From e20c10773c1d9e4e7bc620da199ddf64029166bc Mon Sep 17 00:00:00 2001 From: Stephanie Hicks Date: Mon, 12 Nov 2018 20:03:56 -0500 Subject: [PATCH] tweaking the abstract --- content/01.abstract.md | 15 +++++++-------- 1 file changed, 7 insertions(+), 8 deletions(-) diff --git a/content/01.abstract.md b/content/01.abstract.md index 8a2b4df..184adde 100644 --- a/content/01.abstract.md +++ b/content/01.abstract.md @@ -7,19 +7,18 @@ The HCA provides a reference atlas to human cell types, states, and the biological processes in which they engage. The utility of the reference therefore requires that one can easily compare references to each other, or a new sample to the compendium of reference -samples. Low-dimensional representations, because they compress the -space, provide the building blocks for search approaches that can be +samples. Because they compress the space, low-dimensional representations +provide the building blocks for search approaches that can be practically applied across very large datasets such as the HCA. Our seed network proposes to compress HCA data into fewer dimensions that preserve the important attributes of the original high dimensional data and yield interpretable, searchable features. -We hypothesize that building an ensemble of low -dimensional representations across latent space methods will provide a -reduced dimensional space that captures biological sources of -variability and is robust to measurement noise. -We will identify techniques that learn interpretable, -biologically-aligned representations, improve techniques for fast and +We hypothesize that using latent space methods to identify low +dimensional representations of HCA data will accurately capture biological +sources of variability and will be robust to measurement noise. +We propose techniques that learn interpretable, biologically-aligned +representations, improve techniques for fast and accurate quantification, and implement these base enabling technologies and methods for search, analysis, and latent space transformations as freely available, open source software tools.