We initially applied LC-MS analyses and data processing as described in Materials and Methods to an expanded-discovery set of urine samples from South Africa (D0, M1 and M6) to assess the separability of these time-grouped samples based on metabolic signatures.
Most recently, we have managed to classify brain transcriptomes of an external cohort of PD patients from control samples based on AS signatures detected in our cohort of PD patients' blood leukocytes and matched HC [ 36].
Hierarchical clustering and other similar analyses are informative to determine overall similarity between samples and to separate samples into defined groups based on molecular signatures.
Key steps in sequence signature methods include the counting of frequencies of all k-tuples to compose the sequence signature vector for each sample, calculating the dissimilarity measure between samples based on their sequence signature vectors, and analyzing relationships between multiple samples based on dissimilarities of all sample pairs.
The imageries were classified into four land cover classes based on the training samples signatures.
We conducted a systematic study on the application of sequence signature methods for the comparison of metagenomic samples, and found that, if dissimilarity measures are chosen properly, alignment-free methods based on sequence signatures can successfully reveal major group and gradient relationships among metagenomic samples from NGS reads without using reference databases or sequence assembly.
To investigate how our dataset of PCa samples clustered based on signatures subtracted for their normal tissue components, a hierarchical clustering of all subtracted cancer samples was performed and presented as a heatmap in Figure 2A.
Two prognostic platforms based on expression signatures are commercially available: OncotypeDx, based on a 21-gene signature measured on paraffin-embedded samples by polymerase chain reaction (PCR) [5], and Mammaprint, the 70-gene "Amsterdam signature" measured by a microarray [6], [7], [8], [9].
Based on extracted signatures, they perform dynamic placements of VMs to the hosts that have the highest match between VM resource usage signature and host free capacity signature.
