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The log-transformation provides some measure of variance stabilization ( Law et al. , 2014 ), so that high-abundance genes with large variances do not dominate downstream analyses. The computed values are stored as an exprs matrix in addition to the other assay elements.

We check whether there are technical factors that contribute substantially to the heterogeneity of gene expression. If so, the factor may need to be regressed out to ensure that it does not inflate the variances or introduce spurious correlations. For this dataset, the simple experimental design means that there are no plate or batch effects to examine. Instead, we use the (log-transformed) total count for the spike-in transcripts as a proxy for the relative bias in each sample. This bias is purely technical in origin, given that the same amount of spike-in RNA should have been added to each cell. Thus, any association of gene expression with this factor is not biologically interesting and should be removed.

For each gene, we calculate the percentage of the variance of the expression values that is explained by the spike-in totals ( Figure 9 ). The percentages are generally small (1–3%), indicating that the expression profiles of most genes are not strongly associated with this factor. This result is consistent with successful removal of cell-specific biases by scaling normalization. Thus, the spike-in total does not need to be explicitly modelled in our downstream analyses.

For each gene, the percentage of the variance of the normalized log-expression values across cells that is explained by each factor is calculated. Each curve corresponds to one factor and represents the distribution of percentages across all genes.

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Note that the use of the spike-in total as an accurate proxy for the relative technical bias assumes that no library quantification is performed. Otherwise, the coverage of the spike-in transcripts would be dependent on the total amount of endogenous RNA in each cell. (Specifically, if the same amount of cDNA is used for sequencing per cell, any increase in the amount of endogenous RNA will suppress the coverage of the spike-in transcripts.) This means that the spike-in totals could be confounded with genuine biological effects associated with changes in RNA content.

We identify HVGs to focus on the genes that are driving heterogeneity across the population of cells. This requires estimation of the variance in expression for each gene, followed by decomposition of the variance into biological and technical components. HVGs are then identified as those genes with the largest biological components. This avoids prioritizing genes that are highly variable due to technical factors such as sampling noise during RNA capture and library preparation.

Ideally, the technical component would be estimated by fitting a mean-variance trend to the spike-in transcripts using the trendVar function. Recall that the same set of spike-ins was added in the same quantity to each cell. This means that the spike-in transcripts should exhibit no biological variability, i.e., any variance in their counts should be technical in origin. Given the mean abundance of a gene, the fitted value of the trend can be used as an estimate of the technical component for that gene. The biological component of the variance can then be calculated by subtracting the technical component from the total variance of each gene with the decomposeVar function.

by Heather Kelly @heatherkelly January 31, 2018: 5:20 PM ET
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Next, we attempted to analyze the AVA, RIM, and RIV neurons receiving inputs from ASH and AIB ( Fig 3B ). First, in the AVA interneurons of the wild type animals, both a weak ON calcium increase during stimulation and a strong OFF calcium increase after stimulation removal were observed ( Modal Scarf al blue and pink flowers by VIDA VIDA 9Af9Qp
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'). This result was similar to that of a previous study showing a weak and slow ON calcium increase during the stimulation of ChR2 in ASH neurons and a strong OFF calcium increase after stimulation [ 15 ]. The OFF calcium increase was selectively eliminated in the AVA interneurons of the lin-32 mutants ( Fig 3C ), although normal differentiation of the AVA neuron was suggested ( S2M Appendix , Tote Bag BLUE MOON XXXIV by VIDA VIDA WFFDkgs

We also analyzed the other downstream interneurons, namely, RIM and RIV ( Fig 3B ). The RIM response in the lin-32 mutants was similar to that of the wild type ( Fig 3D ), and no statistically significant differences were detected (p > 0.05, ANOVA followed by the Tukey's post hoc tests). The RIV response was weak and the difference between both strains was also within the error bar (SEM) range ( Womens Ripped Ashley Boyfriend Jeans New Look 9VGwB
), suggesting a weak contribution to optimization of probability of omega turn. These ON calcium increase of the AVA and RIM neurons upon stimulus onset in the wild type animals were consistent with those in the previous study [ 11 ].

Finally, since the defect in AIB differentiation might lead to the remodeling of neural circuits at developmental stages and/or the response of sensory neurons might be modified by feedback through secretory factors [ Mens Texas Necktie Monti szbbNhRk
, 27 ], we confirmed the response of ASH sensory neurons. Both animals showed strong biphasic ON and OFF calcium increases with 30 sec stimulation of 2 M and 1 M sorbitol solution ( Fig 3F and 3G ). In the wild type animals, duration of OFF-response peak were 4.05 ± 0.630 sec (means ± SEM) with 2 M sorbitol stimulus (n = 20) and 6.40 ± 0.859 sec with 1 M one (n = 20), showing significant difference between them (p < 0.05, Student’s t -tests). There is no significant difference between wild type animals and lin-32 mutants with 2 M and 1 M condition (p > 0.05, Student’s t -tests). These results might suggest that the lin-32 mutants have the processing defect in the central nervous system but not by a reduced sensation of sensory neurons, namely, "hypoesthesia".

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