UMAP for Data Integration

This is the twentieth article from the column Mathematical Statistics and Machine Learning for Life Sciences where I try to explain some mysterious analytical techniques used in Bioinformatics and Computational Biology in a simple way. Data integration is an important next step for improving analysis accuracy by utilizing synergistic effects via combining multiple sources of information. In Computational Biology and Biomedicine, data integration is making particular advances in Single Cell research area. Last year, Nature recognized Single Cell Multimodal Omics Integration as a method of the year 2019. In this article, I will demonstrate how one can perform graph based across modalities integration of single cell Omics (scOmics) data by using graph intersection approach with Igraph and** UMAP**.

Idea Behind Data Integration

When combining data originating from different statistical distributions, it is probably not a good idea to simply concatenate them together without taking into account their individual distributions as some of them might be of binary, categorical or continuous nature. Working with scOmics data one often deals with continuous scRNAseq (transcriptomics) / scProteomics (proteomics) and binary scBSseq (methylation) / scATAseq (open chromatin region) data. Suppose the individual data types are capable of distinguishing cells from sick and healthy individuals with an accuracy of 78%, 83% and 75% (the numbers are made up). Then, one expectation from combining the multiple scOmics together would be a boost in prediction accuracy (e.g. up to 96%) due to reduction of technology-specific noise and enhancing the consistent across scOmics signal.

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There can be different approaches to combining the individual scOmics. One possible way could be converting the scOmics into a common non-parametric space where they loose the memory about their technology of origin. This is how e.g. Artificial Neural Networks (ANN) are capable of collapsing multiple sources of information, that I describes in details in one of my previous posts. The Similarity Network Fusion (SNF) also belongs to this type of approach. Another approach would be explicitly model the individual data distributions and combining them as a joint probability using Bayes rule. Finally, one can try to extract common variation (i.e. disentangle the technology-specific and shared variation in the data) and factor it (split into orthogonal components) for better interpretation, a way used by PLSCCA and Factor Analysis.

#editors-pick #machine-learning #stats-ml-life-sciences #towards-data-science #data-science

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UMAP for Data Integration
Siphiwe  Nair

Siphiwe Nair

1620466520

Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

Virgil  Hagenes

Virgil Hagenes

1602702000

Data Quality Testing Skills Needed For Data Integration Projects

The impulse to cut project costs is often strong, especially in the final delivery phase of data integration and data migration projects. At this late phase of the project, a common mistake is to delegate testing responsibilities to resources with limited business and data testing skills.

Data integrations are at the core of data warehousing, data migration, data synchronization, and data consolidation projects.

In the past, most data integration projects involved data stored in databases. Today, it’s essential for organizations to also integrate their database or structured data with data from documents, e-mails, log files, websites, social media, audio, and video files.

Using data warehousing as an example, Figure 1 illustrates the primary checkpoints (testing points) in an end-to-end data quality testing process. Shown are points at which data (as it’s extracted, transformed, aggregated, consolidated, etc.) should be verified – that is, extracting source data, transforming source data for loads into target databases, aggregating data for loads into data marts, and more.

Only after data owners and all other stakeholders confirm that data integration was successful can the whole process be considered complete and ready for production.

#big data #data integration #data governance #data validation #data accuracy #data warehouse testing #etl testing #data integrations

Uriah  Dietrich

Uriah Dietrich

1618457700

What Is ETLT? Merging the Best of ETL and ELT Into a Single ETLT Data Integration Strategy

Data integration solutions typically advocate that one approach – either ETL or ELT – is better than the other. In reality, both ETL (extract, transform, load) and ELT (extract, load, transform) serve indispensable roles in the data integration space:

  • ETL is valuable when it comes to data quality, data security, and data compliance. It can also save money on data warehousing costs. However, ETL is slow when ingesting unstructured data, and it can lack flexibility.
  • ELT is fast when ingesting large amounts of raw, unstructured data. It also brings flexibility to your data integration and data analytics strategies. However, ELT sacrifices data quality, security, and compliance in many cases.

Because ETL and ELT present different strengths and weaknesses, many organizations are using a hybrid “ETLT” approach to get the best of both worlds. In this guide, we’ll help you understand the “why, what, and how” of ETLT, so you can determine if it’s right for your use-case.

#data science #data #data security #data integration #etl #data warehouse #data breach #elt #bid data

Gerhard  Brink

Gerhard Brink

1620629020

Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.

Introduction

As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).


This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.

#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management

Analyzing Data From U.S. Road Accidents With Data Visualization

Every 24 seconds, a life is lost on the road, and it costs countries around 3% of their gross domestic product - World Health Organization.

With a fatality rate of 12.3% per 100,000 inhabitants, traffic accidents are a leading cause of death in the United States. In 2019, it was reported that 36,096 lives were lost on U.S. roads and according to the National Highway Traffic System Administration (NHTSA), it costs about $871 billion annually to the U.S. economy.

In this article, we would be analyzing data related to US road accidents, which can be utilized to study accident-prone locations and also helps understand the factors that influence road fatalities in the United States.

“Having access to accurate and updated information about the current road situation enables drivers, pedestrians, and passengers to make informed road safety decisions.”

- Association For Safe International Road Travel.

#data-science #big-data-analytics #data-integration #solving-data-integration #data #data-analysis