Indian Agricultural Statistics Research Institute

About: Indian Agricultural Statistics Research Institute is a facility organization based out in New Delhi, India. It is known for research contribution in the topics: Population & Small area estimation. The organization has 454 authors who have published 870 publications receiving 7987 citations.

Stratified Inverse Adaptive Cluster Sampling

Abstract: In forestry and environmental sciences, some species of plants and animals are rare and clustered, i.e., abundance of zeros. The traditional sampling methods provide poor estimates of the population mean/total. In such situations, adaptive sampling is useful. In traditional stratified sampling, similar units are grouped a priori into strata, based on prior information about the population. But within a stratum, the population is rare and clumped.

OMICS Tools and Techniques for Study of Defense Mechanism in Plants

Abstract: In recent times, agriculturally important plants face increasing challenges in maintaining productivity, disease control, and welfare of farmers with changing climatic conditions. To accomplish this, the generation and analysis of large volumes of data, especially in the emerging “OMICS” areas of genomics, proteomics, and bioinformatics, is imperative for decision-making over large volumes of data with respect to various crops. Analysis of this large amount of diverged data needs specific tools and techniques. There are various tools and techniques available for the analysis of such data. In this chapter, a detailed discussion on omics data analysis related tools and techniques have been made. This chapter provides a single platform to help the various researchers working in different domains of omics research for analyzing the data.

Learning Patterns from Clusters Using Reduct

Abstract: Usual clustering algorithms just generate general description of the clusters like which entities are member of each cluster and lacks in generating cluster description in the form of pattern. Pattern is defined as a logical statement describing a cluster structure in terms of relevant attributes. In the proposed approach reduct from rough set theory is employed to generate pattern. Reduct is defined as the set of attributes which distinguishes the entities in a homogenous cluster, therefore these can be clear cut removed from the same. Remaining attributes are ranked for their contribution in the cluster. Cluster description is then formed by conjunction of most contributing attributes. Proposed approach is demonstrated using benchmarking mushroom dataset from UCI repository.

Statistical Approach of Gene Set Analysis with Quantitative Trait Loci for Crop Gene Expression Studies

Abstract: Genome-wide expression study is a powerful genomic technology to quantify expression dynamics of genes in a genome. In gene expression study, gene set analysis has become the first choice to gain insights into the underlying biology of diseases or stresses in plants. It also reduces the complexity of statistical analysis and enhances the explanatory power of the obtained results from the primary downstream differential expression analysis. The gene set analysis approaches are well developed in microarrays and RNA-seq gene expression data analysis. These approaches mainly focus on analyzing the gene sets with gene ontology or pathway annotation data. However, in plant biology, such methods may not establish any formal relationship between the genotypes and the phenotypes, as most of the traits are quantitative and controlled by polygenes. The existing Quantitative Trait Loci (QTL)-based gene set analysis approaches only focus on the over-representation analysis of the selected genes while ignoring their associated gene scores. Therefore, we developed an innovative statistical approach, GSQSeq, to analyze the gene sets with trait enriched QTL data. This approach considers the associated differential expression scores of genes while analyzing the gene sets. The performance of the developed method was tested on five different crop gene expression datasets obtained from real crop gene expression studies. Our analytical results indicated that the trait-specific analysis of gene sets was more robust and successful through the proposed approach than existing techniques. Further, the developed method provides a valuable platform for integrating the gene expression data with QTL data.