The table lists the theoretical complexity and run-time (in seconds) of the four methods, SMASH, SPARK-X, SpaGene, and SpatialDE in a simulation setup with K = 1000 genes and varying number of cells N. The number of spatial coordinates d was equal to 2. *SpaGene constructs multiple kNN graphs and performs permutation tests. We are only listing the complexity of the KNN algorithm.</p
The computational efficiency and computational complexity of different networks.</p
<p>The leading order of the computational complexity of the algorithm as a power of , where is the ...
Top row corresponds to the run-times in seconds of different methods in scenario (S1) and scenario (...
(A) Number of edges in the simplified tree sequence for 10 replicate Wright–Fisher simulations with ...
<p>Computational time (in seconds) for reconstructing a single chromosome structure using three diff...
<p>Computation Time in the stages of: (a) scaled down data clustering, (b) extend to all data cluste...
He number of compute nodes used in the analysis. The bar graphs at the bottom of each plot illustrat...
<p>Italicized rows correspond to values of approximated from real cancer datasets. Each entry is me...
<p>The computation time for a single run of sparse CGGM, MRCE, and GFlasso is shown for (A) varying ...
A Four spatial expression patterns that the genes were assumed to follow. B Statistical power plots ...
(A) The clustering runtime vs. the number of cells in the simulated datasets for all four methods. (...
<p>(A) is the transient simulation of gene regulator networks composed of variable number of genes. ...
<p>Each value is the average time in seconds for 10 independent runs.</p><p>Comparison of the comput...
<p>Computational time is depicted as a function of genome size when setting the sample size to 50, a...
Much of the research progress that is achieved nowadays in various scientific fields has its origin ...
The computational efficiency and computational complexity of different networks.</p
<p>The leading order of the computational complexity of the algorithm as a power of , where is the ...
Top row corresponds to the run-times in seconds of different methods in scenario (S1) and scenario (...
(A) Number of edges in the simplified tree sequence for 10 replicate Wright–Fisher simulations with ...
<p>Computational time (in seconds) for reconstructing a single chromosome structure using three diff...
<p>Computation Time in the stages of: (a) scaled down data clustering, (b) extend to all data cluste...
He number of compute nodes used in the analysis. The bar graphs at the bottom of each plot illustrat...
<p>Italicized rows correspond to values of approximated from real cancer datasets. Each entry is me...
<p>The computation time for a single run of sparse CGGM, MRCE, and GFlasso is shown for (A) varying ...
A Four spatial expression patterns that the genes were assumed to follow. B Statistical power plots ...
(A) The clustering runtime vs. the number of cells in the simulated datasets for all four methods. (...
<p>(A) is the transient simulation of gene regulator networks composed of variable number of genes. ...
<p>Each value is the average time in seconds for 10 independent runs.</p><p>Comparison of the comput...
<p>Computational time is depicted as a function of genome size when setting the sample size to 50, a...
Much of the research progress that is achieved nowadays in various scientific fields has its origin ...
The computational efficiency and computational complexity of different networks.</p
<p>The leading order of the computational complexity of the algorithm as a power of , where is the ...
Top row corresponds to the run-times in seconds of different methods in scenario (S1) and scenario (...