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Anirban Maitra

Publications and source records attributed to Anirban Maitra.

5 recordsLinked to original sources
PubMed2026

Pancreatic ductal adenocarcinoma: The Vision of Heracles.

Pancreatic ductal adenocarcinoma (PDAC) remains among the deadliest malignancies, as tumors evolve faster than therapies. Resistance is ecological, not merely KRAS driven, involving overlooked players like high-grade pancreatic intraepithelial neoplasias (PanINs), peripancreatic fat, stromal mechanics, myeloid-neural circuits, metabolic rewiring, and systemic host responses. We propose precision interception targeting PanIN/intraductal papillary mucinous neoplasm (IPMN) biology, spatial-functional-proteogenomic classification beyond transcriptomics, the Heracles Protocol (measure, prime, strike, and adapt), and integrated technologies from AI pathology to exosomal delivery and CRISPR-based synergy mapping, together making PDAC more tractable.

Humans↗
PubMed2025

PoweREST: Statistical power estimation for spatial transcriptomics experiments to detect differentially expressed genes between two conditions.

Recent advancements in spatial transcriptomics (ST) have significantly enhanced biological research in various domains. However, the high cost for current ST data generation techniques restricts the large-scale application of ST. Consequently, maximization of the use of available resources to achieve robust statistical power for ST data is a pressing need. One fundamental question in ST analysis is detection of differentially expressed genes (DEGs) under different conditions using ST data. Such DEG analyses are performed frequently, but their power calculations are rarely discussed in the literature. To address this gap, we developed PoweREST, a power estimation tool designed to support the power calculation for DEG detection with 10X Genomics Visium data. PoweREST enables power estimation both before any ST experiments and after preliminary data are collected, making it suitable for a wide variety of power analyses in ST studies. We also provide a user-friendly, program-free web application that allows users to interactively calculate and visualize study power along with relevant parameters.

Gene Expression Profiling↗
PubMed2024

PoweREST: Statistical Power Estimation for Spatial Transcriptomics Experiments to Detect Differentially Expressed Genes Between Two Conditions.

Recent advancements in Spatial Transcriptomics (ST) have significantly enhanced biological research in various domains. However, the high cost of current ST data generation techniques restricts its application in large-scale population studies. Consequently, there is a pressing need to maximize the use of available resources to achieve robust statistical power. One fundamental question in ST analysis is to detect differentially expressed genes (DEGs) among different conditions using ST data. Such DEG analysis is often performed but the associated power calculation is rarely discussed in the literature. To address this gap, we introduce, PoweREST (https://github.com/lanshui98/PoweREST), a power estimation tool designed to support power calculation of DEG detection with 10X Genomics Visium data. PoweREST enables power estimation both before any ST experiments or after preliminary data are collected, making it suitable for a wide variety of power analyses in ST studies. We also provide a user-friendly, program-free web application (https://lanshui.shinyapps.io/PoweREST/), allowing users to interactively calculate and visualize the study power along with relevant the parameters.

Differentially expressed genes↗