An Overview of NCA-Based Algorithms for Transcriptional Regulatory Network Inference
Abstract
:1. Introduction
2. NCA
- 1
- The connectivity matrix must be full-column rank.
- 2
- If a column of is removed along with all of the rows corresponding to the nonzero entries of the removed column, the remaining matrix must still be full-column rank.
- 3
- The TFA matrix must have full row rank.
3. Extensions of NCA
3.1. Motif-Directed NCA
3.2. Generalized NCA
3.3. Revised NCA
3.4. Generalized-Framework NCA
4. Alternative NCA-Based Algorithms
4.1. Iterative NCA Algorithms
Robust NCA
4.2. Non-Iterative NCA Algorithms
4.2.1. Subspace Separation Principle
4.2.2. FastNCA
4.2.3. Positive NCA, Non-Negative NCA and Non-Iterative NCA
5. Simulation Results
Algorithm | ANSME | SNSME | Data Fitting Error | Computation Time | |||
---|---|---|---|---|---|---|---|
Noise | Noise + Outliers | Noise | Noise + Outliers | Noise | Noise + Outliers | ||
FastNCA | 0.0571 | 0.0500 | 0.2544 | 0.2666 | 1.6973 | 4.4193 | 0.0005 |
NINCA | 0.0037 | 0.0134 | 0.2250 | 0.2280 | 1.7361 | 4.7164 | 0.0119 |
ROBNCA | 0.0033 | 0.0044 | 0.2218 | 0.2062 | 1.7141 | 4.5630 | 0.0080 |
NCA | 0.0033 | 0.0060 | 0.2217 | 0.2068 | 1.7139 | 4.4809 | 6.6728 |
PosNCA | 0.0031 | 0.0055 | 0.3896 | 0.3451 | 1.8200 | 4.7275 | 0.2648 |
6. Comparison of NCA-Based Algorithms
6.1. Estimating the Connectivity Matrix
6.2. Estimating the TF Matrix
6.3. Recommendations on Choosing the Appropriate Algorithm
7. Conclusions
Algorithm | Category | Estimation Technique | Contribution |
---|---|---|---|
NCA [17] | Iterative | ALS | Proposed the NCA framework and criteria, motivated other NCA algorithms |
mNCA [25] | Iterative | ALS | Incorporated motif information to obtain the prior connectivity information |
gNCA [26] | Iterative | ALS | Incorporated the prior information about the TFA matrix |
NCAr [27] | Iterative | ALS | Revised and extended the third identification criterion |
gfNCA [28] | Iterative | ALS | Modified the criteria of NCA, such that they are only related to the prior connectivity information |
ROBNCA [29] | Non-iterative | Alternate optimization | Reduced the computational complexity and improved the robustness against outliers |
FastNCA [31] | Non-iterative | SSP, rank-1 factorization | Reduced the computational complexity |
PosNCA [33] | Non-iterative | SSP, convex optimization | Combined additional prior information to reduce the complexity |
nnNCA [34] | Non-iterative | SSP, convex optimization | Combined additional prior information and reduced the complexity |
NINCA [32] | Non-iterative | SSP, convex optimization, TLS | Combined additional prior information, reduced the complexity and improved the estimation accuracy |
Acknowledgments
Conflicts of Interest
References
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Wang, X.; Alshawaqfeh, M.; Dang, X.; Wajid, B.; Noor, A.; Qaraqe, M.; Serpedin, E. An Overview of NCA-Based Algorithms for Transcriptional Regulatory Network Inference. Microarrays 2015, 4, 596-617. https://doi.org/10.3390/microarrays4040596
Wang X, Alshawaqfeh M, Dang X, Wajid B, Noor A, Qaraqe M, Serpedin E. An Overview of NCA-Based Algorithms for Transcriptional Regulatory Network Inference. Microarrays. 2015; 4(4):596-617. https://doi.org/10.3390/microarrays4040596
Chicago/Turabian StyleWang, Xu, Mustafa Alshawaqfeh, Xuan Dang, Bilal Wajid, Amina Noor, Marwa Qaraqe, and Erchin Serpedin. 2015. "An Overview of NCA-Based Algorithms for Transcriptional Regulatory Network Inference" Microarrays 4, no. 4: 596-617. https://doi.org/10.3390/microarrays4040596