Statistical Methods By N.g. Das Pdf Free !FULL! Download

November 20, 2022

Statistical Methods By N.g. Das Pdf Free !FULL! Download

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Statistical Methods By N.g. Das Pdf Free Download

we believe that the current research illustrates the importance of carefully choosing the appropriate normalization and statistical methods when degs are discovered. in future studies, the importance of choosing an appropriate statistical method can be further emphasized. our work showed that a careful choice of normalization and statistical methods can provide investigators confidence in the deg data generated from rna-seq data. the proper choice of methods can also help avoid or lessen potential confusions from unusual gene expression patterns.

acknowledgements: this work was supported by the national science foundation (che-1425255, eec: 0840119). any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the national science foundation. we would like to thank the two anonymous reviewers and editor for providing constructive comments to help improve this manuscript.

in comparison, we have considered a common scenario that may affect our conclusion. we randomly generated a negative control (nc) group which has only a minimal amount of degs in two oc datasets. we applied the same pipeline as the above degs results to the nc sample. the results of three test statistics combined with three normalization methods are shown in fig. 4 and additional file 1 : figure s4. at the low number of degs setting, the fpr from the three test statistics combined with normalization methods is similar. however, when the number of degs increases, the result of fpr from ql f-test with rle, tmm and uq-pgq2 are similar. moreover, when the number of degs are large, the fpr is lower in both normalization methods compared to the other two test statistics. however, the exact test with rle and tmm approaches the fpr by ql f-test and wald test when the number of degs is large.

in this chapter, we focused on the comparison of four normalization methods: tmm, uq, rle and uq-pgq2. we used maqc rna-seq datasets with small sample replicates to show the performance of uq-pgq2 in differential gene expression analysis. the other three normalization methods can be found in the supplementary material. we also compared the performance of two different test statistics, an exact test and a q-value method, using real and simulated datasets. we found that the q-value method performed better when the number of sample replicates is small. in contrast, the exact test performed better when the number of replicates is large. finally, we used the maqc data to compare the performance of three normalization methods (tmm, rle, and uq) with different statistical methods (exact test, wald test, and ql f-test) given a desired sample size. we found that a ql f-test combined with a normalization method performs the best in deg identification in an intra-group analysis. however, an exact test or a wald test combined with tmm, rle, or uq performed best in the same analysis when the sample size is large. moreover, an exact test combined with a normalization method performs best for an inter-group analysis of the differential gene expression analysis when the number of sample replicates is large. the r code for our analyses can be found in the supplementary materials, together with r script to reproduce the results from the maqc rna-seq datasets with different sample sizes.
the chapter of statistical methods starts with the basic concepts of probability and random variates. the common aspects/concepts in sem are: (i) model specification, (ii) estimation of free parameters, (iii) model evaluation, (iv) significance testing, and (v) parametric versus non-parametric model comparison. in the first section, we define the statistical model and assumptions. in the second section, the concept of probability density functions is used to specify a particular statistical model. in the third section, we define the concept of the sampling distribution of a statistical estimator and define the non-centrality parameters. in the fourth section, we define the concept of asymptotic distribution and define the estimators of parameters. in the fifth section, we define the concept of confidence interval and define the concept of test of significance. the concept of statistical hypothesis test is defined in the sixth section. in the seventh section, we define the concepts of model evaluation and model comparison. in the eighth section, we define the concept of parameters of model and data. in the ninth section, we define the concept of standard errors and confidence intervals and define the method for constructing confidence interval. in the tenth section, we define the concept of significance and define the significance level. in the eleventh section, we define the concept of a particular statistical estimator and define the corresponding sampling distribution. in the twelfth section, we define the concept of hypothesis testing and define hypothesis tests. in the thirteenth section, we define the concept of the null hypothesis and the alternative hypothesis. in the fourteenth section, we define the concept of residual and define the concept of statistical model and parametric versus non-parametric model comparison. in the fifteenth section, we define the concept of the sampling distribution of residuals and define the concept of statistical model. in the sixteenth section, we define the concept of the sampling distribution of random variates and define the concept of random variate.
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