Persistent homology is a well-developed tool which allows topological analysis of large data sets. From a topological perspective, the input is a filtered complex, and the output is a sequence of collections of intervals (one for each dimension) called a *persistence barcode*. The barcode gives information about homology classes which are born and die as you vary the scale (filtration parameter).


This is a very brief, non-expert summary. By now there are several good survey articles on the subject by experts in the field, for example by [Gunnar Carlsson](http://www.ams.org/journals/bull/2009-46-02/S0273-0979-09-01249-X/) and [Rob Ghrist](http://www.math.upenn.edu/~ghrist/preprints/barcodes.pdf).


On the other hand, given a filtered complex $X\_\bullet$, one obtains a spectral sequence converging to the homology $H\_\ast(X\_\bullet)$ (or at least its associated graded object) . It is natural to ask how the persistence barcode relates to this spectral sequence. In a formative paper on the subject by [Carlsson and Zomorodian](http://www.cs.dartmouth.edu/~afra/papers/socg04/persistence.pdf), the authors ask exactly this question in section 1.4 of the introduction, claiming that a persistence interval of length $r$ in the barcode corresponds to a differential $d\_{r+1}$. Thus, in principle, any algorithm for computing persistent homology should give an algorithmic way of computing the differentials in a spectral sequence. So persistent homology, which already has many applications outside of topology, becomes potentially applicable to topology itself.



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> Has anyone ever pursued this approach, and used algorithms for persistent homology to compute the differentials in a spectral sequence? Does this lead to any new theoretical insights?
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I am imagining that by knowing the values of a differential in a given situation one might guess at a description of the differential (eg, in terms of cohomology operations) which applies more generally. 


**Edit:** The book [Computational Topology: An Introduction](http://www.ams.org/bookstore-getitem/item=mbk-69) by Edelsbrunner and Harer states a Spectral Sequence Theorem in Chapter VII.4, which says roughly that the total rank of the $E^r\_{\ast,\ast}$ page of the spectral sequence equals the number of homology classes of persistence $r$ or larger. Here coefficients are taken mod 2. This makes precise the claim made by Carlsson and Zomorodian. 



Persistence barcodes and spectral sequences
The answer to your question is no, nobody has used persistence to improve the algorithmic efficiency of computing differentials, although of course the relationship between persistence intervals of a filtration and various terms in its Leray spectral sequence have been [described rather explicitly](http://arxiv.org/abs/1308.0801) by Basu and Parida. 


Here's an elementary observation: as mentioned in that article by Carlsson and Zomorodian, every sequence of $k$-modules admits a straightforward reinterpretation as a graded $k[t]$-module where $t$ acts by moving things forward one step along the grading. The existence of a persistence barcode relies crucially on the structure theorem for graded modules over graded PIDs. Since $k[t]$ is a PID only when $k$ is a field, relying on persistence will not solve any extension problems for you when you try to compute differentials -- all your $E\_{\bullet,\bullet}^\bullet$s will have to be vector spaces already. 


