Browsing by Subject "STATISTICAL-METHODS"

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  • Caicedo, Juan C.; Cooper, Sam; Heigwer, Florian; Warchal, Scott; Qiu, Peng; Molnar, Csaba; Vasilevich, Aliaksei S.; Barry, Joseph D.; Bansal, Harmanjit Singh; Kraus, Oren; Wawer, Mathias; Paavolainen, Lassi; Herrmann, Markus D.; Rohban, Mohammad; Hung, Jane; Hennig, Holger; Concannon, John; Smith, Ian; Clemons, Paul A.; Singh, Shantanu; Rees, Paul; Horvath, Peter; Linington, Roger G.; Carpenter, Anne E. (2017)
    Image-based cell profiling is a high-throughput strategy for the quantification of phenotypic differences among a variety of cell populations. It paves the way to studying biological systems on a large scale by using chemical and genetic perturbations. The general workflow for this technology involves image acquisition with high-throughput microscopy systems and subsequent image processing and analysis. Here, we introduce the steps required to create high-quality image-based (i.e., morphological) profiles from a collection of microscopy images. We recommend techniques that have proven useful in each stage of the data analysis process, on the basis of the experience of 20 laboratories worldwide that are refining their image-based cell-profiling methodologies in pursuit of biological discovery. The recommended techniques cover alternatives that may suit various biological goals, experimental designs, and laboratories' preferences.
  • Okser, Sebastian; Lehtimaki, Terho; Elo, Laura L.; Mononen, Nina; Peltonen, Nina; Kahonen, Mika; Juonala, Markus; Fan, Yue-Mei; Hernesniemi, Jussi A.; Laitinen, Tomi; Lyytikainen, Leo-Pekka; Rontu, Riikka; Eklund, Carita; Hutri-Kahonen, Nina; Taittonen, Leena; Hurme, Mikko; Viikari, Jorma S. A.; Raitakari, Olli T.; Aittokallio, Tero (2010)
  • Skwark, Marcin J.; Croucher, Nicholas J.; Puranen, Santeri; Chewapreecha, Claire; Pesonen, Maiju; Xu, Ying Ying; Turner, Paul; Harris, Simon R.; Beres, Stephen B.; Musser, James M.; Parkhill, Julian; Bentley, Stephen D.; Aurell, Erik; Corander, Jukka (2017)
    Recent advances in the scale and diversity of population genomic datasets for bacteria now provide the potential for genome-wide patterns of co-evolution to be studied at the resolution of individual bases. Here we describe a new statistical method, genomeDCA, which uses recent advances in computational structural biology to identify the polymorphic loci under the strongest co-evolutionary pressures. We apply genomeDCA to two large population data sets representing the major human pathogens Streptococcus pneumoniae (pneumococcus) and Streptococcus pyogenes (group A Streptococcus). For pneumococcus we identified 5,199 putative epistatic interactions between 1,936 sites. Over three-quarters of the links were between sites within the pbp2x, pbp1a and pbp2b genes, the sequences of which are critical in determining non-susceptibility to beta-lactam antibiotics. A network-based analysis found these genes were also coupled to that encoding dihydrofolate reductase, changes to which underlie trimethoprim resistance. Distinct from these antibiotic resistance genes, a large network component of 384 protein coding sequences encompassed many genes critical in basic cellular functions, while another distinct component included genes associated with virulence. The group A Streptococcus (GAS) data set population represents a clonal population with relatively little genetic variation and a high level of linkage disequilibrium across the genome. Despite this, we were able to pinpoint two RNA pseudouridine synthases, which were each strongly linked to a separate set of loci across the chromosome, representing biologically plausible targets of co-selection. The population genomic analysis method applied here identifies statistically significantly co-evolving locus pairs, potentially arising from fitness selection interdependence reflecting underlying protein- protein interactions, or genes whose product activities contribute to the same phenotype. This discovery approach greatly enhances the future potential of epistasis analysis for systems biology, and can complement genome-wide association studies as a means of formulating hypotheses for targeted experimental work.
  • Heidel-Fischer, Hanna M.; Vogel, Heiko; Heckel, David G.; Wheat, Christopher W. (2010)