Raman spectroscopy has previously been used to identify eukaryotic and prokaryotic cells. While prokaryotic cells are small in size and can be assessed by a single Raman spectrum, the larger size of eukaryotic cells and their complex organization requires the acquisition of multiple Raman spectra to properly characterize them. A Raman spectrum from a diffraction-limited spot at an arbitrary location within a cell results in spectral variations that affect classification approaches. To probe whole cells with Raman imaging at high spatial resolution is time consuming, because a large number of Raman spectra need to be collected, resulting in low cell throughput and impairing statistical analysis due to low cell numbers. Here we propose a method to overcome the effects of cellular heterogeneity by acquiring integrated Raman spectra covering a large portion of a cell. The acquired spectrum represents the mean macromolecular composition of a cell with an exposure time that is comparable to acquisition of a single Raman spectrum. Data sets were collected from T lymphocyte Jurkat cells, and pancreatic cell lines Capan1 and MiaPaca2. Cell classification by support vector machines was compared for single spectra, spectra of images and integrated Raman spectra of cells. The integrated approach provides better and more stable prediction for individual cells, and in the current implementation, the mean macromolecular information of a cell can be acquired faster than with the acquisition of individual spectra from a comparable region. It is expected that this approach will have a major impact on the implementation of Raman based cell classification.