DOI: 10.5061/dryad.2vm70
title: Data from: Determining the subcellular location of new proteins from microscope images using local features

description: Motivation: Evaluation of previous systems for automated determination of
 subcellular location from microscope images has been done using datasets
 in which each location class consisted of multiple images of the same
 representative protein. Here, we frame a more challenging and useful
 problem where previously unseen proteins are to be classified. Results:
 Using CD-tagging, we generated two new image datasets for evaluation of
 this problem, which contain several different proteins for each location
 class. Evaluation of previous methods on these new datasets showed that it
 is much harder to train a classifier that generalizes across different
 proteins than one that simply recognizes a protein it was trained on. We
 therefore developed and evaluated additional approaches, incorporating
 novel modifications of local features techniques. These extended the
 notion of local features to exploit both the protein image and any
 reference markers that were imaged in parallel. With these, we obtained a
 large accuracy improvement in our new datasets over existing methods.
 Additionally, these features help achieve classification improvements for
 other previously studied datasets.
description: Labeled RandTag
 Widefield Images (Unlabeled Subset)This datasets consists
 of fluorescent microscope images of GFP-tagged proteins locating to
 different organelles. The organelles were annotated by visual inspection.
 For each organelle, multiple proteins locating to it were identified; and
 for each protein, multiple images were acquired. Images contain two
 channels: GFP tagged protein and a nuclear marker (Hoechst). NIH 3T3 cells
 were tagged using CD tagging. This data was acquired on a widefield
 microscopeunlabeled.tar.bz2Labeled RandTag Confocal ImagesThis datasets consists of fluorescent microscope images of GFP-tagged proteins locating to different organelles. The organelles were annotated by visual inspection. For each organelle, multiple proteins locating to it were identified; and for each protein, multiple images were acquired. Images contain two channels: GFP tagged protein and a nuclear marker (Hoechst). NIH 3T3 cells were tagged using CD tagging. This data was acquired on a confocal microscope.labeled-confocal.tar.bz2Labeled RandTag Widefield Images (Nuclear Subset)Part of widefield labeled datasetnuclear.tar.bz2Labeled RandTag Widefield Images (Cytoskeleton Subset)This is part of the labeled widefield dataset.cytoskeleton.tar.bz2Labeled RandTag Widefield Images (Nucleoli Subset)This is part of the labeled widefield dataset.nucleoli.tar.bz2Labeled RandTag Widefield Images (Golgi Subset)This file is part of the labeled widefield dataset.Golgi.tar.bz2Labeled RandTag Widefield Images (Mitochondria Subset)Part of widefield labeled subsetmitochondria.tar.bz2Labeled RandTag Widefield Images (Cytoplasmic Subset)This is part of the labeled widefield dataset.cytoplasmic.tar.bz2Labeled RandTag Widefield Images (Lysosomal Subset)This is part of the labeled widefield datasetlysosome.tar.bz2Labeled RandTag Widefield Images (Plasma Membrane Subset)This file is part of the widefield labeled datasetmembrane.tar.bz2Labeled RandTag Widefield Images (ER Subset)This is part of the labeled widefield dataset.ER.tar.bz2