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Fast intratumor heterogeneity inference from single-cell sequencing data

Nature Computational Science volume 2, pages 577–583 (2022)Cite this article

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Abstract

We introduce HUNTRESS, a computational method for mutational intratumor heterogeneity inference from noisy genotype matrices derived from single-cell sequencing data, the running time of which is linear with the number of cells and quadratic with the number of mutations. We prove that, under reasonable conditions, HUNTRESS computes the true progression history of a tumor with high probability. On simulated and real tumor sequencing data, HUNTRESS is demonstrated to be faster than available alternatives with comparable or better accuracy. Additionally, the progression histories of tumors inferred by HUNTRESS on real single-cell sequencing datasets agree with the best known evolution scenarios for the associated tumors.

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Fig. 1: Analysis of the HGSOC dataset16.
Fig. 2: Analysis of the AML dataset.

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Data availability

Our study makes use of two publicly available datasets introduced in previous studies16,17. For the leukemia dataset, single-cell and bulk sequencing data have been deposited at NCBI BioProject ID PRJNA648656 and SNP array data at NCBI GEO ID GSE156934. For the HGSOC dataset, the single-cell FASTQs have been deposited in the European Genome-phenome Archive under accession no. EGA: EGAS00001003190. The OV2295 datasets are available at Zenodo20. Source data for the performance results for Figs. 1 and 2 are available in Supplementary Table 1. Simulated data generated and used in this study for obtaining the results shown in Extended Data Figs. 1–10 are available at Zenodo21. Source data are provided with this paper.

Code availability

The open-source implementation of HUNTRESS is available at Zenodo22.

Change history

  • 16 September 2022

    In the version of this article initially published, the email address shown for Salem Malikić was incorrect and has been amended in the HTML and PDF versions of the article.

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Acknowledgements

This work is supported in part by the Intramural Research Program of the National Institutes of Health, National Cancer Institute (to F.R.M., E.P.-G., K.L.M., M.P.L., C.-P.D., G.M., S.C.S. and S.M.) and utilized the computational resources of the NIH Biowulf high-performance computing cluster (http://hpc.nih.gov) and Gurobi (http://www.gurobi.com) to solve some optimization problems. Additionally, F.R.M. was supported in part by Indiana U. Grand Challenges Precision Health Initiative. C.K. and A.B. were supported by the Advanced Scientific Computing Research (ASCR) Program of the Department of Energy Office of Science under contract no. DE-AC02- 05CH11231.

Author information

Author notes

  1. Erfan Sadeqi Azer

    Present address: Google LLC, Sunnyvale, CA, USA

  2. These authors contributed equally: Can Kizilkale, Farid Rashidi Mehrabadi.

Authors and Affiliations

  1. Department of Electrical Engineering and Computer Sciences UC Berkeley, Berkeley, CA, USA

    Can Kızılkale & Aydın Buluç

  2. Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Can Kızılkale & Aydın Buluç

  3. Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA

    Farid Rashidi Mehrabadi, S. Cenk Sahinalp & Salem Malikić

  4. Department of Computer Science, Indiana University, Bloomington, IN, USA

    Farid Rashidi Mehrabadi, Erfan Sadeqi Azer & Funda Ergün

  5. Laboratory of Cancer Biology and Genetics, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA

    Eva Pérez-Guijarro, Kerrie L. Marie, Maxwell P. Lee, Chi-Ping Day & Glenn Merlino

Authors
  1. Can Kızılkale
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Contributions

S.C.S. and A.B. jointly initiated and supervised the project. The algorithmic approach was developed by C.K. HUNTRESS was implemented by C.K. and was tested by F.R.M. Theorem 1, related lemmas and their proofs are by S.C.S., S.M., F.E. and C.K. Experimental results on real data and external simulators are by F.R.M. The internal simulator was developed and the simulated data were generated by S.M. The experimental results on the internal simulator are by C.K. and F.R.M. The bulk of the paper was written by S.M., S.C.S., F.R.M., C.K. and F.E. with feedback from all co-authors. E.P.-G., K.L.M., M.P.L., E.S.A., C.-P.D. and G.M. contributed to the interpretation of the results and biological implications.

Corresponding authors

Correspondence to S. Cenk Sahinalp or Salem Malikić.

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Competing interests

The authors declare no competing interests.

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Peer review information

Nature Computational Science thanks Yufeng Wu, Hamim Zafar and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Fernando Chirigati, in collaboration with the Nature Computational Science team.

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Extended data

Extended Data Fig. 1 A running time assessment of HUNTRESS on simulated data with no false positives, in comparison to ScisTree, SPhyR, and PhISCS-BnB, as well as its slower but more general variants, PhISCS-I, and PhISCS-B.

All numbers on y-axis are in log10 scale. Here n, m and fn, respectively, denote the number of cells, the number of mutations and the false negative error rate in single-cell data. For each setting of n and m, we report the distribution of the running time for each tool - over 10 distinct trees of tumor progression, with false negative error rates of 0.05, 0.1 and 0.2. Each tool was run with a time limit of 8 hours (those cases that exceed the time limit are not included here). The corresponding accuracy measures for each setting are shown in Extended Data Figure 2 (ancestor descendant accuracy measure) and Extended Data Figure 3 (different-lineages accuracy measure).

Source data

Extended Data Fig. 2 Comparison of ancestor-descendant (AD) accuracy measures for HUNTRESS, PhISCS-BnB, ScisTree and SPhyR on simulated data with no false positives.

Here n, m and fn, respectively, denote the number of cells, the number of mutations and the false negative error rate in single-cell sequencing. For each setting of n and m, we report the ancestor-descendent (AD) accuracy measure for each tool with respect to the ground truth. The experiments were performed over 10 distinct trees of tumor progression, using a false negative error rate of 0.05, 0.1 or 0.2. Each tool was run with a time limit of 8 hours (those cases that exceed the time limit are not included here). Note that we have not included results of PhISCS-I and PhISCS-B as their accuracy values are identical to that of PhISCS-BnB (on these instances on which they completed the task) due to the same underlying objective function and optimality guarantee that they all provide.

Source data

Extended Data Fig. 3 Comparison of different-lineages (DL) accuracy measure distributions for HUNTRESS, PhISCS-BnB, ScisTree and SPhyR on simulated data with no false positives.

Here n, m and fn, respectively, denote the number of cells, the number of mutations and the false negative error rate for single-cell sequencing. For each setting of n and m, we report the different-lineages (DL) accuracy measure for each tool with respect to the ground truth. The experiments were performed over 10 distinct trees of tumor progression, with a false negative error rate varying across 0.05, 0.1 and 0.2. Each tool was run with a time limit of 8 hours (those cases that exceed the time limit are not included here). Note that we have not included results of PhISCS-I and PhISCS-B separately as their accuracy values match those of PhISCS-BnB (on these instances on which they completed the task) due to the same underlying objective function and optimality guarantee that they all provide.

Source data

Extended Data Fig. 4 Ancestor-Descendant (AD) accuracy measure distributions for HUNTRESS, ScisTree and SPhyR on simulated data with false positives, false negatives and missing entries.

Here n, m, fn, fp and na respectively, denote the number of cells, the number of mutations, the false negative, false positive error and missing entry rates in single-cell sequencing data. For each setting we report the distribution for each tool over 10 distinct trees of tumor progression. Each tool was allowed to run with a time limit of 48 hours (those cases that exceed the time limit are not included here). Note that each (violin) plot shows the maximum, center and minimum value of the data depicted, together with the probability density.

Source data

Extended Data Fig. 5 Different Lineages (DL) accuracy measure distributions for HUNTRESS, ScisTree and SPhyR on simulated data with false positives, false negatives and missing entries.

Here n, m, fn, fp and na respectively, denote the number of cells, the number of mutations, the false negative, false positive error and missing entry rates in single-cell sequencing data. For each setting we report the distribution for each tool over 10 distinct trees of tumor progression. Each tool was allowed to run with a time limit of 48 hours (those cases that exceed the time limit are not included here). Note that each (violin) plot shows the maximum, center and minimum value of the data depicted, together with the probability density.

Source data

Extended Data Fig. 6 Running time distributions for HUNTRESS, ScisTree and SPhyR on simulated data with false positives, false negatives and missing entries.

Here n, m, fn, fp and na respectively, denote the number of cells, the number of mutations, the false negative, false positive error and missing entry rates in single-cell sequencing data. For each setting we report the distribution for each tool over 10 distinct trees of tumor progression. For any given task each tool was allowed to run with a time limit of 48 hours (those cases that exceed the time limit are not included here). Note that each (violin) plot shows the maximum, center and minimum value of the data depicted, together with the probability density.

Source data

Extended Data Fig. 7 Ancestor-Descendant (AD) and Different Lineages (DL) accuracy measures as well as running time distributions for HUNTRESS, ScisTree and SPhyR on simulated datasets with doublets.

Here n=1000, m=300, fn=0.2 or 0.05, fp=0.001, na=0.05, and the doublet rate is set to 0.03. Each distribution is over 10 distinct trees of tumor progression. For any given task each tool was allowed to run with a time limit of 48 hours. Note that each (violin) plot shows the maximum, center and minimum value of the data depicted, together with the probability density.

Source data

Extended Data Fig. 8 Ancestor-Descendant (AD) and Different Lineages (DL) accuracy measures, as well as the running time distributions for HUNTRESS on large simulated datasets.

In these simulations we set n=5000, m=500, fn=0.05 or 0.2, fp=0.001 and na=0.05. For each setting, the distributions are reported over 10 distinct trees of tumor progression. Note that because ScisTree could not finish any of the tasks within the time limit of 48 hours and SPhyR failed to generate any output, they are not presented in the figure. Note that each (violin) plot shows the maximum, center and minimum value of the data depicted, together with the probability density.

Source data

Extended Data Fig. 9 Ancestor-Descendant (AD) and Different Lineages (DL) accuracy measures as well as running time distributions for HUNTRESS, ScisTree and SPhyR on simulated datasets with a high(er) false positive rate of fp=0.003.

Here n=1000, m=300, fn=0.2 or 0.05, and na=0.05. Each distribution is over 10 distinct trees of tumor progression. For any given task each tool was allowed to run with a time limit of 48 hours. Note that each (violin) plot shows the maximum, center and minimum value of the data depicted, together with the probability density.

Source data

Extended Data Fig. 10 Running time, ancestor-descendant and different lineages accuracy measure distributions for HUNTRESS and SPhyR on simulations with parameters similar to those observed in the AML dataset (the Tapestri platform).

Here n=5000, m=50, fn=0.2 or 0.05, fp=0.01 or 0.003 and na=0.1. All simulated data used in this figure were generated by the simulator developed for OncoNEM. For any given task each tool was allowed to run with a time limit of 48 hours. Note that each (box) plot shows the maximum, center and minimum value, as well as the median half of the data depicted.

Source data

Supplementary information

Supplementary Information

Supplementary Figs. 1 and 2, Tables 1–7, Algorithms 1–5, proofs and discussions.

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Kızılkale, C., Rashidi Mehrabadi, F., Sadeqi Azer, E. et al. Fast intratumor heterogeneity inference from single-cell sequencing data. Nat Comput Sci 2, 577–583 (2022). https://doi.org/10.1038/s43588-022-00298-x

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