Autocorrelation analysis reveals widespread spatial biases in microarray experiments
نویسندگان
چکیده
منابع مشابه
Spatial Analysis of cDNA Microarray Experiments
Some normalization methods assume that the two channels of intensities are related by a constant so that Microarray experiments allow RNA level measurements for many the center of the distribution of the log of the ratio is genes in multiple samples. However, mining the biological information shifted to zero. Chen et al. (1997) proposed an iterative from the large sets of data generated by micr...
متن کاملBiases induced by pooling samples in microarray experiments
MOTIVATION If there is insufficient RNA from the tissues under investigation from one organism, then it is common practice to pool RNA. An important question is to determine whether pooling introduces biases, which can lead to inaccurate results. In this article, we describe two biases related to pooling, from a theoretical as well as a practical point of view. RESULTS We model and quantify t...
متن کاملSpatial Autocorrelation
Glossary AutoA prefix literally meaning self; spatial autocorrelation means self-correlation, or values within a given variable are correlated, resulting in the variable being correlated with itself. Correlation A description of the nature and degree of a relationship between a pair of quantitative variables. Geary Ratio An index of spatial autocorrelation, involving the computation of squared ...
متن کاملSpatial Autocorrelation
Spatial autocorrelation is a method of Exploratory Spatial Data Analysis (ESDA). The latter set of methods allow for the study and understanding of the spatial distribution and spatial structure as well as they allow for detecting spatial dependence or autocorrelation in spatial data. More specifically, spatial autocorrelation is the correlation between the values of a single variable that is s...
متن کاملAnalysis of DNA diversity by spatial autocorrelation.
Two statistics are proposed for summarizing spatial patterns of DNA diversity. These autocorrelation indices for DNA analysis, or AIDAs, can be applied to RFLP and sequence data; the resulting set of autocorrelation coefficients, or correlogram, measures whether, and to what extent, individual DNA sequences or haplotypes resemble the haplotypes sampled at arbitrarily chosen spatial distances. A...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: BMC Genomics
سال: 2007
ISSN: 1471-2164
DOI: 10.1186/1471-2164-8-164