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Fig. 1 | BMC Genomics

Fig. 1

From: Strategies for detecting and identifying biological signals amidst the variation commonly found in RNA sequencing data

Fig. 1

Rank-ordering RNA sequencing counts identifies individuals displaying gene count divergence. a Box plots of sequencing counts for five genes INTS6, AKAP13, KCNJ2, IFIT3 and EIF1AY depicting increasing levels of sample dispersion with computed coefficient of variation values ranging from 17.9 to 171.2% of the unadjusted TPM gene counts (Mean ± 1SD). Box boundaries exclude individuals in the first and fourth quartile for each gene. b Rank-ordering the unadjusted counts of 35 individuals delineates different gene trendline patterns for the five genes. Gene rank-order position is established in relation to the gene expression level for an individual gene within the sample group, therefore the ranking order does not identify the same individual at each position along the various gene trendlines since the relative level of gene expression for an individual changes across genes. c Minimum Value Adjusted (MVA) gene counts significantly improve count heteroscedasticity (5-fold scale reduction) without altering the incremental trendline profiles within the sample group. Rank-order analysis extends the descriptive sample information available from a box plot by: defining the number of data points within the sample that deviate from the count level in the 2nd and 3rd quartiles; identifying their inflection point(s) and providing an estimate of the relative change in gene expression based on the computed slope ratio change. Black vertical lines identify quartiles 1, 2–3 and 4. See Additional file 1 for a more detailed discussion

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