Effective Clustering of scRNA-seq Data to Identify Biomarkers without User Input Hussain A. Chowdhury 35th Aaai Conference on Artificial Intelligence Aaai 2021, 2021 Clustering unleashes the power of scRNA-seq through identification of appropriate cell groups. It is considered a pre-requisite to performing differential expression analysis, followed by functional profiling to identify potential biomarkers from scRNA-seq data. Most existing clustering methods either integrate cluster validity indices or need user assistance to identify clusters of arbitrary shape. We develop two clustering methods 1) UIFDBC to identify clusters of arbitrary shapes, 2) UIPBC to cluster scRNA-seq data. Neither method integrates a cluster validity index nor takes any user input. However, specialised approaches are used to benchmark the parameters. Both approaches outperform state-of-the-art methods.
(Differential) Co-Expression Analysis of Gene Expression: A Survey of Best Practices Hussain Ahmed Chowdhury, Dhruba Kumar Bhattacharyya, Jugal K. Kalita IEEE ACM Transactions on Computational Biology and Bioinformatics, 2020 Analysis of gene expression data is widely used in transcriptomic studies to understand functions of molecules inside a cell and interactions among molecules. Differential co-expression analysis studies diseases and phenotypic variations by finding modules of genes whose co-expression patterns vary across conditions. We review the best practices in gene expression data analysis in terms of analysis of (differential) co-expression, co-expression network, differential networking, and differential connectivity considering both microarray and RNA-seq data along with comparisons. We highlight hurdles in RNA-seq data analysis using methods developed for microarrays. We include discussion of necessary tools for gene expression analysis throughout the paper. In addition, we shed light on scRNA-seq data analysis by including preprocessing and scRNA-seq in co-expression analysis along with useful tools specific to scRNA-seq. To get insights, biological interpretation and functional profiling is included. Finally, we provide guidelines for the analyst, along with research issues and challenges which should be addressed.
Differential Expression Analysis of RNA-seq Reads: Overview, Taxonomy, and Tools Hussain Ahmed Chowdhury, Dhruba Kumar Bhattacharyya, Jugal K. Kalita IEEE ACM Transactions on Computational Biology and Bioinformatics, 2020 Analysis of RNA-sequence (RNA-seq) data is widely used in transcriptomic studies and it has many applications. We review RNA-seq data analysis from RNA-seq reads to the results of differential expression analysis. In addition, we perform a descriptive comparison of tools used in each step of RNA-seq data analysis along with a discussion of important characteristics of these tools. A taxonomy of tools is also provided. A discussion of issues in quality control and visualization of RNA-seq data is also included along with useful tools. Finally, we provide some guidelines for the RNA-seq data analyst, along with research issues and challenges which should be addressed.
Integrative network analysis identifies differential regulation of neuroimmune system in Schizophrenia and Bipolar disorder Ankur Sahu, Hussain Ahmed Chowdhury, Mithil Gaikwad, Chen Chongtham, Uddip Talukdar, Jadab Kishor Phukan, Dhruba Kumar Bhattacharyya, Pankaj Barah Brain Behavior and Immunity Health, 2020 Background: Neuropsychiatric disorders such as Schizophrenia (SCZ) and Bipolar disorder (BPD) pose a broad range of problems with different symptoms mainly characterized by some combination of abnormal thoughts, emotions, behaviour, etc. However, in depth molecular and pathophysiological mechanisms among different neuropsychiatric disorders have not been clearly understood yet. We have used RNA-seq data to investigate unique and overlapping molecular signatures between SCZ and BPD using an integrative network biology approach. Methods: RNA-seq count data were collected from NCBI-GEO database generated on post-mortem brain tissues of controls (n = 24) and patients of BPD (n = 24) and SCZ (n = 24). Differentially expressed genes (DEGs) were identified using the consensus of DESeq2 and edgeR tools and used for downstream analysis. Weighted gene correlation networks were constructed to find non-preserved (NP) modules for SCZ, BPD and control conditions. Topological analysis and functional enrichment analysis were performed on NP modules to identify unique and overlapping expression signatures during SCZ and BPD conditions. Results: We have identified four NP modules from the DEGs of BPD and SCZ. Eleven overlapping genes have been identified between SCZ and BPD networks, and they were found to be highly enriched in inflammatory responses. Among these eleven genes, TNIP2, TNFRSF1A and AC005840.1 had higher sum of connectivity exclusively in BPD network. In addition, we observed that top five genes of NP module from SCZ network were downregulated which may be a key factor for SCZ disorder. Conclusions: Differential activation of the immune system components and pathways may drive the common and unique pathogenesis of the BPD and SCZ.
UIPBC: An effective clustering for scRNA-seq data analysis without user input HA Chowdhury, DK Bhattacharyya, JK Kalita Knowledge-Based Systems 248, 108767 , 2022 2022 Citations: 2
UIFDBC: Effective density based clustering to find clusters of arbitrary shapes without user input HA Chowdhury, DK Bhattacharyya, JK Kalita Expert Systems with Applications 186, 115746 , 2021 2021 Citations: 20
Identification of potential Parkinson’s disease biomarkers using computational biology approaches HA Chowdhury, P Barah, DK Bhattacharyya, JK Kalita Network Modeling Analysis in Health Informatics and Bioinformatics 10 (1), 10 , 2021 2021 Citations: 2
UICPC: Centrality-based clustering for scRNA-seq data analysis without user input HA Chowdhury, DK Bhattacharyya, JK Kalita Computers in Biology and Medicine 137, 104820 , 2021 2021 Citations: 10
Effective clustering of scRNA-seq data to identify biomarkers without user input HA Chowdhury Proceedings of the AAAI Conference on Artificial Intelligence 35 (18), 15710 … , 2021 2021 Citations: 18
Integrative network analysis identifies differential regulation of neuroimmune system in Schizophrenia and Bipolar disorder A Sahu, HA Chowdhury, M Gaikwad, C Chongtham, U Talukdar, ... Brain, Behavior, & Immunity-Health 2, 100023 , 2020 2020 Citations: 23
NCBI: a novel correlation based imputing technique using biclustering HA Chowdhury, HA Ahmed, DK Bhattacharyya, JK Kalita Computational Intelligence in Pattern Recognition: Proceedings of CIPR 2019 … , 2019 2019 Citations: 6
(Differential) co-expression analysis of gene expression: a survey of best practices HA Chowdhury, DK Bhattacharyya, JK Kalita IEEE/ACM transactions on computational biology and bioinformatics 17 (4 … , 2019 2019 Citations: 80
Differential expression analysis of RNA-seq reads: overview, taxonomy, and tools HA Chowdhury, DK Bhattacharyya, JK Kalita IEEE/ACM transactions on computational biology and bioinformatics 17 (2 … , 2018 2018 Citations: 57
Plagiarism: Taxonomy, tools and detection techniques HA Chowdhury, DK Bhattacharyya arXiv preprint arXiv:1801.06323 , 2018 2018 Citations: 94
Plagiarism: Taxonomy HA Chowdhury, DK Bhattacharyya Tools and Detection Techniques. arXiv , 2018 2018 Citations: 8
mRMR+: An effective feature selection algorithm for classification HA Chowdhury, DK Bhattacharyya International Conference on Pattern Recognition and Machine Intelligence … , 2017 2017 Citations: 5
MOST CITED SCHOLAR PUBLICATIONS
Plagiarism: Taxonomy, tools and detection techniques HA Chowdhury, DK Bhattacharyya arXiv preprint arXiv:1801.06323 , 2018 2018 Citations: 94
(Differential) co-expression analysis of gene expression: a survey of best practices HA Chowdhury, DK Bhattacharyya, JK Kalita IEEE/ACM transactions on computational biology and bioinformatics 17 (4 … , 2019 2019 Citations: 80
Differential expression analysis of RNA-seq reads: overview, taxonomy, and tools HA Chowdhury, DK Bhattacharyya, JK Kalita IEEE/ACM transactions on computational biology and bioinformatics 17 (2 … , 2018 2018 Citations: 57
Integrative network analysis identifies differential regulation of neuroimmune system in Schizophrenia and Bipolar disorder A Sahu, HA Chowdhury, M Gaikwad, C Chongtham, U Talukdar, ... Brain, Behavior, & Immunity-Health 2, 100023 , 2020 2020 Citations: 23
UIFDBC: Effective density based clustering to find clusters of arbitrary shapes without user input HA Chowdhury, DK Bhattacharyya, JK Kalita Expert Systems with Applications 186, 115746 , 2021 2021 Citations: 20
Effective clustering of scRNA-seq data to identify biomarkers without user input HA Chowdhury Proceedings of the AAAI Conference on Artificial Intelligence 35 (18), 15710 … , 2021 2021 Citations: 18
UICPC: Centrality-based clustering for scRNA-seq data analysis without user input HA Chowdhury, DK Bhattacharyya, JK Kalita Computers in Biology and Medicine 137, 104820 , 2021 2021 Citations: 10
Plagiarism: Taxonomy HA Chowdhury, DK Bhattacharyya Tools and Detection Techniques. arXiv , 2018 2018 Citations: 8
NCBI: a novel correlation based imputing technique using biclustering HA Chowdhury, HA Ahmed, DK Bhattacharyya, JK Kalita Computational Intelligence in Pattern Recognition: Proceedings of CIPR 2019 … , 2019 2019 Citations: 6
mRMR+: An effective feature selection algorithm for classification HA Chowdhury, DK Bhattacharyya International Conference on Pattern Recognition and Machine Intelligence … , 2017 2017 Citations: 5
UIPBC: An effective clustering for scRNA-seq data analysis without user input HA Chowdhury, DK Bhattacharyya, JK Kalita Knowledge-Based Systems 248, 108767 , 2022 2022 Citations: 2
Identification of potential Parkinson’s disease biomarkers using computational biology approaches HA Chowdhury, P Barah, DK Bhattacharyya, JK Kalita Network Modeling Analysis in Health Informatics and Bioinformatics 10 (1), 10 , 2021 2021 Citations: 2
