Project description:The goal of this study was to use global gene expression as a diagnostic tool to compare hepatic gene expression patterns in both male and female FHM in streams with the lowest and highest reproductive success, and potentially identify a suite of mRNA transcripts indicative of reproduction in a population The goal of this study was to compare differences in hepatic mRNA expression between gender at high and low egg-producing streams, not differences between individual streams. A k-means cluster analysis was performed using eggs/pair/day on the original 17 streams to delineate 3 clusters: high, medium and low. From that analysis, FHM from 6 of the original 17-streams used in Crago et al. (2010) were chosen for the microarray experiment (Fig. 1, Table 1). In this study the experimental condition is reproductive success; High versus Low reproductive success. The streams grouped into High Reproductive Success were Oak Creek-2007 (2313 eggs), Point Creek (1277 eggs), Meeme Creek (1164 eggs) and Baird Creek (967 eggs). The streams grouped into Low Reproductive Success were: Ashwaubenon Creek (0 eggs), Devils Creek (541 eggs) and Oak Creek-2006 (642 eggs). Multiple regression analysis using the 22 sediment and water quality characteristics measured in the 6 streams with the highest (n = 4 and lowest (n = 3) streams demonstrated that there were no differences amongst the streams in regards to measure sediment and water variables. .5 One array was run for each gender from each stream. So that Males from Point Creek were pooled and run on one array, males from Ashwaubenon Creek were run on a separate array, and so forth. There were 14 arrays used in this study, 7 for males, 7 for females from individual rivers. So that Males from Point Creek were pooled and run on one array, males from Ashwaubenon Creek were run on a separate array, and so forth. In the case of Oak Creek, which was sampled in both years, there was a large difference in egg production between two years. Therefore separate arrays were run for Oak Creek 2006 and Oak Creek 2007. All streams chosen had overall survival rates of at least 80% through the 21-day sampling period, except Devils River. The survival rate for Devils River was at 100% until four days prior to the end of the experiment when six fish died or escaped.

Project description:Contemporary high dimensional biological assays, such as mRNA expression microarrays, regularly involve multiple data processing steps, such as experimental processing, computational processing, sample selection, or feature selection (i.e. gene selection), prior to deriving any biological conclusions. These steps can dramatically change the interpretation of an experiment. Evaluation of processing steps has received limited attention in the literature. It is not straightforward to evaluate different processing methods and investigators are often unsure of the best method. We present a simple statistical tool, Standardized WithIn class Sum of Squares (SWISS), that allows investigators to compare alternate data processing methods, such as different experimental methods, normalizations, or technologies, on a dataset in terms of how well they cluster a priori biological classes. SWISS uses Euclidean distance to determine which method does a better job of clustering the data elements based on a priori classifications. We apply SWISS to three different gene expression applications. The first application uses four different datasets to compare different experimental methods, normalizations, and gene sets. The second application, using data from the MicroArray Quality Control (MAQC) project, compares different microarray platforms. The third application compares different technologies: a single Agilent two-color microarray versus one lane of RNA-Seq. These applications give an indication of the variety of problems that SWISS can be helpful in solving. The SWISS analysis of one-color versus two-color microarrays provides investigators who use two-color arrays the opportunity to review their results in light of a single-channel analysis, with all of the associated benefits offered by this design. Analysis of the MACQ data shows differential intersite reproducibility by array platform. SWISS also shows that one lane of RNA-Seq clusters data by biological phenotypes as well as a single Agilent two-color microarray.

Dataset Information

Stream biofilms from four Swiss streams

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