The Experts below are selected from a list of 36273 Experts worldwide ranked by ideXlab platform
Florian Klimm - One of the best experts on this subject based on the ideXlab platform.
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Functional Module detection through integration of single cell rna sequencing data with protein protein interaction networks
BMC Genomics, 2020Co-Authors: Florian Klimm, Enrique M Toledo, Thomas Monfeuga, Charlotte M. Deane, Fang Zhang, Gesine ReinertAbstract:Recent advances in single-cell RNA sequencing have allowed researchers to explore transcriptional function at a cellular level. In particular, single-cell RNA sequencing reveals that there exist clusters of cells with similar gene expression profiles, representing different transcriptional states. In this study, we present scPPIN, a method for integrating single-cell RNA sequencing data with protein–protein interaction networks that detects active Modules in cells of different transcriptional states. We achieve this by clustering RNA-sequencing data, identifying differentially expressed genes, constructing node-weighted protein–protein interaction networks, and finding the maximum-weight connected subgraphs with an exact Steiner-tree approach. As case studies, we investigate two RNA-sequencing data sets from human liver spheroids and human adipose tissue, respectively. With scPPIN we expand the output of differential expressed genes analysis with information from protein interactions. We find that different transcriptional states have different subnetworks of the protein–protein interaction networks significantly enriched which represent biological pathways. In these pathways, scPPIN identifies proteins that are not differentially expressed but have a crucial biological function (e.g., as receptors) and therefore reveals biology beyond a standard differential expressed gene analysis. The introduced scPPIN method can be used to systematically analyse differentially expressed genes in single-cell RNA sequencing data by integrating it with protein interaction data. The detected Modules that characterise each cluster help to identify and hypothesise a biological function associated to those cells. Our analysis suggests the participation of unexpected proteins in these pathways that are undetectable from the single-cell RNA sequencing data alone. The techniques described here are applicable to other organisms and tissues.
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Functional Module detection through integration of single cell rna sequencing data with protein protein interaction networks
BMC Genomics, 2020Co-Authors: Florian Klimm, Enrique M Toledo, Thomas Monfeuga, Charlotte M. Deane, Fang Zhang, Gesine ReinertAbstract:BACKGROUND Recent advances in single-cell RNA sequencing have allowed researchers to explore transcriptional function at a cellular level. In particular, single-cell RNA sequencing reveals that there exist clusters of cells with similar gene expression profiles, representing different transcriptional states. RESULTS In this study, we present SCPPIN, a method for integrating single-cell RNA sequencing data with protein-protein interaction networks that detects active Modules in cells of different transcriptional states. We achieve this by clustering RNA-sequencing data, identifying differentially expressed genes, constructing node-weighted protein-protein interaction networks, and finding the maximum-weight connected subgraphs with an exact Steiner-tree approach. As case studies, we investigate two RNA-sequencing data sets from human liver spheroids and human adipose tissue, respectively. With SCPPIN we expand the output of differential expressed genes analysis with information from protein interactions. We find that different transcriptional states have different subnetworks of the protein-protein interaction networks significantly enriched which represent biological pathways. In these pathways, SCPPIN identifies proteins that are not differentially expressed but have a crucial biological function (e.g., as receptors) and therefore reveals biology beyond a standard differential expressed gene analysis. CONCLUSIONS The introduced SCPPIN method can be used to systematically analyse differentially expressed genes in single-cell RNA sequencing data by integrating it with protein interaction data. The detected Modules that characterise each cluster help to identify and hypothesise a biological function associated to those cells. Our analysis suggests the participation of unexpected proteins in these pathways that are undetectable from the single-cell RNA sequencing data alone. The techniques described here are applicable to other organisms and tissues.
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Functional Module detection through integration of single cell rna sequencing data with protein protein interaction networks
bioRxiv, 2019Co-Authors: Florian Klimm, Enrique M Toledo, Thomas Monfeuga, Charlotte M. Deane, Fang Zhang, Gesine ReinertAbstract:Recent advances in single-cell RNA sequencing (scRNA-seq) have allowed researchers to explore transcriptional function at a cellular level. In this study, we present scPPIN, a method for integrating single-cell RNA sequencing data with protein-protein interaction networks to detect active Modules in cells of different transcriptional states. We achieve this by clustering RNA-sequencing data, identifying differentially expressed genes, constructing node-weighted protein-protein interaction networks, and finding the maximum-weight connected subgraphs with an exact Steiner-tree approach. As a case study, we investigate RNA-sequencing data from human liver spheroids but the techniques described here are applicable to other organisms and tissues. scPPIN allows us to expand the output of differential expressed genes analysis with information from protein interactions. We find that different transcriptional states have different subnetworks of the PPIN significantly enriched which represent biological pathways. In these pathways, scPPIN also identifies proteins that are not differentially expressed but of crucial biological function (e.g., as receptors) and therefore reveals biology beyond a standard differentially expressed gene analysis.
Gesine Reinert - One of the best experts on this subject based on the ideXlab platform.
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Functional Module detection through integration of single cell rna sequencing data with protein protein interaction networks
BMC Genomics, 2020Co-Authors: Florian Klimm, Enrique M Toledo, Thomas Monfeuga, Charlotte M. Deane, Fang Zhang, Gesine ReinertAbstract:Recent advances in single-cell RNA sequencing have allowed researchers to explore transcriptional function at a cellular level. In particular, single-cell RNA sequencing reveals that there exist clusters of cells with similar gene expression profiles, representing different transcriptional states. In this study, we present scPPIN, a method for integrating single-cell RNA sequencing data with protein–protein interaction networks that detects active Modules in cells of different transcriptional states. We achieve this by clustering RNA-sequencing data, identifying differentially expressed genes, constructing node-weighted protein–protein interaction networks, and finding the maximum-weight connected subgraphs with an exact Steiner-tree approach. As case studies, we investigate two RNA-sequencing data sets from human liver spheroids and human adipose tissue, respectively. With scPPIN we expand the output of differential expressed genes analysis with information from protein interactions. We find that different transcriptional states have different subnetworks of the protein–protein interaction networks significantly enriched which represent biological pathways. In these pathways, scPPIN identifies proteins that are not differentially expressed but have a crucial biological function (e.g., as receptors) and therefore reveals biology beyond a standard differential expressed gene analysis. The introduced scPPIN method can be used to systematically analyse differentially expressed genes in single-cell RNA sequencing data by integrating it with protein interaction data. The detected Modules that characterise each cluster help to identify and hypothesise a biological function associated to those cells. Our analysis suggests the participation of unexpected proteins in these pathways that are undetectable from the single-cell RNA sequencing data alone. The techniques described here are applicable to other organisms and tissues.
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Functional Module detection through integration of single cell rna sequencing data with protein protein interaction networks
BMC Genomics, 2020Co-Authors: Florian Klimm, Enrique M Toledo, Thomas Monfeuga, Charlotte M. Deane, Fang Zhang, Gesine ReinertAbstract:BACKGROUND Recent advances in single-cell RNA sequencing have allowed researchers to explore transcriptional function at a cellular level. In particular, single-cell RNA sequencing reveals that there exist clusters of cells with similar gene expression profiles, representing different transcriptional states. RESULTS In this study, we present SCPPIN, a method for integrating single-cell RNA sequencing data with protein-protein interaction networks that detects active Modules in cells of different transcriptional states. We achieve this by clustering RNA-sequencing data, identifying differentially expressed genes, constructing node-weighted protein-protein interaction networks, and finding the maximum-weight connected subgraphs with an exact Steiner-tree approach. As case studies, we investigate two RNA-sequencing data sets from human liver spheroids and human adipose tissue, respectively. With SCPPIN we expand the output of differential expressed genes analysis with information from protein interactions. We find that different transcriptional states have different subnetworks of the protein-protein interaction networks significantly enriched which represent biological pathways. In these pathways, SCPPIN identifies proteins that are not differentially expressed but have a crucial biological function (e.g., as receptors) and therefore reveals biology beyond a standard differential expressed gene analysis. CONCLUSIONS The introduced SCPPIN method can be used to systematically analyse differentially expressed genes in single-cell RNA sequencing data by integrating it with protein interaction data. The detected Modules that characterise each cluster help to identify and hypothesise a biological function associated to those cells. Our analysis suggests the participation of unexpected proteins in these pathways that are undetectable from the single-cell RNA sequencing data alone. The techniques described here are applicable to other organisms and tissues.
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Functional Module detection through integration of single cell rna sequencing data with protein protein interaction networks
bioRxiv, 2019Co-Authors: Florian Klimm, Enrique M Toledo, Thomas Monfeuga, Charlotte M. Deane, Fang Zhang, Gesine ReinertAbstract:Recent advances in single-cell RNA sequencing (scRNA-seq) have allowed researchers to explore transcriptional function at a cellular level. In this study, we present scPPIN, a method for integrating single-cell RNA sequencing data with protein-protein interaction networks to detect active Modules in cells of different transcriptional states. We achieve this by clustering RNA-sequencing data, identifying differentially expressed genes, constructing node-weighted protein-protein interaction networks, and finding the maximum-weight connected subgraphs with an exact Steiner-tree approach. As a case study, we investigate RNA-sequencing data from human liver spheroids but the techniques described here are applicable to other organisms and tissues. scPPIN allows us to expand the output of differential expressed genes analysis with information from protein interactions. We find that different transcriptional states have different subnetworks of the PPIN significantly enriched which represent biological pathways. In these pathways, scPPIN also identifies proteins that are not differentially expressed but of crucial biological function (e.g., as receptors) and therefore reveals biology beyond a standard differentially expressed gene analysis.
Aidong Zhang - One of the best experts on this subject based on the ideXlab platform.
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bfo fmd bacterial foraging optimization for Functional Module detection in protein protein interaction networks
Soft Computing, 2018Co-Authors: Cuicui Yang, Aidong ZhangAbstract:Identifying Functional Modules in PPI networks contributes greatly to the understanding of cellular functions and mechanisms. Recently, the swarm intelligence-based approaches have become effective ways for detecting Functional Modules in PPI networks. This paper presents a new computational approach based on bacterial foraging optimization for Functional Module detection in PPI networks (called BFO-FMD). In BFO-FMD, each bacterium represents a candidate Module partition encoded as a directed graph, which is first initialized by a random-walk behavior according to the topological and Functional information between protein nodes. Then, BFO-FMD utilizes four principal biological mechanisms, chemotaxis, conjugation, reproduction, and elimination and dispersal to search for better protein Module partitions. To verify the performance of BFO-FMD, we compared it with several other typical methods on three common yeast datasets. The experimental results demonstrate the excellent performances of BFO-FMD in terms of various evaluation metrics. BFO-FMD achieves outstanding Recall, F-measure, and PPV while performing very well in terms of other metrics. Thus, it can accurately predict protein Modules and help biologists to find some novel biological insights.
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survey Functional Module detection from protein protein interaction networks
IEEE Transactions on Knowledge and Data Engineering, 2014Co-Authors: Aidong Zhang, Chunnian Liu, Xiaomei Quan, Zhijun LiuAbstract:A protein-protein interaction (PPI) network is a biomolecule relationship network that plays an important role in biological activities. Studies of Functional Modules in a PPI network contribute greatly to the understanding of biological mechanism. With the development of life science and computing science, a great amount of PPI data has been acquired by various experimental and computational approaches, which presents a significant challenge of detecting Functional Modules in a PPI network. To address this challenge, many Functional Module detecting methods have been developed. In this survey, we first analyze the existing problems in detecting Functional Modules and discuss the countermeasures in the data preprocess and postprocess. Second, we introduce some special metrics for distance or graph developed in clustering process of proteins. Third, we give a classification system of Functional Module detecting methods and describe some existing detection methods in each category. Fourth, we list databases in common use and conduct performance comparisons of several typical algorithms by popular measurements. Finally, we present the prospects and references for researchers engaged in analyzing PPI networks.
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semantic integration to identify overlapping Functional Modules in protein interaction networks
BMC Bioinformatics, 2007Co-Authors: Young-rae Cho, Woochang Hwang, Murali Ramanathan, Aidong ZhangAbstract:The systematic analysis of protein-protein interactions can enable a better understanding of cellular organization, processes and functions. Functional Modules can be identified from the protein interaction networks derived from experimental data sets. However, these analyses are challenging because of the presence of unreliable interactions and the complex connectivity of the network. The integration of protein-protein interactions with the data from other sources can be leveraged for improving the effectiveness of Functional Module detection algorithms. We have developed novel metrics, called semantic similarity and semantic interactivity, which use Gene Ontology (GO) annotations to measure the reliability of protein-protein interactions. The protein interaction networks can be converted into a weighted graph representation by assigning the reliability values to each interaction as a weight. We presented a flow-based modularization algorithm to efficiently identify overlapping Modules in the weighted interaction networks. The experimental results show that the semantic similarity and semantic interactivity of interacting pairs were positively correlated with Functional co-occurrence. The effectiveness of the algorithm for identifying Modules was evaluated using Functional categories from the MIPS database. We demonstrated that our algorithm had higher accuracy compared to other competing approaches. The integration of protein interaction networks with GO annotation data and the capability of detecting overlapping Modules substantially improve the accuracy of Module identification.
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a novel Functional Module detection algorithm for protein protein interaction networks
Algorithms for Molecular Biology, 2006Co-Authors: Woochang Hwang, Aidong Zhang, Young-rae Cho, Murali RamanathanAbstract:The sparse connectivity of protein-protein interaction data sets makes identification of Functional Modules challenging. The purpose of this study is to critically evaluate a novel clustering technique for clustering and detecting Functional Modules in protein-protein interaction networks, termed STM. STM selects representative proteins for each cluster and iteratively refines clusters based on a combination of the signal transduced and graph topology. STM is found to be effective at detecting clusters with a diverse range of interaction structures that are significant on measures of biological relevance. The STM approach is compared to six competing approaches including the maximum clique, quasi-clique, minimum cut, betweeness cut and Markov Clustering (MCL) algorithms. The clusters obtained by each technique are compared for enrichment of biological function. STM generates larger clusters and the clusters identified have p-values that are approximately 125-fold better than the other methods on biological function. An important strength of STM is that the percentage of proteins that are discarded to create clusters is much lower than the other approaches. STM outperforms competing approaches and is capable of effectively detecting both densely and sparsely connected, biologically relevant Functional Modules with fewer discards.
Thomas Monfeuga - One of the best experts on this subject based on the ideXlab platform.
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Functional Module detection through integration of single cell rna sequencing data with protein protein interaction networks
BMC Genomics, 2020Co-Authors: Florian Klimm, Enrique M Toledo, Thomas Monfeuga, Charlotte M. Deane, Fang Zhang, Gesine ReinertAbstract:Recent advances in single-cell RNA sequencing have allowed researchers to explore transcriptional function at a cellular level. In particular, single-cell RNA sequencing reveals that there exist clusters of cells with similar gene expression profiles, representing different transcriptional states. In this study, we present scPPIN, a method for integrating single-cell RNA sequencing data with protein–protein interaction networks that detects active Modules in cells of different transcriptional states. We achieve this by clustering RNA-sequencing data, identifying differentially expressed genes, constructing node-weighted protein–protein interaction networks, and finding the maximum-weight connected subgraphs with an exact Steiner-tree approach. As case studies, we investigate two RNA-sequencing data sets from human liver spheroids and human adipose tissue, respectively. With scPPIN we expand the output of differential expressed genes analysis with information from protein interactions. We find that different transcriptional states have different subnetworks of the protein–protein interaction networks significantly enriched which represent biological pathways. In these pathways, scPPIN identifies proteins that are not differentially expressed but have a crucial biological function (e.g., as receptors) and therefore reveals biology beyond a standard differential expressed gene analysis. The introduced scPPIN method can be used to systematically analyse differentially expressed genes in single-cell RNA sequencing data by integrating it with protein interaction data. The detected Modules that characterise each cluster help to identify and hypothesise a biological function associated to those cells. Our analysis suggests the participation of unexpected proteins in these pathways that are undetectable from the single-cell RNA sequencing data alone. The techniques described here are applicable to other organisms and tissues.
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Functional Module detection through integration of single cell rna sequencing data with protein protein interaction networks
BMC Genomics, 2020Co-Authors: Florian Klimm, Enrique M Toledo, Thomas Monfeuga, Charlotte M. Deane, Fang Zhang, Gesine ReinertAbstract:BACKGROUND Recent advances in single-cell RNA sequencing have allowed researchers to explore transcriptional function at a cellular level. In particular, single-cell RNA sequencing reveals that there exist clusters of cells with similar gene expression profiles, representing different transcriptional states. RESULTS In this study, we present SCPPIN, a method for integrating single-cell RNA sequencing data with protein-protein interaction networks that detects active Modules in cells of different transcriptional states. We achieve this by clustering RNA-sequencing data, identifying differentially expressed genes, constructing node-weighted protein-protein interaction networks, and finding the maximum-weight connected subgraphs with an exact Steiner-tree approach. As case studies, we investigate two RNA-sequencing data sets from human liver spheroids and human adipose tissue, respectively. With SCPPIN we expand the output of differential expressed genes analysis with information from protein interactions. We find that different transcriptional states have different subnetworks of the protein-protein interaction networks significantly enriched which represent biological pathways. In these pathways, SCPPIN identifies proteins that are not differentially expressed but have a crucial biological function (e.g., as receptors) and therefore reveals biology beyond a standard differential expressed gene analysis. CONCLUSIONS The introduced SCPPIN method can be used to systematically analyse differentially expressed genes in single-cell RNA sequencing data by integrating it with protein interaction data. The detected Modules that characterise each cluster help to identify and hypothesise a biological function associated to those cells. Our analysis suggests the participation of unexpected proteins in these pathways that are undetectable from the single-cell RNA sequencing data alone. The techniques described here are applicable to other organisms and tissues.
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Functional Module detection through integration of single cell rna sequencing data with protein protein interaction networks
bioRxiv, 2019Co-Authors: Florian Klimm, Enrique M Toledo, Thomas Monfeuga, Charlotte M. Deane, Fang Zhang, Gesine ReinertAbstract:Recent advances in single-cell RNA sequencing (scRNA-seq) have allowed researchers to explore transcriptional function at a cellular level. In this study, we present scPPIN, a method for integrating single-cell RNA sequencing data with protein-protein interaction networks to detect active Modules in cells of different transcriptional states. We achieve this by clustering RNA-sequencing data, identifying differentially expressed genes, constructing node-weighted protein-protein interaction networks, and finding the maximum-weight connected subgraphs with an exact Steiner-tree approach. As a case study, we investigate RNA-sequencing data from human liver spheroids but the techniques described here are applicable to other organisms and tissues. scPPIN allows us to expand the output of differential expressed genes analysis with information from protein interactions. We find that different transcriptional states have different subnetworks of the PPIN significantly enriched which represent biological pathways. In these pathways, scPPIN also identifies proteins that are not differentially expressed but of crucial biological function (e.g., as receptors) and therefore reveals biology beyond a standard differentially expressed gene analysis.
Charlotte M. Deane - One of the best experts on this subject based on the ideXlab platform.
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Functional Module detection through integration of single cell rna sequencing data with protein protein interaction networks
BMC Genomics, 2020Co-Authors: Florian Klimm, Enrique M Toledo, Thomas Monfeuga, Charlotte M. Deane, Fang Zhang, Gesine ReinertAbstract:Recent advances in single-cell RNA sequencing have allowed researchers to explore transcriptional function at a cellular level. In particular, single-cell RNA sequencing reveals that there exist clusters of cells with similar gene expression profiles, representing different transcriptional states. In this study, we present scPPIN, a method for integrating single-cell RNA sequencing data with protein–protein interaction networks that detects active Modules in cells of different transcriptional states. We achieve this by clustering RNA-sequencing data, identifying differentially expressed genes, constructing node-weighted protein–protein interaction networks, and finding the maximum-weight connected subgraphs with an exact Steiner-tree approach. As case studies, we investigate two RNA-sequencing data sets from human liver spheroids and human adipose tissue, respectively. With scPPIN we expand the output of differential expressed genes analysis with information from protein interactions. We find that different transcriptional states have different subnetworks of the protein–protein interaction networks significantly enriched which represent biological pathways. In these pathways, scPPIN identifies proteins that are not differentially expressed but have a crucial biological function (e.g., as receptors) and therefore reveals biology beyond a standard differential expressed gene analysis. The introduced scPPIN method can be used to systematically analyse differentially expressed genes in single-cell RNA sequencing data by integrating it with protein interaction data. The detected Modules that characterise each cluster help to identify and hypothesise a biological function associated to those cells. Our analysis suggests the participation of unexpected proteins in these pathways that are undetectable from the single-cell RNA sequencing data alone. The techniques described here are applicable to other organisms and tissues.
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Functional Module detection through integration of single cell rna sequencing data with protein protein interaction networks
BMC Genomics, 2020Co-Authors: Florian Klimm, Enrique M Toledo, Thomas Monfeuga, Charlotte M. Deane, Fang Zhang, Gesine ReinertAbstract:BACKGROUND Recent advances in single-cell RNA sequencing have allowed researchers to explore transcriptional function at a cellular level. In particular, single-cell RNA sequencing reveals that there exist clusters of cells with similar gene expression profiles, representing different transcriptional states. RESULTS In this study, we present SCPPIN, a method for integrating single-cell RNA sequencing data with protein-protein interaction networks that detects active Modules in cells of different transcriptional states. We achieve this by clustering RNA-sequencing data, identifying differentially expressed genes, constructing node-weighted protein-protein interaction networks, and finding the maximum-weight connected subgraphs with an exact Steiner-tree approach. As case studies, we investigate two RNA-sequencing data sets from human liver spheroids and human adipose tissue, respectively. With SCPPIN we expand the output of differential expressed genes analysis with information from protein interactions. We find that different transcriptional states have different subnetworks of the protein-protein interaction networks significantly enriched which represent biological pathways. In these pathways, SCPPIN identifies proteins that are not differentially expressed but have a crucial biological function (e.g., as receptors) and therefore reveals biology beyond a standard differential expressed gene analysis. CONCLUSIONS The introduced SCPPIN method can be used to systematically analyse differentially expressed genes in single-cell RNA sequencing data by integrating it with protein interaction data. The detected Modules that characterise each cluster help to identify and hypothesise a biological function associated to those cells. Our analysis suggests the participation of unexpected proteins in these pathways that are undetectable from the single-cell RNA sequencing data alone. The techniques described here are applicable to other organisms and tissues.
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Functional Module detection through integration of single cell rna sequencing data with protein protein interaction networks
bioRxiv, 2019Co-Authors: Florian Klimm, Enrique M Toledo, Thomas Monfeuga, Charlotte M. Deane, Fang Zhang, Gesine ReinertAbstract:Recent advances in single-cell RNA sequencing (scRNA-seq) have allowed researchers to explore transcriptional function at a cellular level. In this study, we present scPPIN, a method for integrating single-cell RNA sequencing data with protein-protein interaction networks to detect active Modules in cells of different transcriptional states. We achieve this by clustering RNA-sequencing data, identifying differentially expressed genes, constructing node-weighted protein-protein interaction networks, and finding the maximum-weight connected subgraphs with an exact Steiner-tree approach. As a case study, we investigate RNA-sequencing data from human liver spheroids but the techniques described here are applicable to other organisms and tissues. scPPIN allows us to expand the output of differential expressed genes analysis with information from protein interactions. We find that different transcriptional states have different subnetworks of the PPIN significantly enriched which represent biological pathways. In these pathways, scPPIN also identifies proteins that are not differentially expressed but of crucial biological function (e.g., as receptors) and therefore reveals biology beyond a standard differentially expressed gene analysis.
