Narein Rao

2023

1. arxiv Preprint

   Rao, Narein, Pai, Rhea, Mishra, Archita, Ginhoux, Florent, Chan, Jerry, Sharma, Ankur, Zafar, Hamim

   2023

   Abs PDF Code

   The advancement of single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics has made it possible to infer interactions amongst heterogeneous cells and their surrounding cellular environments. Existing methods assist in the analysis of ligand-receptor interactions by either adding spatial information to the currently available scRNA-seq data or utilizing spot-level or high-resolution spatial transcriptomics data. However, till date, there is a lack of methods capable of mapping ligand-target interactions across a spatial topology with specific cell type composition, with the potential to shed further light on the niche-specific relationship between ligands and their downstream targets. Here we present Renoir for charting the ligand-target activities across a spatial topology and delineating spatial communication niches harboring specific ligand-target activities and cell type composition. Renoir can also spatially map pathway-level aggregate activity of ligand-target gene sets and identify domain-specific activities between ligands and targets. We applied Renoir to three spatial datasets ranging from development to disease to demonstrate its effectiveness in inferring cellular niches with distinct ligand-target interactions, spatially mapping hallmark pathway activities, ranking ligand activity across spatial niches, and visualizing overall cell type-specific, ligand-target interactions in spatial niches.

2022

1. findings-EMNLP Conference

   TransLIST: A Transformer-Based Linguistically Informed Sanskrit Tokenizer

   Sandhan, Jivnesh, Singha, Rathin, Rao, Narein, Samanta, Suvendu, Behera, Laxmidhar, Goyal, Pawan

   Findings of the Association for Computational Linguistics: EMNLP 2022

   Abs PDF Code

   Sanskrit Word Segmentation (SWS) is essential in making digitized texts available and in deploying downstream tasks. It is, however, non-trivial because of the sandhi phenomenon that modifies the characters at the word boundaries, and needs special treatment. Existing lexicon driven approaches for SWS make use of Sanskrit Heritage Reader, a lexicon-driven shallow parser, to generate the complete candidate solution space, over which various methods are applied to produce the most valid solution. However, these approaches fail while encountering out-of-vocabulary tokens. On the other hand, purely engineering methods for SWS have made use of recent advances in deep learning, but cannot make use of the latent word information on availability. To mitigate the shortcomings of both families of approaches, we propose Transformer based Linguistically Informed Sanskrit Tokenizer (TransLIST) consisting of (1) a module that encodes the character input along with latent-word information, which takes into account the sandhi phenomenon specific to SWS and is apt to work with partial or no candidate solutions, (2) a novel soft-masked attention to prioritize potential candidate words and (3) a novel path ranking algorithm to rectify the corrupted predictions. Experiments on the benchmark datasets for SWS show that TransLIST outperforms the current state-of-the-art system by an average 7.2 points absolute gain in terms of perfect match (PM) metric.