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bioc
multiHiCcompare:Normalize and detect differences between Hi-C datasets when replicates of each experimental condition are available
multiHiCcompare provides functions for joint normalization and difference detection in multiple Hi-C datasets. This extension of the original HiCcompare package now allows for Hi-C experiments with more than 2 groups and multiple samples per group. multiHiCcompare operates on processed Hi-C data in the form of sparse upper triangular matrices. It accepts four column (chromosome, region1, region2, IF) tab-separated text files storing chromatin interaction matrices. multiHiCcompare provides cyclic loess and fast loess (fastlo) methods adapted to jointly normalizing Hi-C data. Additionally, it provides a general linear model (GLM) framework adapting the edgeR package to detect differences in Hi-C data in a distance dependent manner.
Maintained by Mikhail Dozmorov. Last updated 5 months ago.
softwarehicsequencingnormalization
9 stars 7.30 score 37 scripts 2 dependents
ambarish-chattopadhyay
FSM:Finite Selection Model
Randomized and balanced allocation of units to treatment groups using the Finite Selection Model (FSM). The FSM was originally proposed and developed at the RAND corporation by Carl Morris to enhance the experimental design for the now famous Health Insurance Experiment. See Morris (1979) <doi:10.1016/0304-4076(79)90053-8> for details on the original version of the FSM.
Maintained by Ambarish Chattopadhyay. Last updated 4 years ago.
2.00 score 3 scripts
