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Archive for the ‘Advanced Computing Platform’ Category


scPopCorn: A New Computational Method for Subpopulation Detection and their Comparative Analysis Across Single-Cell Experiments

Reporter and Curator: Dr. Sudipta Saha, Ph.D.

 

Present day technological advances have facilitated unprecedented opportunities for studying biological systems at single-cell level resolution. For example, single-cell RNA sequencing (scRNA-seq) enables the measurement of transcriptomic information of thousands of individual cells in one experiment. Analyses of such data provide information that was not accessible using bulk sequencing, which can only assess average properties of cell populations. Single-cell measurements, however, can capture the heterogeneity of a population of cells. In particular, single-cell studies allow for the identification of novel cell types, states, and dynamics.

 

One of the most prominent uses of the scRNA-seq technology is the identification of subpopulations of cells present in a sample and comparing such subpopulations across samples. Such information is crucial for understanding the heterogeneity of cells in a sample and for comparative analysis of samples from different conditions, tissues, and species. A frequently used approach is to cluster every dataset separately, inspect marker genes for each cluster, and compare these clusters in an attempt to determine which cell types were shared between samples. This approach, however, relies on the existence of predefined or clearly identifiable marker genes and their consistent measurement across subpopulations.

 

Although the aligned data can then be clustered to reveal subpopulations and their correspondence, solving the subpopulation-mapping problem by performing global alignment first and clustering second overlooks the original information about subpopulations existing in each experiment. In contrast, an approach addressing this problem directly might represent a more suitable solution. So, keeping this in mind the researchers developed a computational method, single-cell subpopulations comparison (scPopCorn), that allows for comparative analysis of two or more single-cell populations.

 

The performance of scPopCorn was tested in three distinct settings. First, its potential was demonstrated in identifying and aligning subpopulations from single-cell data from human and mouse pancreatic single-cell data. Next, scPopCorn was applied to the task of aligning biological replicates of mouse kidney single-cell data. scPopCorn achieved the best performance over the previously published tools. Finally, it was applied to compare populations of cells from cancer and healthy brain tissues, revealing the relation of neoplastic cells to neural cells and astrocytes. Consequently, as a result of this integrative approach, scPopCorn provides a powerful tool for comparative analysis of single-cell populations.

 

This scPopCorn is basically a computational method for the identification of subpopulations of cells present within individual single-cell experiments and mapping of these subpopulations across these experiments. Different from other approaches, scPopCorn performs the tasks of population identification and mapping simultaneously by optimizing a function that combines both objectives. When applied to complex biological data, scPopCorn outperforms previous methods. However, it should be kept in mind that scPopCorn assumes the input single-cell data to consist of separable subpopulations and it is not designed to perform a comparative analysis of single cell trajectories datasets that do not fulfill this constraint.

 

Several innovations developed in this work contributed to the performance of scPopCorn. First, unifying the above-mentioned tasks into a single problem statement allowed for integrating the signal from different experiments while identifying subpopulations within each experiment. Such an incorporation aids the reduction of biological and experimental noise. The researchers believe that the ideas introduced in scPopCorn not only enabled the design of a highly accurate identification of subpopulations and mapping approach, but can also provide a stepping stone for other tools to interrogate the relationships between single cell experiments.

 

References:

 

https://www.sciencedirect.com/science/article/pii/S2405471219301887

 

https://www.tandfonline.com/doi/abs/10.1080/23307706.2017.1397554

 

https://ieeexplore.ieee.org/abstract/document/4031383

 

https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0927-y

 

https://www.sciencedirect.com/science/article/pii/S2405471216302666

 

 

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Seven Alternative Designs to Quantum Computing Platform – The Race by IBM, Google, Microsoft, and Others

 

Reporter: Aviva Lev-Ari, PhD, RN

 

Business Bets on a Quantum Leap

Quantum computing could help companies address problems as huge as supply chains and climate change. Here’s how IBM, Google, Microsoft, and others are racing to bring the tech from theory to practice.
May 21, 2019

quantum computer at IonQ, an Alphabet-backed startup

A version of this article appears in the June 2019 issue of Fortune with the headline “The Race for Quantum Domination.”

Medicine

One day, your health may depend on a quantum leap.

  • Pharmaceutical giant Biogen teamed up with consultancy Accenture and startup 1QBit on a quantum computing experiment in 2017 aimed at molecular modeling, one of the more complex disciplines in medicine. The goal: finding candidate drugs to treat neurodegenerative diseases.
  • Microsoft is collaborating with Case Western Reserve University to improve the accuracy of MRI machines, which help detect cancer, using so-called quantum-inspired algorithms.

 

7 ways to win the quantum race

There are multiple ways that quantum computing could work.

Here’s a guide to which companies are backing which tech.

Superconducting uses an electrical current, flowing through special semiconductor chips cooled to near absolute zero, to produce computational “qubits.” Google, IBM, and Intel are pursuing this approach, which has so far been the front-runner.

Ion trap relies on charged atoms that are manipulated by lasers in a vacuum, which helps to reduce noisy interference that can contribute to errors. Industrial giant Honeywell is betting on this technique. So is IonQ, a startup with backing from Alphabet.

Neutral Atom Similar to the ion-trap method, except it uses, you guessed it, neutral atoms. Physicist Mikhail Lukin’s lab at Harvard is a pioneer.

Annealing designed to find the lowest-energy (and therefore speediest) solutions to math problems. Canadian firm D-Wave has sold multimillion-dollar machines based on the idea to Google and NASA. They’re fast, but skeptics question whether they qualify as “quantum.”

Silicon spin uses single electrons trapped in transistors. Intel is hedging its bets between the more mature superconducting qubits and this younger, equally semiconductor-friendly method.

Topological uses exotic, highly stable quasi-particles called “anyons.” Microsoft deems this unproven moonshot as the best candidate in the long run, though the company has yet to produce a single one.

Photonics uses light particles sent through special silicon chips. The particles interact with one another very little (good), but can scatter and disappear (bad). Three-year-old stealth startup Psi Quantum is tinkering away on this idea.

SOURCE

http://fortune.com/longform/business-quantum-computing/

 

Other related articles published in this Open Access Online Scientific Journal include the following:

 

  • R&D for Artificial Intelligence Tools & Applications: Google’s Research Efforts in 2018

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2019/01/16/rd-for-artificial-intelligence-tools-applications-googles-research-efforts-in-2018/

 

  • LIVE Day Two – World Medical Innovation Forum ARTIFICIAL INTELLIGENCE, Boston, MA USA, Monday, April 9, 2019

www.worldmedicalinnovation.org

https://pharmaceuticalintelligence.com/2019/04/09/live-day-two-world-medical-innovation-forum-artificial-intelligence-boston-ma-usa-monday-april-9-2019/

 

  • Research and Development (R&D) Expenditure by Country represent time, capital, and effort being put into researching and designing the products of the future – Data from the UNESCO Institute for Statistics adjusted for purchasing-power parity (PPP).

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2019/05/26/research-and-development-rd-expenditure-by-country-represent-time-capital-and-effort-being-put-into-researching-and-designing-the-products-of-the-future-data-from-the-unesco-institute-for-s/

 

  • Resources on Artificial Intelligence in Health Care and in Medicine: Articles of Note at PharmaceuticalIntelligence.com @AVIVA1950 @pharma_BI

https://www.linkedin.com/pulse/resources-artificial-intelligence-health-care-note-lev-ari-phd-rn/

 

  • IBM’s Watson Health division – How will the Future look like?I

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2019/04/24/ibms-watson-health-division-how-will-the-future-look-like/

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