From 36e7964b31787ffeb6064f3a424c2e131eab1cf6 Mon Sep 17 00:00:00 2001 From: mikelove Date: Mon, 12 Nov 2018 13:41:23 -0500 Subject: [PATCH] first hack at abstract, 251 words --- content/01.abstract.md | 30 ++++++++++++++++++++++++++++++ 1 file changed, 30 insertions(+) diff --git a/content/01.abstract.md b/content/01.abstract.md index c6ebbc4..c5a9289 100644 --- a/content/01.abstract.md +++ b/content/01.abstract.md @@ -1,3 +1,33 @@ ## Abstract {.page_break_before} **Instructions**: Describe your collaborative project, highlighting key achievements of the project; limited to 250 words. + + +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 +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 +accurate quantification, and implement these base enabling +technologies and methods for search, analysis, and latent space +transformations as freely available, open source software tools. +By using and extending our base enabling technologies, we will provide +three principle tools and resources for the HCA. These include 1) +software to enable fast and accurate search and annotation using +low-dimensional representations of cellular features, 2) a versioned +and annotated catalog of latent spaces corresponding to signatures of +cell types, states, and biological attributes across the the HCA, and +3) short course and educational materials that will increase the use +and impact of low-dimensional representations and the HCA in general.