Categories

• papers 27
• paper 26
• protocols 9
• protocol 9
• misc 2
• home 1
• data 2
• chapter 1
• team 10
• news 12
• projects 4
• project 4

papers

• McImpute : Matrix Completion Based Imputation for Single Cell RNA-seq Data
• Texture Classification Using Deep Convolutional Neural Network with Ensemble Learning
• Discovery of rare cells from voluminous single cell expression data
• AutoImpute : Autoencoder based imputation of single-cell RNA-seq data
• Structure aware Principal Component Analysis for single cell RNA-seq data
• CellAtlasSearch: a scalable search engine for single cells
• dropClust: Efficient clustering of ultra-large scRNA-seq data
• Reference component analysis of single-cell transcriptomes elucidates cellular heterogeneity in human colorectal tumors
• ParSel: Parallel Selection of Micro‐RNAs for Survival Classification in Cancers
• A scoring scheme for online feature selection: simulating model performance without retraining
• FORKS: Finding Orderings Robustly using k-means and Steiner trees
• Tumor-derived circulating endothelial cell clusters in colorectal cancer
• Fast, scalable and accurate differential expression analysis for single cells
• Pathway Informatics
• FOCS: Fast Overlapped Community Search
• Feature selection using feature dissimilarity measure and density-based clustering: Application to biological data
• Weighted Markov Chain Based Aggregation of Biomolecule Orderings
• Strong Nash Stability based Approach to Minimum Quasi Clique Partitioning
• Determining a relative importance among ordered lists
• XRDS Mobilizes
• GRF: a greedy rank fusion algorithm for combining microRNA target orderings
• Reformulated Kemeny Optimal Aggregation with Application in Consensus Ranking of microRNA Targets
• Topological patterns in microRNA–gene regulatory network: studies in colorectal and breast cancer
• Score based aggregation of microRNA target orderings
• Participation of microRNAs in human interactome: extraction of microRNA–microRNA regulations
• Entropy steered Kendall's tau measure for a fair Rank Aggregation
• A novel measure for evaluating an ordered list: application in microRNA target prediction

paper

• McImpute : Matrix Completion Based Imputation for Single Cell RNA-seq Data
• Texture Classification Using Deep Convolutional Neural Network with Ensemble Learning
• Discovery of rare cells from voluminous single cell expression data
• AutoImpute : Autoencoder based imputation of single-cell RNA-seq data
• Structure aware Principal Component Analysis for single cell RNA-seq data
• CellAtlasSearch: a scalable search engine for single cells
• dropClust: Efficient clustering of ultra-large scRNA-seq data
• Reference component analysis of single-cell transcriptomes elucidates cellular heterogeneity in human colorectal tumors
• ParSel: Parallel Selection of Micro‐RNAs for Survival Classification in Cancers
• A scoring scheme for online feature selection: simulating model performance without retraining
• FORKS: Finding Orderings Robustly using k-means and Steiner trees
• Tumor-derived circulating endothelial cell clusters in colorectal cancer
• Fast, scalable and accurate differential expression analysis for single cells
• FOCS: Fast Overlapped Community Search
• Feature selection using feature dissimilarity measure and density-based clustering: Application to biological data
• Weighted Markov Chain Based Aggregation of Biomolecule Orderings
• Strong Nash Stability based Approach to Minimum Quasi Clique Partitioning
• Determining a relative importance among ordered lists
• XRDS Mobilizes
• GRF: a greedy rank fusion algorithm for combining microRNA target orderings
• Reformulated Kemeny Optimal Aggregation with Application in Consensus Ranking of microRNA Targets
• Topological patterns in microRNA–gene regulatory network: studies in colorectal and breast cancer
• Score based aggregation of microRNA target orderings
• Participation of microRNAs in human interactome: extraction of microRNA–microRNA regulations
• Entropy steered Kendall's tau measure for a fair Rank Aggregation
• A novel measure for evaluating an ordered list: application in microRNA target prediction

protocols

• LSPCA: Structure preserving principal component analysis
• FiRE: Fast rare cell discovery from single cell RNA-Seq
• CellAtlasSearch: Single cell search engine
• dropClust: Efficient clustering of Drop-seq data
• ParSel: Feature selection for improved survival prediction
• RCA: Noise free single cell clustering (Shyam Prabhakar’s lab)
• OFS: Online feature selection
• FOCS: Overlapping graph clustering
• RA: Kemeny Optimal Rank Aggregation

protocol

• LSPCA: Structure preserving principal component analysis
• FiRE: Fast rare cell discovery from single cell RNA-Seq
• CellAtlasSearch: Single cell search engine
• dropClust: Efficient clustering of Drop-seq data
• ParSel: Feature selection for improved survival prediction
• RCA: Noise free single cell clustering (Shyam Prabhakar’s lab)
• OFS: Online feature selection
• FOCS: Overlapping graph clustering
• RA: Kemeny Optimal Rank Aggregation

misc

• About
• The Sengupta Lab

home

• The Sengupta Lab

data

• Disaggregation assay data visualization
• Aggregation assay data visualization

chapter

• Pathway Informatics

team

• Arvind Iyer
• Shivam Sharma
• Ayushi Gupta
• Shreya Mishra
• Chitrita Goswami
• Smriti Chawla
• Sarita Poonia
• Priya Rai
• Krishan Gupta
• Debarka Sengupta

news

• indiabioscience.org: 'FiRE: Finding a needle in the haystack'
• researchmatters.in: 'Finding the odd cell out'
• jansatta.com: 'Unique algorithm of blood sample investigation'
• The Hindu: 'Delhi researchers develop an algorithm to detect rare cells'
• FiRE, discovering rare cells
• Autoimpute, autoencoder based imputation technique
• New Member in Lab
• CellAtlasSearch
• Sengupta Lab partners with Circle of Life Health Care Pvt. Ltd.
• DropClust, speediest clustering algorithm
• Brooking India Consortium
• Sciencedaily

projects

• Rapid rare cell discovery
• Label-free, in silico detection of Circulating Tumor Cells (CTCs)
• GPU accelerated single cell search
• LSH for clustering ultra-large Drop-seq data

project

• Rapid rare cell discovery
• Label-free, in silico detection of Circulating Tumor Cells (CTCs)
• GPU accelerated single cell search
• LSH for clustering ultra-large Drop-seq data