Many researchers are investigating the possibility of utilizing global gene expression profile data as a platform to infer gene regulatory networks. However, heavy computational burden and measurement noises render these efforts difficult and approaches based on quantized levels are vigorously investigated as an alternative. Methods based on quantized values require a procedure to convert continuous expression values into discrete ones. Although there have been algorithms to quantize values into multiple discrete states, these algorithms assumed strict state mixtures (SSM) so that all expression profiles were divided into pre-specified number of states. We propose two novel quantization algorithms (QAs), model-based quantization algorithm and model-free quantization algorithm, that generalize SSM algorithms in two major aspects. First, our QAs assume the maximum number of expression states (Es) be arbitrary. Second, expression profiles can exhibit any combinations of Es possible states. In this paper, we compare the performances between SSM algorithms and QAs using simulation studies as well as applications to actual data and show that quantizing gene expression data using adaptive algorithms is an effective way to reduce data complexity without sacrificing much of essential information.