Jiangning Li | PubMed Health

PubMed2006

cDNA microarray analysis reveals fundamental differences in the expression profiles of primary human monocytes, monocyte-derived macrophages, and alveolar macrophages.

We report the systematic use of large-scale cDNA microarrays to study the gene expression profiles of primary human peripheral blood monocytes (MONO) in comparison with in vitro-differentiated, M-CSF-induced MONO-derived macrophages (MAC) and primary human alveolar MAC (AM), obtained by bronchoalveolar lavage from the lungs of normal volunteers. These studies revealed large-scale differences in the gene expression profile between both MAC types (MAC and AM) and MONO. In addition, large differences were observed in the gene expression profiles of the two MAC types. Specifically, 21% of genes on the array (2904 out of 13,582) were differentially expressed between AM and MONO, and 2229 out of 13,583 probes were differentially expressed between MAC and AM. Our expression data show remarkable differences in gene expression between different MAC subpopulations and emphasize the heterogeneity of different MAC populations. This study underscores the need to scrutinize models of MAC biology for relevance to specific disease processes.

Adult↗

PubMed2004

Sample size for detecting differentially expressed genes in microarray experiments.

BACKGROUND: Microarray experiments are often performed with a small number of biological replicates, resulting in low statistical power for detecting differentially expressed genes and concomitant high false positive rates. While increasing sample size can increase statistical power and decrease error rates, with too many samples, valuable resources are not used efficiently. The issue of how many replicates are required in a typical experimental system needs to be addressed. Of particular interest is the difference in required sample sizes for similar experiments in inbred vs. outbred populations (e.g. mouse and rat vs. human). RESULTS: We hypothesize that if all other factors (assay protocol, microarray platform, data pre-processing) were equal, fewer individuals would be needed for the same statistical power using inbred animals as opposed to unrelated human subjects, as genetic effects on gene expression will be removed in the inbred populations. We apply the same normalization algorithm and estimate the variance of gene expression for a variety of cDNA data sets (humans, inbred mice and rats) comparing two conditions. Using one sample, paired sample or two independent sample t-tests, we calculate the sample sizes required to detect a 1.5-, 2-, and 4-fold changes in expression level as a function of false positive rate, power and percentage of genes that have a standard deviation below a given percentile. CONCLUSIONS: Factors that affect power and sample size calculations include variability of the population, the desired detectable differences, the power to detect the differences, and an acceptable error rate. In addition, experimental design, technical variability and data pre-processing play a role in the power of the statistical tests in microarrays. We show that the number of samples required for detecting a 2-fold change with 90% probability and a p-value of 0.01 in humans is much larger than the number of samples commonly used in present day studies, and that far fewer individuals are needed for the same statistical power when using inbred animals rather than unrelated human subjects.

Animals↗

PubMedDate not supplied

RNA amplification, fidelity and reproducibility of expression profiling.

We quantitatively address the effect of T7 RNA amplification on expression profiling data, and answer the following questions: (1) What fraction of genes sampled is amplified non-linearly? (2) What is the effect of RNA amplification on comparative expression measurements? (3) If there is amplification bias, is the bias dependent on the degree of amplification or the amount of starting material and (4) Does amplification increase the overall variability of the results? We show that while there is significant amplification bias, the bias is consistent and generally has little effect on array comparisons between amplified samples.

HeLa Cells↗