygun, S., Peng, C., Lehti‐Shiu, M. D., Last, R. L., & Shiu, S. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005244 · Cited by 61
Transcriptomic Insight Into the Polygenic Mechanisms Underlying Psychiatric Disorders
Hernandez, L. M., Kim, M., Hoftman, G. D., Haney, J. R., de la Torre-Ubieta, L., Paşaniuc, B., & Gandal, M. J. (2020) · Biological Psychiatry · doi:10.1016/j.biopsych.2020.06.005 · Cited by 59
“gene expression variance explained by each covariate was calculated using a linear mixed effects model using the variancePartition package (28).”
MethodConstruction and Optimization of a Large Gene Coexpression Network in Maize Using RNA-Seq Data
Huang, J., Vendramin, S., Shi, L., & McGinnis, K. M. (2017) · PLANT PHYSIOLOGY · doi:10.1104/pp.17.00825 · Cited by 51
“…Pearson correlation coef fi cient (PCC), Spearman ’ s correlation coef fi cient (SCC), Kendall rank correlation co-ef fi cient (KCC), Gini correlation coef fi cient (GCC), and biweight midcorrelation (BIC; Langfelder and Horvath, 2008; Kumari et al., 2012; Ma and Wang, 2012; Ballouz et al., 2015).”
BackgroundMethodResultInfluentialMistry, D., Wise, R. P., & Dickerson, J. A. (2017) · PLoS ONE · doi:10.1371/journal.pone.0187091 · Cited by 51
Liesecke, F., Craene, J. D., Besseau, S., Courdavault, V., Clastre, M., Vergès, V., Papon, N., Giglioli‐Guivarc’h, N., Glévarec, G., Pichon, O., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-50885-8 · Cited by 44
CoCoCoNet: conserved and comparative co-expression across a diverse set of species
Lee, J., Shah, M., Ballouz, S., Crow, M., & Gillis, J. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa348 · Cited by 39
Self-citationMachine Learning-Based State-of-the-Art Methods for the Classification of RNA-Seq Data
Jabeen, A., Ahmad, N., & Raza, K. (2017) · Lecture notes in computational vision and biomechanics · doi:10.1007/978-3-319-65981-7_6 · Cited by 58
Systems Biology Approaches for Host–Fungal Interactions: An Expanding Multi-Omics Frontier
Culibrk, L., Croft, C. A., & Tebbutt, S. J. (2016) · OMICS A Journal of Integrative Biology · doi:10.1089/omi.2015.0185 · Cited by 52
“Despite this, current methods are still unable to provide a definitive model of gene regulatory networks (Ballouz et al., 2015).”
MethodLSTrAP: efficiently combining RNA sequencing data into co-expression networks
Proost, S., Krawczyk, A., & Mutwil, M. (2017) · BMC Bioinformatics · doi:10.1186/s12859-017-1861-z · Cited by 37
“While the bare minimum was estimated to be >20 samples with >10 million reads, more samples and higher read depth were found to increase the predictive capabilities of the resulting network [14].”
BackgroundMethodCoexpression reveals conserved gene programs that co-vary with cell type across kingdoms
Crow, M., Suresh, H., Lee, J., & Gillis, J. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac276 · Cited by 34
Self-citationPark, Y., Heider, D., & Hauschild, A.-C. (2021) · Cancers · doi:10.3390/cancers13133148 · Cited by 46
“compared microarray and NGS-based co-expression networks and found the existence of a bias originating from batch effects between the two technologies [80].”
BackgroundDeane, C. S., Willis, C. R. G., Phillips, B. E., Atherton, P. J., Harries, L. W., Ames, R. M., Szewczyk, N. J., & Etheridge, T. (2021) · Journal of Cachexia Sarcopenia and Muscle · doi:10.1002/jcsm.12706 · Cited by 42
Liang, Y., & Kelemen, A. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbx036 · Cited by 37
Hin, N., Newman, M., Kaslin, J., Douek, A. M., Lumsden, A. L., Nik, S. H. M., Dong, Y., Zhou, X., Manucat-Tan, N. B., Ludington, A. J., et al. (2020) · PLoS ONE · doi:10.1371/journal.pone.0227258 · Cited by 40
“Although revealing valuable insights, our comparison of the gene co-expression patterns in the zebrafish and human datasets is limited by inherent differences in species-level gene expression, differences in the brain regions and tissues sampled in each dataset, and difference in the RNA-seq platforms used to collect…”
BackgroundMethodYlla, G., Piulachs, M., & Bellés, X. (2017) · BMC Genomics · doi:10.1186/s12864-017-4177-5 · Cited by 38
Genomic data integration systematically biases interactome mapping
Skinnider, M. A., Stacey, R. G., & Foster, L. J. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006474 · Cited by 34
“We began by analyzing the functional connectivity of each network, defined as the degree to which the function of any given protein can be predicted from those of its interacting partners, based on the principle of ‘guilt by association’ [21,22].”
MethodEmamjomeh, A., Robat, E. S., Zahiri, J., Solouki, M., & Khosravi, P. (2017) · Plant Biotechnology Reports · doi:10.1007/s11816-017-0433-z · Cited by 37
“Algorithms based on RNA-Seq Although DNA microarray has been the main platform for expression data, recently, RNA-Seq has played an important role in quantitative analysis of gene expression and detection of transcripts (Richard et al. 2010; Giorgi et al. 2013; Ballouz et al. 2015).”
MethodCo-expression of calcium and hERG potassium channels reduces the incidence of proarrhythmic events
Ballouz, S., Mangala, M. M., Perry, M., Heitmann, S., Gillis, J., Hill, A. P., & Vandenberg, J. I. (2020) · Cardiovascular Research · doi:10.1093/cvr/cvaa280 · Cited by 31
Self-citationStem cells and heart disease - Brake or accelerator?
Steinhoff, G., Nesteruk, J., Wolfien, M., Große, J., Ruch, U., Vasudevan, P., & Müller, P. (2017) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2017.10.007 · Cited by 37
CoPhosK: A method for comprehensive kinase substrate annotation using co-phosphorylation analysis
Ayati, M., Wiredja, D. D., Schlatzer, D., Maxwell, S., Li, M., Koyutürk, M., & Chance, M. R. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006678 · Cited by 29
Yang, S., Marín‐Juez, R., Meijer, A. H., & Spaink, H. P. (2015) · BMC Genomics · doi:10.1186/s12864-015-1740-9 · Cited by 33
Obayashi, T., Kodate, S., Hibara, H., Kagaya, Y., & Kinoshita, K. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac983 · Cited by 31
Aono, A. H., Pimenta, R. J. G., Garcia, A. L. B., Correr, F. H., Hosaka, G. K., Carrasco, M. M., Cardoso-Silva, C. B., Mancini, M. C., Sforça, D. A., Santos, L. B. D., et al. (2021) · Frontiers in Plant Science · doi:10.3389/fpls.2021.668623 · Cited by 30
“Pearson correlation coefficients were used because of their reasonable performance in RNA-Seq datasets (Ballouz et al., 2015).”
MethodYang, L., Su, Z., Wang, Z., Li, Z., Shang, Z., Du, H., Liu, G., Qi, D., Yang, Z., Xu, Z., et al. (2021) · Cell Death and Disease · doi:10.1038/s41419-021-03552-8 · Cited by 32
Meng, G., & Mei, H. (2019) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2019.00101 · Cited by 28
(2017) · doi:10.1104/pp.16.01982 · Cited by 29
“The pathogen is soil borne and remains in infected roots, where it produces toxins that cause the foliar SDS (Brar et al., 2011; Brar and Bhattacharyya, 2012; Pudake et al., 2013; Chang et al., 2016).”
BackgroundNetwork aggregation improves gene function prediction of grapevine gene co-expression networks
Wong, D. C. J. (2020) · Plant Molecular Biology · doi:10.1007/s11103-020-01001-2 · Cited by 28
Brain transcriptome atlases: a computational perspective
Mahfouz, A. E., Huisman, S. M. H., Lelieveldt, B. P. F., & Reinders, M. J. T. (2016) · Brain Structure and Function · doi:10.1007/s00429-016-1338-2 · Cited by 27
“There are numerous technical considerations to considere while constructing co-expression networks that go beyond the scope of this review (Allen et al. 2012; Ballouz et al. 2015).”
BackgroundCellMapper: rapid and accurate inference of gene expression in difficult-to-isolate cell types
Nelms, B. D., Waldron, L., Barrera, L. A., Weflen, A. W., Goettel, J. A., Guo, G., Montgomery, R. K., Neutra, M. R., Breault, D. T., Snapper, S. B., et al. (2016) · Genome biology · doi:10.1186/s13059-016-1062-5 · Cited by 24
“Many algorithms that explore gene co-expression relationships have translated well to RNA sequencing (RNA-Seq) data [46], and CellMapper in principle could be adapted for RNA-Seq to allow for more complete coverage of the transcriptome.”
BackgroundSun, H., Zhao, K., Zhou, M., Chen, Y., & Guan, L. L. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty883 · Cited by 26
DeORFanizing Candida albicans Genes using Coexpression
O’Meara, T. R., & O’Meara, M. J. (2021) · mSphere · doi:10.1128/msphere.01245-20 · Cited by 20
“Genome scale coexpression networks built from bulk RNAseq studies have been used extensively for gene function prediction using guilt by association (1, 2, 48).”
Construction, comparison and evolution of networks in life sciences and other disciplines
Gysi, D. M., & Nowick, K. (2020) · Journal of The Royal Society Interface · doi:10.1098/rsif.2019.0610 · Cited by 25
“They are best built from genome-wide expression data measured by RNA-Seq [124] or microarrays [125,126].”
BackgroundDifferential Regulatory Analysis Based on Coexpression Network in Cancer Research
Li, J., Li, Y., & Li, Y. (2016) · BioMed Research International · doi:10.1155/2016/4241293 · Cited by 24
“suggest minimal experimental criteria to obtain useful functional connectivity and topology information of coexpression with microarrays greater than 20 samples and read depth greater than 10M per sample [64].”
BackgroundPhytoNet: comparative co-expression network analyses across phytoplankton and land plants
Ferrari, C., Proost, S., Ruprecht, C., & Mutwil, M. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky298 · Cited by 22
Qian, Y.-Q., Zhou, Z.-W., Ouyang, T., Li, D., Li, R., Gan, P., Qiao, R., Tan, Y. L., Qian, M., Liu, L., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-62887-4 · Cited by 28
“Four tools were used to construct gene and TE co-expression networks: WGCNA 125 , Pearson correlation coef fi cient (PCC) 126 – 128 , CORNET 129 , and TEffectR 130 .”
InfluentialNoise regularization removes correlation artifacts in single-cell RNA-seq data preprocessing
Zhang, R., Atwal, G. S., & Lim, W. K. (2021) · Patterns · doi:10.1016/j.patter.2021.100211 · Cited by 21
Pang, C. N. I., Ballouz, S., Weissberger, D., Thibaut, L. M., Hamey, J. J., Gillis, J., Wilkins, M. R., & Hart‐Smith, G. (2020) · Molecular & Cellular Proteomics · doi:10.1074/mcp.ra120.002154 · Cited by 19
“For merged networks, gene co-expression networks for yeast (47) or human (48), or high confidence PPI networks for yeast (44) or human (45), were merged with CF-MS networks.”
BackgroundMethodSelf-citationKosvyra, A., Maramis, C., & Chouvarda, I. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.103971 · Cited by 15
Breen, M. S., Stein, D. J., & Baldwin, D. S. (2016) · Human Psychopharmacology Clinical and Experimental · doi:10.1002/hup.2546 · Cited by 20
“To obtain gene module reproducibility under a case–control design, it is recommended to construct networks using a minimum of 20 independent samples per condition and to perform permutation analyses on modules of interest to provide in silico support of coexpression (Ballouz et al., 2015).”
MethodVandenbon, A. (2022) · PLoS ONE · doi:10.1371/journal.pone.0263344 · Cited by 20
“High numbers of samples are needed to accurately infer correlation of expression [11].”
BackgroundResultInfluentialKober, K. M., Schumacher, M. A., Conley, Y. P., Topp, K. S., Mazor, M. B., Hammer, M. J., Paul, S. M., Levine, J. D., & Miaskowski, C. A. (2019) · Molecular Pain · doi:10.1177/1744806919878088 · Cited by 19
“Although the performance of co-expression network analysis is dependent of sample size, WGCNA performs well on sample sizes of >20.(52,53) Multiple logistic regression analysis, which controlled for significant demographic (i.”
MethodSystems biology and gene networks in Alzheimer’s disease
Wang, Z.-T., Tan, C., Tan, L., & Yu, J. (2018) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2018.11.007 · Cited by 20
“network, and the use of statistical modeling and multiple corrections (Ballouz et al., 2015; Contreras-Lopez et al., 2018; Gaiteri et al., 2014; Liu et al., 2014; Rapaport et al., 2013; Robles et al., 2012; Song et al., 2012).”
Fortelny, N., Butler, G. S., Overall, C. M., & Pavlidis, P. (2017) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m116.065706 · Cited by 19
“Finally, with the aim of deriving a more robust coexpression result, we performed a meta-analysis (34, 35) of gene expression over multiple microarray datasets (listed in supplemental Table S6 and shown in Fig.”
O’Meara, M. J., Ballouz, S., Shoichet, B. K., & Gillis, J. (2016) · PLoS ONE · doi:10.1371/journal.pone.0160098 · Cited by 15
“This improvement is particularly striking as the performance of the CoExp network has diminishing returns as sample size grows [29] (Fig 4B), suggesting that no reasonable amount of new expression data could compensate for the medicinal chemistry data captured by SEA.”
BackgroundSelf-citationPiya, S., Hawk, T. E., Patel, B., Baldwin, L., Rice, J. H., Stewart, C. N., & Hewezi, T. (2021) · Molecular Plant Pathology · doi:10.1111/mpp.13168 · Cited by 16
Li, Z., Lou, Y., Tian, G., Wu, J., Lu, A., Chen, J., Xu, B., Shi, J., & Yang, J. (2019) · Aging · doi:10.18632/aging.102579 · Cited by 18
“Streamlining the analysis of multiple datasets using the same computational processes enhances statistical power and may significantly increase the accuracy of the findings [41, 42].”
MethodGene communities in co-expression networks across different tissues
Russell, M., Aqil, A., Saitou, M., Gökçümen, Ö., & Masuda, N. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011616 · Cited by 18
“Systematically investigating biological performance of network community detection as a function of the number of samples [135, 137, 138] will help us to better understand potentials and limitations of both single-layer and multilayer community detection in gene and other related networks, which is…”
PlantNexus: A Gene Co-expression Network Database and Visualization Tool for Barley and Sorghum
Zhou, Y., Sukul, A., Mishler‐Elmore, J. W., Faik, A., & Held, M. (2022) · Plant and Cell Physiology · doi:10.1093/pcp/pcac007 · Cited by 16
GCEN: An Easy-to-Use Toolkit for Gene Co-Expression Network Analysis and lncRNAs Annotation
Chen, W., Li, J., Huang, S., Li, X., Zhang, X., Hu, X., Xiang, S., & Liu, C. (2022) · Current Issues in Molecular Biology · doi:10.3390/cimb44040100 · Cited by 16
“figured out that network aggregation shows better performance [11,27].”
BackgroundMethodInfluentialMachine Learning-based state-of-the-art methods for the classification of RNA-Seq data
(2017) · doi:10.1101/120592 · Cited by 18
“K.”
Mutanda, I., Inafuku, M., Saitoh, S., Iwasaki, H., Fukuta, M., Watanabe, K., & Oku, H. (2016) · Plant Cell & Environment · doi:10.1111/pce.12797 · Cited by 18
Guo, W., Calixto, C. P. G., Τzioutziou, N. A., Lin, P., Waugh, R., Brown, J. W. S., & Zhang, R. (2017) · BMC Systems Biology · doi:10.1186/s12918-017-0440-2 · Cited by 18
Co-expression networks reveal the tissue-specific regulation of transcription and splicingPreprint
Saha, A., Kim, Y., Gewirtz, A. D., Jo, B., Gao, C., McDowell, I. C., Engelhardt, B. E., & Battle, A. J. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/078741 · Cited by 17
“Many regulatory relationships are shared between tissues, and assessing hubs across all tissues jointly may improve robustness (Ballouz et al. 2015).”
Addressing noise in co-expression network construction
Burns, J., Shealy, B. T., Greer, M. S., Hadish, J. A., McGowan, M., Biggs, T. D., Smith, M. C., Feltus, F. A., & Ficklin, S. P. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab495 · Cited by 18
Abbassi‐Daloii, T., Kan, H. E., Raz, V., & Hoen, P. A. C. ’. (2020) · Genomics · doi:10.1016/j.ygeno.2020.05.026 · Cited by 14
The transcriptional legacy of developmental stochasticity
(2019) · doi:10.1101/2019.12.11.873265 · Cited by 16
“For each quadruplet at each time point, we constructed a co-expression network(27).”
MethodSelf-citationEmad, A., & Sinha, S. (2018) · npj Systems Biology and Applications · doi:10.1038/s41540-021-00169-7 · Cited by 15
Ebner, F., Kuhring, M., Radonić, A., Midha, A., Renard, B. Y., & Hartmann, S. (2018) · Frontiers in Immunology · doi:10.3389/fimmu.2018.01868 · Cited by 14
“We applied established and efficient procedures to infer and analyze GENs including pairwise correlation, cluster (module) analysis, and functional enrichment (95, 96).”
MethodOmics approaches to study gene regulatory networks for development in echinoderms
Lowe, E. K., Cuomo, C., & Arnone, M. I. (2017) · Briefings in Functional Genomics · doi:10.1093/bfgp/elx012 · Cited by 15
Metabolic engineering to increase crop yield: From concept to execution
Skraly, F. A., Ambavaram, M. M. R., Peoples, O. P., & Snell, K. D. (2018) · Plant Science · doi:10.1016/j.plantsci.2018.03.011 · Cited by 15
Positive and negative forms of replicability in gene network analysis
Verleyen, W., Ballouz, S., & Gillis, J. A. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv734 · Cited by 12
Self-citationCan dynamic network modelling be used to identify adaptive microbiomes?
Garcia, J., & Kao‐Kniffin, J. (2019) · Functional Ecology · doi:10.1111/1365-2435.13491 · Cited by 13
Gene Differential Co-Expression Networks Based on RNA-Seq: Construction and Its Applications
Wang, P., & Wang, D. (2021) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2021.3103280 · Cited by 14
“As an alternative of GCNs, differential rewiring or differential co-expression among genes has been proved to be an effective tool to identify informative genes that may contribute to the phenotype differences between treated and con-trol samples [27], [43], [44], [45], [46], [47], [48], [49].”
MethodInfluentialDTW-MIC Coexpression Networks from Time-Course Data
Riccadonna, S., Jurman, G., Visintainer, R., Filosi, M., & Furlanello, C. (2016) · PLoS ONE · doi:10.1371/journal.pone.0152648 · Cited by 17
Orduña, L., Santiago, A., Navarro‐Payá, D., Zhang, C., Wong, D. C. J., & Matus, J. T. (2023) · Journal of Experimental Botany · doi:10.1093/jxb/erad344 · Cited by 13
Metadata
- Authors
- Ballouz, S., Verleyen, W., & Gillis, J.
- Affiliations
- Cold Spring Harbor Laboratory (US)
- Corresponding author
- Ballouz, S.
- Gillis, J.
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2015-02-28
- Volume
- 31
- Issue
- 13
- Pages
- 2123-2130
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W2104724027
- Cited by
- 215
- References
- 47
- Citations by year
- 2026: 7 · 2025: 15 · 2024: 7 · 2023: 21 · 2022: 15 · 2021: 31 · 2020: 18 · 2019: 27 · 2018: 24 · 2017: 23 · 2016: 20 · 2015: 6
- FWCI
- 10.55
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Bioinformatics (publishedVersion)
- PubMed (publishedVersion)
- Cold Spring Harbor Laboratory Institutional Repository (Cold Spring Harbor Laboratory) (acceptedVersion)
- HighWire Press Open Archive (submittedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- guilt by association, gene co-expression, network topology, hub genes, sample size requirements, sequencing depth, stochastic gene expression, microarray comparison, functional connectivity, gene function prediction
- MeSH terms
- Algorithms, Brain, Humans, Models, Theoretical, Sequence Analysis, RNA, Computational Biology, Gene Expression Profiling, Gene Regulatory Networks, High-Throughput Nucleotide Sequencing
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