RNA-sequencing (RNA-seq) technologies have not only pushed the boundaries of science, but also pushed the computational and analytic capacities of many laboratories. With respect to mapping and quantifying transcriptomes, RNA-seq has certainly established itself as the approach of choice. However, as the complexities of experiments continue to grow, there is still no standard practice that allows for design, processing, normalization, efficient dimension reduction and/or statistical analysis. With this in mind, we provide a brief review of some of the key challenges that are general to all RNA-seq experiments, namely experimental design, statistical analysis and dimensionality reduction.
This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 13 January 2012.
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#20,779,053
of 33,079,649 outputs
Outputs from Briefings in Functional Genomics
#401
of 652 outputs
Outputs of similar age
#208,173
of 296,423 outputs
Outputs of similar age from Briefings in Functional Genomics
#8
of 10 outputs
Altmetric has tracked 33,079,649 research outputs across all sources so far. This one is in the 36th percentile – i.e., 36% of other outputs scored the same or lower than it.
So far Altmetric has tracked 652 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one is in the 37th percentile – i.e., 37% of its peers scored the same or lower than it.
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We're also able to compare this research output to 10 others from the same source and published within six weeks on either side of this one. This one has scored higher than 2 of them.
