Spss 13.0 statistics software

Continuous data were expressed as mean ± SD, and categorical data were presented as frequency. Continuous data analysis was performed using the independent t test for parametric variables. Multiple groups were compared using ANOVA, which pairwise comparisons using LSD method. Nonparametric variables analysis was performed using the Mann–Whitney test. Categorical data were calculated using Pearson Chi-square test.
Progression-free survival (PFS) was compared by employing the Kaplan–Meier method and Cox proportional-hazard model. The cut-off point of each continuous parameter was determined through receiver-operating characteristic curve and Youden index, using PFS as the classification status. The Kaplan–Meier was used for univariate analysis. The Cox proportional-hazard model was used for multivariate analysis. P < .05 was considered statistically significant. The SPSS 13.0 statistics software was used for statistical analysis.
The correlation of each 2 parameters was calculated and compared. The correlation coefficient (R value) = 0.21–0.40 for the poor consistency, R value = 0.41–0.60 for the general consistency, R value = 0.61–0.80 for the good consistency, and R value = 0.81–1.00 for the excellent consistency. P < .05 were assumed to indicate significant differences.

All the data are expressed as mean ± SD, where the error bars represent the standard deviation of the mean. Student’s t test and one-way analysis of variance (ANOVA) were used to calculate the statistical significance. P<0.05 was considered to be statistically significant. Statistical analyses were performed using the SPSS 13.0 statistics software (SPSS Inc, U.S.A.).

The open reading frames (ORFs) in each unigene sequence were predicted by searching against protein databases using BLASTX (E-value ≤1e−5) in the following order: Nr, SwissProt, KEGG, and COG. Sequences having hits in the former database did not go to the next round of searching against a later database. The coding regions were then extracted according to the best BLASTX match with a custom Perl script. CodonW (http://codonw.sourceforge.net/) (Peden 1999 ) was used to analyze RSCU and GC3 of ORFs (≥150 nt). Perl scripts were used to process the output files of CodonW. We used SPSS 13.0 statistics software (SPSS Inc., Chicago, IL) for correlation analysis.
NC and CAI were calculated by DAMBE (Xia 2013 ) using the Delia antiqua ribosomal proteins codon usage table as the reference when calculated CAI.

All the data are expressed as mean ± S.E.M. The data were performed by SPSS13.0 statistics software (SPSS Inc., Chicago, IL, USA). The significance of differences between groups was evaluated by one-way analysis of variance (ANOVA) for multiple comparisons. A p value of less than 0.05 was considered to be significant.

The ORFs in each unigene sequence was predicted by BLAST against protein databases using BLASTX (E-value < = 1e-5) in the following order: Nr, SwissProt, KEGG, COG. Sequences with hits in former databases would not go to next round search against later database. The coding regions were then extracted according to the best BLASTX match with a custom perl script. We analyzed GC content and codon usage bias of ORFs longer than 150 nt by CodonW (http://codonw.sourceforge.net/), and processed the output files of Codonw with a perl script. The correlation analysis was performed using SPSS 13.0 statistics software (http://www-01.ibm.com/software/analytics/spss/).

SPSS13.0 statistics software was used for analysis. Measurement data were expressed with x̄±s, and independent t test was used for intra-group comparison. Enumeration data were expressed with frequency and percentage, and χ2 (link) test was adopted for inter-group comparison. P<0.05 means differences have statistical significance.