The locations of co-evolutionary sites driving the antigenic drift of four events: “BE89-BE92,” “BE92-WU95,” “WU95-SY97,” and “SY97-FU02.”

<p>The H3N2 structure pdb (2VIU) are used as the backbone and the antigenic domains A, B, C, D and E are also marked after the position numbers.</p

Top 65 predominant antigenicity associated sites for H3N2 influenza A viruses.

<p>Weight denotes the importance of the single and co-evolutionary sites in shaping the antigenic evolution. As suggested by the parameter tuning process (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0106660#pone.0106660.s012" target="_blank">Table S2</a>), the sites are generated by feature type “Sinco+EvolT4” and Lasso parameter 2⁴.</p><p>Top 65 predominant antigenicity associated sites for H3N2 influenza A viruses.</p

The prediction RMSE curves comparing eight feature types.

<p>A sequential prediction was applied for viruses spanning from 1985 to 2003. The 8 feature types are “single”, “sinco+Struct6A”, “sinco+Struct10A”, “sinco+EvolT4”, “sinco+EvolT8”, “sinco+EvolT10”, “Sinco+EvolT16”, and “sinco+Struct10A+EvolT2”.</p

Sequence-based cartographies on 1415 H3N2 influenza viruses from 2002 to 2013 downloadable from NCBI.

<p>Each colored ball represents a virus. The different colors mark its collection year. The five vaccine strains “Fujian/411/2002,” “California/07/2004,” “Wisconsin/67/2005,” “Brisbane/10/2007,” and “Perth/16/2007” are shown in big ball. We also mark the year of a representative virus in other years.</p

Single and co-evolutionary sites driving the 12 antigenic drift events between successive clusters from HK68, EN72, VI75, TX77, BK79, SI87, BE89, BE92, WU95, SY97, FU02, CA04 and BR07.

<p>As suggested by parameter tuning process (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0106660#pone.0106660.s013" target="_blank">Table S3</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0106660#pone.0106660.s014" target="_blank">S4</a>), the sites are generated by feature type “sinco+EvolT8” and Lasso parameter 1; the top numbers are selected by prediction RMSE curve. For simplicity, all top numbers are set to be 10, except for drift EN72-VI75 and BK79-SI87, which is set to be 15, CA04-BR07, which is 3 and SY97-FU02, which is 20.</p><p>Single and co-evolutionary sites driving the 12 antigenic drift events between successive clusters from HK68, EN72, VI75, TX77, BK79, SI87, BE89, BE92, WU95, SY97, FU02, CA04 and BR07.</p

HI-based and sequence-based cartographies on H3N2 68-07 data.

<p>Each ball denotes a single influenza virus and each individual color denotes a specific antigenic cluster.</p

Four simulation cartographies of antigenic drifts and mutants of positions driving the drifts.

<p>The four antigenic drift events are: “BE89-BE92,” “BE92-WU95,” “WU95-SY97” and “SY97-FU02”. The mutants listed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0106660#pone-0106660-t002" target="_blank">Table 2</a> from four wild strains “BE/352/1989,””JO/33/1994,” “NA/933/1995,” and “SY/5/1997” are also marked in the cartographies.</p

Performance on the dataset EF-family.

<p>Performance on the dataset EF-family.</p

Table_2_DECtp: Calling Differential Gene Expression Between Cancer and Normal Samples by Integrating Tumor Purity Information.XLSX

Identifying differentially expressed genes (DEGs) between tumor and normal samples is critical for studying tumorigenesis, and has been routinely applied to identify diagnostic, prognostic, and therapeutic biomarkers for many cancers. It is well-known that solid tumor tissue samples obtained from clinical settings are always mixtures of cancer and normal cells. However, the tumor purity information is more or less ignored in traditional differential expression analyses, which might decrease the power of differential gene identification or even bias the results. In this paper, we have developed a novel differential gene calling method called DECtp by integrating tumor purity information into a generalized least square procedure, followed by the Wald test. We compared DECtp with popular methods like t-test and limma on nine simulation datasets with different sample sizes and noise levels. DECtp achieved the highest area under curves (AUCs) for all the comparisons, suggesting that cancer purity information is critical for DEG calling between tumor and normal samples. In addition, we applied DECtp into cancer and normal samples of 14 tumor types collected from The Cancer Genome Atlas (TCGA) and compared the DEGs with those called by limma. As a result, DECtp achieved more sensitive, consistent, and biologically meaningful results and identified a few novel DEGs for further experimental validation.</p

Comparing eight feature types and 11 Lasso parameters.

<p>Comparing eight feature types and 11 Lasso parameters.</p