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Biomedical subjects

R A Irizarry

Publications and source records attributed to R A Irizarry.

5 recordsLinked to original sources
PubMed2005

On the utility of pooling biological samples in microarray experiments.

Over 15% of the data sets catalogued in the Gene Expression Omnibus Database involve RNA samples that have been pooled before hybridization. Pooling affects data quality and inference, but the exact effects are not yet known because pooling has not been systematically studied in the context of microarray experiments. Here we report on the results of an experiment designed to evaluate the utility of pooling and the impact on identifying differentially expressed genes. We find that inference for most genes is not adversely affected by pooling, and we recommend that pooling be done when fewer than three arrays are used in each condition. For larger designs, pooling does not significantly improve inferences if few subjects are pooled. The realized benefits in this case do not outweigh the price paid for loss of individual specific information. Pooling is beneficial when many subjects are pooled, provided that independent samples contribute to multiple pools.

Analysis of Variance↗
PubMed2003

A comparison of normalization methods for high density oligonucleotide array data based on variance and bias.

MOTIVATION: When running experiments that involve multiple high density oligonucleotide arrays, it is important to remove sources of variation between arrays of non-biological origin. Normalization is a process for reducing this variation. It is common to see non-linear relations between arrays and the standard normalization provided by Affymetrix does not perform well in these situations. RESULTS: We present three methods of performing normalization at the probe intensity level. These methods are called complete data methods because they make use of data from all arrays in an experiment to form the normalizing relation. These algorithms are compared to two methods that make use of a baseline array: a one number scaling based algorithm and a method that uses a non-linear normalizing relation by comparing the variability and bias of an expression measure. Two publicly available datasets are used to carry out the comparisons. The simplest and quickest complete data method is found to perform favorably. AVAILABILITY: Software implementing all three of the complete data normalization methods is available as part of the R package Affy, which is a part of the Bioconductor project http://www.bioconductor.org. SUPPLEMENTARY INFORMATION: Additional figures may be found at http://www.stat.berkeley.edu/~bolstad/normalize/index.html

Algorithms↗
PubMed2001

Cross-correlation of fetal cardiac and somatic activity as an indicator of antenatal neural development.

OBJECTIVE: In this study, we wanted to model the emergence of coupling between fetal cardiac and somatic activity in normal and at-risk fetuses. STUDY DESIGN: One hundred six fetuses of uncomplicated pregnancies were longitudinally monitored at 20, 24, 28, 32, 36, and 38 weeks of gestation by using a fetal actocardiograph and computerized data collection. Twenty-six fetuses of complicated pregnancies were also included. Statistical time series analysis techniques were used to examine the relation between fetal movement and fetal heart rate. RESULTS: A linear increase was found in the magnitude of the cross-correlation function between fetal movement and fetal heart rate as gestation advanced, with coalescence around a peak lag of 5 seconds by 32 weeks. Fetuses that delivered before term evidenced accelerated fetal movement and fetal heart rate coupling, whereas fetuses affected by deleterious conditions showed a decline in developmental trajectory. CONCLUSIONS: The cross-correlation between fetal cardiac and somatic activity is an indicator of neuroregulation in human fetuses.

Embryonic and Fetal Development↗
PubMed2001

Assessing homeostasis through circadian patterns.

An organism is thought to be in a dynamic state of homeostasis when each physiological and behavioral system reaches a delicate balance within the framework of other regulatory processes. Many biological systems target specific set-point variables and generate circadian patterns. In this article, we focus on specific measurements representative of two systems, namely deep-body temperature and activity counts. We examine data collected every 30 minutes in mice, assume there are underlying circadian patterns, and extend the approach presented in Brumback and Rice (1998, Journal of the American Statistical Association 93, 961-976) in order to obtain estimates in the presence of correlated data. We then assess homeostasis using these estimates and their statistical properties.

Animals↗