August 16, 2017  |  Announcements, DROP, Release Notes  |  By  |  0 Comments

New Release: DROP Expands Machine Learning Analysis
to More Data Types

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The team at Cytobank is delighted to announce the release of version 6.0.

In addition to updates and improvements across the platform, we are most excited to announce that our new DROP functionality is now available on all Enterprise Cytobank sites.

DROP (Data to Results Optimization Portal) enables you to import any numeric data in the format of a spreadsheet / numeric matrix for analysis by automating its conversion into an FCS file within Cytobank.

Background:

The investigation of biological systems often requires the use of multiple technologies to understand states and processes throughout different components of organisms and their cells.

These technologies produce data in a format similar to cytometry: a familiar numerical matrix with the relative quantity of a biological marker being measured (proteins, RNA, DNA, clinical variables) for different observations (cells, samples, patients).

Many of Cytobank’s tools and workflows for automated data analysis typically reserved for cytometry are also effective for the analysis of other data, such as those derived from DNA, RNA, imaging, clinical information, other protein measurements, metabolomics, etc. What’s more, this analysis is effective in both single cells and bulk cell mixtures.

See DROP in Action:

We’ve already compiled some great examples using existing data. Check out these recent blog posts.

cyobankarrow40x40Cytobank Analysis Using DROP on NanoString Protein & RNA Markers

cyobankarrow40x40Understanding Sample Heterogeneity from RNA-seq Data with DROP + viSNE

 

Which data analysis methods to use?

The analytical strategies taken on any data will depend on the nature of the data. See below which analytical methods you can apply in Cytobank based on the example technology and data properties:

Table for mapping data types to analysis options. "Continuous" describes data that represents a quantity or magnitude measurement of some trait of the observation. For example, the relative quantity of some gene or protein. "Ordinal" describes data that take the form of discrete categories that have a natural order. For example, pain on a scale of 1-10 or genotype at a locus (0,1,2). Categorical data that do not have a natural order are termed "nominal" and shouldn't be used in any algorithmic analysis, though they can be used for useful visual annotations if they accompany continuous or ordinal data

 

Learn More:

We will host a free demo and training session Wednesday, August 23rd. Join our Application Scientist Geoff Kraker for a live overview with Q&A.

cyobankarrow40x40Register Now for Our Webinar

Get Started with DROP: