Below are the abstract, introduction, and conclusion of a computer science research paper. Please summarize the main contribution of the work in a single sentence. Your response should include the summary and no additional text.

Paper text:

Single-cell RNA-sequencing (scRNA-seq) is a powerful tool for analyzing biological systems.
However, due to biological and technical noise, quantifying the effects of multiple experimental conditions presents an analytical challenge.
To overcome this challenge, we developed MELD: Manifold Enhancement of Latent Dimensions.
MELD leverages tools from graph signal processing to learn a latent dimension within the data scoring the prototypicality of each datapoint with respect to experimental or control conditions.
We call this dimension the Enhanced Experimental Signal (EES).
MELD learns the EES by filtering the noisy categorical experimental label in the graph frequency domain to recover a smooth signal with continuous values.
This method can be used to identify signature genes that vary between conditions and identify which cell types are most affected by a given perturbation.
We demonstrate the advantages of MELD analysis in two biological datasets, including T-cell activation in response to antibody-coated beads and treatment of human pancreatic islet cells with interferon gamma.
As single-cell RNA-sequencing (scRNA-seq) has become more accessible, design of single cell experiments has become increasingly complex.
However, quantifying the differences between single cell data sets collected from different conditions presents an analytical challenge.
There is often a large overlap between single-cell profiles across conditions and single cell data sets are prone to biological and technical noise.
As a result, the signal of an experimental perturbation is small with respect to the biological and technical variation in an experiment FIG0 .To
quantify the differences between experimental conditions, it would be helpful to find groups of cells that are prototypical of experimental or control conditions. Thus
, we effectively want a quantification (i.e. a score) of how prototypical each cell is of the control or experimental condition. Such
a score would identify the cells and populations that are the most or least affected by an experimental perturbation. We term
this score the Enhanced Experimental Signal (EES). For example
, in a simple experiment with one experimental condition and one control condition, we would like the EES to be +1 or -1 for cells that are most likely to arise in the experimental or control condition, respectively, and 0 for cells equally likely to arise in either condition.To derive this score, we developed MELD (Manifold Enhancement of Latent Dimensions). MELD is based
on methods from graph signal processing (GSP) that, despite their proven strength in other domains, have not often been used in biomedical data analysis BID6 .The key advantage
of GSP is the access to a set of tools for processing graph signals, which are functions defined over the nodes in a graph.MELD models the condition label indicating from which condition each cell was sampled as a graph signal that we call the Raw Experimental Signal. In a two-sample experiment
, the RES would be defined as -1 for cells from the control condition and +1 for cells in the experimental condition. To remove high-frequency
noise from the RES, MELD applies a novel filter over the graph frequency domain of the RES to infer the EES. Finally, we incorporate
information from the RES and EES into Vertex Frequency Clustering (VFC), a novel clustering algorithm that identifies cell types most or least affected by the experimental perturbation.MELD has wide applicability in the analysis of high dimensional single cell data. Here, we describe the algorithms
for MELD and VFC and demonstrate the methods on two scRNA-seq datasets. More results can be found in BID1
. The goal of the MELD algorithm is
to use a manifold model of cellular states across experimental conditions to learn an Enhanced Experimental Signal (EES) that quantifies how prototypical each cell is of each experimental condition.
MELD introduces a novel and flexible filter on graph frequency domain to remove noise from the experimental labels indicating from which condition each cell was sampled.
We show that this filter is capable of recovering unique signals from data with multiple structures.
We also demonstrate the ability of MELD and VFC to identify biologically relevant signals across multiple cell types and biological systems.
A novel graph signal processing framework for quantifying the effects of experimental perturbations in single cell biomedical data.