scholarly journals Exploratory analysis and error modeling of a sequencing technology

2016 ◽  
Author(s):  
Michael Inouye ◽  
Kerrin S Small ◽  
Yik Y Teo ◽  
Heng Li ◽  
Nava Whiteford ◽  
...  
Keyword(s):  
Linear Models ◽  
Sequence Data ◽  
Artificial Chromosome ◽  
Error Modeling ◽  
Illumina Genome Analyzer ◽  
Data Generation ◽  
Sequencing Technology ◽  
Additional Information ◽  
Base Position ◽  
Log Linear

Next generation DNA sequencing methods have created an unprecedented leap in sequence data generation, thus novel computational tools and statistical models are required to optimize and assess the resulting data. In this report, we explore underlying causes of error for the Illumina Genome Analyzer (IGA) sequencing technology and attempt to quantify their effects using a human bacterial artificial chromosome sequenced to 60,000 fold coverage. Seven potential error predictors are considered: Phred score, read entropy, tile coordinates, local tile density, base position within read, nucleotide call, and lane. With these parameters, logistic regression and log-linear models are constructed and used to show that each of the potential predictors contributes to error (P<1x10-4). With this additional information, we apply the logistic model and achieve a 3% improvement in both the sensitivity and specificity to detect IGA errors. Further, we demonstrate that these modeling approaches can be used as a feedback loop to inform laboratory methods and identify specific machine or run bias.

Generalized Log-Linear Models of Housing Choice

1988 ◽  
Vol 20 (1) ◽  
pp. 55-69 ◽  
Author(s):  
M C Deurloo ◽  
F M Dieleman ◽  
W A V Clark
Keyword(s):  
Linear Models ◽  
Nonlinear Effects ◽  
Design Matrix ◽  
Housing Choice ◽  
Ordered Categories ◽  
Public And Private ◽  
Income Effects ◽  
Additional Information ◽  
Log Linear

By incorporating the structure of polytomous variables with ordered categories in the design matrix, nonstandard logit models are used to analyze housing choice. The detailed effects of income, age, and type of housing market on choice are examined. The additional information that is incorporated in the modeling leads to a more parsimonious representation of the data. The results confirm the central and substantial role of income; income effects are linear for owners but there are nonlinear effects for public and private renters. There are important age and region interaction effects on choice for households originally in the rental sector, and for former owners the value of the previous dwelling influences choice.

Measuring the Change in Contingency Tables Using Markov Models — Application to the Effect of Preceding Conception on the Next one

1982 ◽  
Vol 21 (01) ◽  
pp. 31-33
Author(s):  
A. K. Saleh ◽  
M. A. A. Moussa ◽  
H. Hathout ◽  
R. Kasraoui
Keyword(s):  
Linear Models ◽  
Markov Models ◽  
Transition Probabilities ◽  
Sequence Data ◽  
Contingency Tables ◽  
Maternity Hospital ◽  
Time Sequence ◽  
Maximum Likelihood Estimates ◽  
Previous Pregnancy ◽  
Log Linear

A method is described for the analysis of time sequence data viewed as contingency tables. This includes 1.) determining the maximum likelihood estimates of stationary and transition probabilities; 2.) testing stationarity of transition probabilities in first and second order Markov models by fitting the data to the appropriate configuration of log-linear models; 3.) assessing the order of dependence through fitting log-linear models. Data based on a study recently conducted on women delivered in the Principal Maternity Hospital in Kuwait is used for illustration. It is concluded that conception outcome depends on pregnancy order as well as on at least two previous pregnancy outcomes.

Improved inference in linear models with additional information

Optimization ◽  
1975 ◽  
Vol 6 (5) ◽  
pp. 817-829
Author(s):  
O. Bunke
Keyword(s):  
Linear Models ◽  
Additional Information

When and why are log-linear models self-normalizing?

2015 ◽  
Author(s):  
Jacob Andreas ◽  
Dan Klein
Keyword(s):  
Linear Models ◽  
Log Linear

Log-Linear Models with Dependent Spatial Data

1983 ◽  
Vol 15 (6) ◽  
pp. 801-813 ◽  
Author(s):  
B Fingleton
Keyword(s):  
Spatial Data ◽  
Statistical Models ◽  
Linear Models ◽  
Square Lattice ◽  
Categorical Variables ◽  
Systematic Sampling ◽  
Pairwise Correlations ◽  
Independent Observations ◽  
Intersection Points ◽  
Log Linear

Log-linear models are an appropriate means of determining the magnitude and direction of interactions between categorical variables that in common with other statistical models assume independent observations. Spatial data are often dependent rather than independent and thus the analysis of spatial data by log-linear models may erroneously detect interactions between variables that are spurious and are the consequence of pairwise correlations between observations. A procedure is described in this paper to accommodate these effects that requires only very minimal assumptions about the nature of the autocorrelation process given systematic sampling at intersection points on a square lattice.

scholarly journals Log-Linear Models for Frequency Data: Sufficient Statistics and Likelihood Equations

1973 ◽  
Vol 1 (4) ◽  
pp. 617-632 ◽  
Author(s):  
Shelby J. Haberman
Keyword(s):  
Linear Models ◽  
Frequency Data ◽  
Sufficient Statistics ◽  
Log Linear

Educational Homogamy Among Married and Unmarried Couples in Europe

2008 ◽  
Vol 30 (1) ◽  
pp. 28-52 ◽  
Author(s):  
Dana Hamplova
Keyword(s):  
Linear Models ◽  
Married Couples ◽  
European Social Survey ◽  
Social Survey ◽  
Educational Homogamy ◽  
Educational Barriers ◽  
Educational Groups ◽  
Institutional Contexts ◽  
Using Data ◽  
Log Linear

In this article, educational homogamy among married and cohabiting couples in selected European countries is examined. Using data from two waves (2002 and 2004) of the European Social Survey, this article compares three cultural and institutional contexts that differ in terms of institutionalization of cohabitation. Evidence from log-linear models yields two main conclusions. First, as cohabitation becomes more common in society, marriage and cohabitation become more similar with respect to partner selection. Second, where married and unmarried unions differ in terms of educational homogamy, married couples have higher odds of overcoming educational barriers (i.e., intermarrying with other educational groups).

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