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UID-tagging has been proposed as a way to minimize errors in observed count variation that are due to technical error, namely, PCR over-amplification. Therefore, we added UID-tags to our libraries during cDNA library synthesis, calculated the consensus sequences and performed parallel analysis to the raw data, discussed in Section 3.2.

The proportional clonal and count outcomes do not significantly differ from raw Ig-seq except in amplification PCR, which is sensible as sequence count discrepancies in observed raw versus UID-tagged output are introduced during PCR (Figure 3.5). As with raw data, the overall shared clonal representation is still poor among technical and biological replicates (Figure 3.6). As we observed with the non-consensus, non-UID count normalized data, there is little overlap in clonal capture between blood samples. Proportional count values show a greater overlap in general, but as before, this is likely a product of larger clones or clones with more immunoglobulin transcript production.

As in Section 3.2, we performed parallel exploratory analysis to determine the normality and variance of the data to determine appropriate statistical tests (Figure S3.5 and Table S3.3). The consensus data had similar characteristics to the raw, so we

continued by performing an omnibus ANOVA to confirm significant variance, followed by post hoc Tukey-Kramer and n-way ANOVA tests. As before, the Tukey-Kramer analysis did not reveal any specific factor that significantly contributed to the observed variance. Interestingly, the n-way ANOVA only resulted in one significant factor, which was an interacting factor, cDNA:PCR1. This is somewhat surprising as this represents two relatively upstream steps, however the interaction effect in this analysis (Figure

S3.5C) is more extreme than in the raw data (Figure S3.4). As with the raw data, these analyses are likely to be affected by the successive processing of samples and

Figure 3.5. Comparison of proportion unique clones and counts in raw versus consensus-called immunoglobulin sequencing (Ig-seq).

Ig-seq libraries were combined per subject with and without UID-based consensus calling (“UID” and “Raw,” respectively), and clones were inferred by Cloanalyst. For each step of library processing, the clones observed in biological or technical replicates were compared for presence or absence of the same clone in the sister replicate. The number of sequences within unique or shared clones was then summed per subject-replicate. The proportion of unique clones and sequence counts within these clones per subject- replicates were calculated. A value of 1.00 indicates that all clones or sequences in a sample are unique and were not observed in its analogous replicate. A value of 0 indicates that all clones or sequences in a sample were observed in its analogous

replicate. Horizontal lines represent the median value, and vertical lines, the interquartile range. Asterisks represent significance at p <0.05.

Figure 3.6. Clonal overlap of UID-tagged consensus sequences from biological and technical replicates.

Biological and technical replicate blood samples from two volunteers resulted in sixteen Ig-seq datasets (Illustration 3.1). These Ig-seq libraries were combined per subject with UID-based consensus calling and clones were inferred by Cloanalyst. For each step of library processing, the clones observed in biological or technical replicates were compared for presence or absence in the sister replicate. The leftmost column refers to the number of shared clones, and the rightmost column is the number of sequences within shared or unique clones. For instance, at the biological replicate step of blood collection, 6,088 total clones were identified. Of these, 1,867 were only observed in the first blood draw, and 3,427 were only observed in the second blood draw. 397 clones were observed in both blood draws. In this same comparison, 11,287 immunoglobulin sequences were observed. Of these, 2,544 sequences were assigned to clones only observed in the first blood draw, and 5,410 were assigned to clones only observed in the second blood draw. 3,333 sequences were assigned to the 397 clones that were observed in both samples.

A)

Df Sum Sq Mean Sq F value Pr(>F) Factor 5 0.7233 0.14466 2.883 0.0405* Residuals 20 1.0036 0.05018

B)

Difference Lower limit Upper limit Adj. p-value cDNA-Blood -0.0138 -0.5117 0.48411 1 PCR1-Blood -0.417 -0.8715 0.03756 0.08401 PCR2-Blood -0.0679 -0.5658 0.42999 0.99787 RNA-Blood -0.3089 -0.8068 0.18898 0.40287 Seq-Blood -0.2789 -0.7768 0.21895 0.51066 PCR1-cDNA -0.4032 -0.8577 0.05134 0.10093 PCR2-cDNA -0.0541 -0.552 0.44377 0.99928 RNA-cDNA -0.2951 -0.793 0.20276 0.45115 Seq-cDNA -0.2652 -0.7631 0.23273 0.5629 PCR2-PCR1 0.34906 -0.1055 0.80356 0.19836 RNA-PCR1 0.10804 -0.3465 0.56255 0.9732 Seq-PCR1 0.13801 -0.3165 0.59252 0.92688 RNA-PCR2 -0.241 -0.7389 0.25687 0.65525 Seq-PCR2 -0.211 -0.7089 0.28684 0.7643 Seq-RNA 0.02997 -0.4679 0.52786 0.99996 C)

Df Sum Sq Mean Sq F value Pr(>F) Blood 1 0.1876 0.18763 4.301 0.0585 RNA 1 0.0123 0.01233 0.283 0.6039 cDNA 1 0.1459 0.14591 3.345 0.0904 PCR1 1 0.1391 0.13905 3.188 0.0975 PCR2 1 0.1355 0.1355 3.106 0.1015 Seq 1 0.1137 0.11373 2.607 0.1304 Blood:RNA 1 0.0455 0.04553 1.044 0.3256 Blood:cDNA 1 0.012 0.01197 0.275 0.6091 cDNA:PCR1 _{1 0.2693 0.26925 6.172 0.0274*} RNA:PCR2 1 0.0099 0.00994 0.228 0.641 RNA:Seq 1 0.0615 0.06146 1.409 0.2565 PCR2:Seq 1 0.0275 0.02751 0.631 0.4414 Residuals 13 0.5671 0.04362

Table 3.2. ANOVA and post hoc tests to determine contribution to variance in UID consensus Ig-seq.

Following confirmation of data normality (Figure S3.3 and Table S3.1), statistical tests were performed to estimate the points of sample collection and processing that contribute the most variance to clonal observation. The values used for these tests are the proportion of unique clones of the total observed clones per subject-replicate for UID-consensus called Ig-seq. For instance, Subject A has two biological blood draw replicates; following comparison of observed clones for shared and unique clones, we can calculate the

proportion of unique clones for both blood draw 1 and blood draw 2. A) Omnibus ANOVA of all steps was performed to determine if the observed variance between factors was significant and appropriate for further tests. B) Tukey-Kramer multiple comparison procedure was used to test pairwise relationships for significant changes in the mean. C) N-way ANOVA was used to test independent and interacting factors for significant contributions to variance. The significance level was set at 0.05.