MOTIVATION: Single-cell proteomics technologies, such as mass cytometry, have enabled characterization of cell-to-cell variation and cell populations at a single-cell resolution. These large amounts of data, require dedicated, interactive tools for translating the data into knowledge. RESULTS: We present a comprehensive, interactive method called Cyto to streamline analysis of large-scale cytometry data. Cyto is a workflow-based open-source solution that automates the use of state-of-the-art single-cell analysis methods with interactive visualization. We show the utility of Cyto by applying it to mass cytometry data from peripheral blood and high-grade serous ovarian cancer (HGSOC) samples. Our results show that Cyto is able to reliably capture the immune cell sub-populations from peripheral blood and cellular compositions of unique immune- and cancer cell subpopulations in HGSOC tumor and ascites samples. AVAILABILITYAND IMPLEMENTATION: The method is available as a Docker container at https://hub.docker.com/r/anduril/cyto and the user guide and source code are available at https://bitbucket.org/anduril-dev/cyto. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Fusion genes are both useful cancer biomarkers and important drug targets. Finding relevant fusion genes is challenging due to genomic instability resulting in a high number of passenger events. To reveal and prioritize relevant gene fusion events we have developed FUsionN Gene Identification toolset (FUNGI) that uses an ensemble of fusion detection algorithms with prioritization and visualization modules. RESULTS: We applied FUNGI to an ovarian cancer dataset of 107 tumor samples from 36 patients. Ten out of 11 detected and prioritized fusion genes were validated. Many of detected fusion genes affect the PI3K-AKT pathway with potential role in treatment resistance. AVAILABILITYAND IMPLEMENTATION: FUNGI and its documentation are available at https://bitbucket.org/alejandra_cervera/fungi as standalone or from Anduril at https://www.anduril.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: Anduril is an analysis and integration framework that facilitates the design, use, parallelization and reproducibility of bioinformatics workflows. Anduril has been upgraded to use Scala for pipeline construction, which simplifies software maintenance, and facilitates design of complex pipelines. Additionally, Anduril's bioinformatics repository has been expanded with multiple components, and tutorial pipelines, for next-generation sequencing data analysis. AVAILABILITYAND IMPLEMENTATION: Freely available at http://anduril.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Current aim was to introduce and initially evaluate the performance of a system called Extended Cognition, which was developed for enhancing the filtering of emotionally meaningful information from visual lifelogging data. The system is proposed to add facial expression markers to the stream of visual lifelogging data in order to later find special, possibly emotionally meaningful moments from the data. In an initial user study we collected subjective evaluations (N=10) about the ergonomics of the data measurement setup and the use of voluntary smiles and frowns for adding markers. Experience evaluations about the system were also collected. Results showed that data measurement setup was ergonomic to use and the system was evaluated positively. Smiling was rated as pleasant, natural, and functional expression for data marking purposes. Participants considered that, for example, the system would support memorization, and that it could be used to share important memories with others.

This study presents the results of a longitudinal study on multimodal text entry where objects were selected by gazing and smiling. Gaze was used to point at the desired characters and smiling movements were performed to select them. Participants (N=12) took part in the experiments where they entered text for a total of 2.5 hours in ten 15-minute-long sessions during one-month time period. The results showed that the text entry rate improved with practice from 4.1 to 6.7 words per minute. However, the learning curve had not reached its plateau phase at the end of the experiment. Subjective ratings showed that the participants appreciated this multimodal technique.

The aim of this paper was to evaluate the use of three facial actions (i.e. frowning, raising the eyebrows, and smiling) in selecting objects on a computer screen when gaze was used for pointing. Dwell time is the most commonly used selection technique in gaze-based interaction, and thus, a dwell time of 400 ms was used as a reference selection technique. A wireless, head-mounted prototype device that carried out eye tracking and contactless, capacitive measurement of facial actions was used for the interaction task. Participants (N=16) performed point-and-select tasks with three pointing distances (i.e. 60, 120 and 240 mm) and three target sizes (i.e. 25, 30 and 40 mm). Task completion times, pointing errors and throughput values based on Fitts' law were used to compare the selection techniques. The participants also rated the techniques with subjective ratings scales. The results showed that the different techniques performed equally well in many respects. However, throughput values varied from 8.38 bits/s (raising the eyebrows) to 15.33 bits/s (smiling) and were comparable to or, in the case of smiling, better than in earlier research with similar interaction techniques. The dwell time was found to be the least accurate selection technique in terms of the magnitudes of point-and-select errors. Smiling technique was rated as more accurate to use than the frowning or the raising techniques. The results give further support for methods that combine facial behavior to eye tracking when interacting with technology.

The effect of facial behaviour on gaze tracking accuracy was studied while using a prototype system that integrated head-mounted, video-based gaze tracking and a capacitive facial movement detection for respective pointing and selecting objects in a simple graphical user interface. Experiments were carried out to determine how voluntary smiling movements that were used to indicate clicks affect the accuracy of gaze tracking due to the combination of user eye movement behaviour and the operation of gaze tracking algorithms. The results showed no observable degradation of the gaze tracking accuracy when using voluntary smiling for object selections.

The present aim was to investigate the functionality of a new wireless prototype called Face Interface. The prototype combines the use of voluntary gaze direction and facial muscle activations, for pointing and selecting objects on a computer screen, respectively. The subjective and objective functionality of the prototype was evaluated with a series of pointing tasks using either frowning (i.e., frowning technique) or raising the eyebrows (i.e., raising technique) as the selection technique. Pointing task times and accuracies were measured using three target diameters (i.e., 25, 30, 40 mm), seven pointing distances (i.e., 60, 120, 180, 240, 260, 450, and 520 mm), and eight pointing angles (0°, 45°, 90°, 135°, 180°, 225°, 270°, and 315°). The results showed that the raising technique was faster selection technique than the frowning technique for the objects that were presented in the pointing distances from 60 mm to 260 mm. For those pointing distances the overall pointing task times were 2.4 s for the frowning technique, and 1.6 s for the raising technique. Fitts’ law computations showed that the correlations for the Fitts’ law model were r = 0.77 for the frowning technique and r = 0.51 for the raising technique. Further, the index of performance (IP) value was 1.9 bits/s for the frowning technique and 5.4 bits/s for raising the eyebrows technique. Based on the results, the prototype functioned well and was adjustable so that two different facial activations can be used in combination with gaze direction for pointing and selecting objects on a computer screen.

A light-weight, wearable, wireless gaze tracker with integrated selection command source for human-computer interaction is introduced. The prototype system combines head-mounted, video-based gaze tracking with capacitive facial movement detection that enable multimodal interaction by gaze pointing and making selections with facial gestures. The system is targeted mainly to disabled people with limited mobility over their hands. The hardware was made wireless to remove the need to take off the device when moving away from the computer, and to allow future use in more mobile contexts. The algorithms responsible for determining the eye and head orientations to map gaze direction to on-screen coordinates are presented together with the one to detect movements from the measured capacitance signal. Point-and-click experiments were conducted to assess the performance of the multimodal system. The results show decent performance in laboratory and office conditions. The overall point-and-click accuracy in the multimodal experiments is comparable to the errors in previous research on head-mounted, single modality gaze tracking that does not compensate for changes in head orientation.