Typical dendrograms obtained for plasmode datasets from MSUPRP experimental data with two dissimilarity measures.

<p>Dendrograms using complete linkage based on (a) Poisson dissimilarity (<i>poi</i>), (b) Euclidean distance calculated from raw normalized data (<i>rnr</i>), and (c) reference dissimilarity based on maximum proportion of shared reads. Original samples are labeled with 3 same letters (AAA, BBB, CCC, DDD or DDD), synthetic samples are labelled with 2 or 3 letters symbolizing the proportion of transcripts, ½ or ⅓ respectively, taken from the original samples. Dendrogram (a) clustered original samples A, B and C and their synthetic combinations in one group, and original samples E and D and their synthetic combinations in another group. The hierarchical structure of each of these two groups represented the degree of shared reads between samples by joining first samples that shared ⅓ of reads and then samples that shared ½ of reads. Dendrogram (b) did not cluster samples according to the expected configuration.</p

Thermal Hatch Data File

File contains data for the 1200 larvae measured at hatch. Data includes individual ID codes, thermal incubation treatment the individual incubated in from egg to hatch, family code (each code represents a cross between 1 female and 1 male; families were half-sibling groups with one female crossed with two males therefore the half-sib groups include A and B, C and D, E and F, G and H, and I and J), and measurements for the three phenotypic traits quantified at hatch: body length (mm), body area (mm), and yolk-sac area (mm^2). Note: Thermal incubation treatments were coded as A=Warm, B=Variable, C=Ambient, and D=Cold prior to analysis of the data

Algorithm used to generate plasmodes from Bottomly dataset.

<p>Algorithm used to generate plasmodes from Bottomly dataset.</p

Typical dendrograms obtained for plasmode datasets from Bottomly experimental data with two dissimilarity measures under three scenarios.

<p>Dendrograms obtained using complete linkage hierarchical clustering based on Poisson dissimilarity (<i>poi</i>) are presented in the left column (a, c and e), and dendrograms based on Euclidean distance calculated from raw normalized data (<i>rnr</i>) are presented in right column (b, d, f). The rows correspond to three scenarios with different percentage of differentially expressed (DE) transcripts: 1) DE_[100%] (a and b), 2) DE_[10%]+nonDE_[90%] (c and d), and 3) DE_[20%]+nonDE_[80%] (e and f). Sample labels correspond to main treatment (A or B) and flowcell number (4, 6 or 7). Dendrograms based on <i>poi</i> separates samples according to the expected sources of variation; in (a), only DE transcripts, samples are arranged in two separate groups following treatment labels; in (c), with a predominant number of non DE transcripts, the structure of groups is dominated by flowcell characteristics in addition to main treatment: and in (e) an in-between scenario, the dendrogram presents an intermediate group structure. Dendrograms based on <i>rnr</i> do not resemble any expected configuration.</p

Agreement between dissimilarity measures using MSUPRP plasmode datasets.

<p>The matrix contains means (upper triangle) and standard deviations (lower triangle) of correlation between cophenetic matrices of dendrograms (N = 50 plasmode datasets) for eight dissimilarity measures: Euclidean distances using raw count data (<i>raw</i>), Euclidean distances using normalized samples (<i>rnr</i>), Euclidean distances using variance stabilizing transformation (<i>vsd</i>), Euclidean distances using regularized logarithm (<i>rld)</i> 1- Pearson correlation using raw counts (<i>pea</i>), 1- Pearson correlation using counts transformed by logarithm of raw counts +1 (<i>plg</i>), and 1- Spearman correlation using raw counts (<i>spe</i>). We identified the same three sets of dissimilarity measures described before: 1) <i>raw</i>, 2) <i>rnr</i> and <i>pea</i>, and 3) <i>poi</i>, <i>rld</i>, <i>vsd</i>, <i>plg</i> and <i>spe</i>.</p

Consistency for each dissimilarity measure.

<p>Consistency for each dissimilarity measure.</p