Our investigation revealed that the most critical considerations for the analysis of RNA-Seq read count data were the numbers of biological samples, normalization method, and underlying data distribution assumptions.
Normalization using global mean expression reduced variability for technical and biological replicates while normalization using the expression of the identified reference miRNAs reduced variability only for biological replicates.
The novel features of the flexible silicon jacket for µECG telemetry unraveled the biological clock and normalization of QT intervals at 26 dpa, providing the first evidence of new physiological phenomena during cardiac injury and repair as well as cardiac drug-mediated aberrant rhythms.
After the exclusion of probes with missing data, absolute mean fluorescence signal values (f) obtained across biological replicates after normalization were divided into four classes (f < 10, 10 < = f < 100, 100 < = f < 1000, f < = 1000).
A similar strategy was used to identify differentially regulated genes and/or mRNA between Hsf4-null and wild-type lens (biological triplicates, RMA normalization, conjunction of t-test; P < 0.05 and SAM FDR < 5%).
Based on variance between biological replications after normalization and exclusion of any genes flagged "non-present" for one of the biological replicates, the mean, standard deviation, and coefficient of variation of signal intensity over biological replicates was 7.70, 0.20, and 3.18%, respectively.
RPKMs were normalized between biological replicates using scaling normalization [ 76].
The probe intensities from various biological replicates were normalized using scale normalization using median absolute deviation as a spread measure.
The variables studied were basal recovery time (as reflected in crying and the normalization of biological constants), number of punctures, oxygen saturation, heartbeat, blood pressure, response to skin-to-skin contact, and gestational age.
