Starvation Resistance Analysis: Phase 1.1
Last updated: 2022-11-05
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Knit directory: dgrp-starve/
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Setup
The analysis begins with the data files prepared in Phase 1. MAKE THIS LINK TO DATA PREP. The goal of these steps was to visually characterize the data and form the basis for groupings of DGRP lines for further analysis.
Histograms of Starvation Resistance
First impressions on histograms are that both are unimodal with light skew to the right.
Comparative Boxplot
Plotting the distributions on the same axis gives a more accurate representation of distribution between sexes. The average female has a much greate starvation resistance of 60.665 compared to the average male 45.732. Outliers to the right of both boxplots indicate right skew.
Scatter Plot
The trendline is a Simple Linear Regression ‘reg=lm(y~x)’ with R and p values extracted from summary statistics. The slope indicates a positive correlation between male and female starvation resistance while the R value of 0.4693 indicates that the correlation is not close to linear.
QQ Plots
There is some systematic deviation from linearity on both tails in Females and on the upper tail in Males. Eyeballing is not an exhaustive measure of discerning normality. Quantitative methods are needed:
Shapiro-Wilk Tests for Normality
The Shapiro-Wilk method was chosen as it retains the best power
The test statistic formula is: \[ W = \frac{(\sum_{i=1}^{n}a_ix_{(i)})^{2}}{\sum_{i=1}^{n}(x_i-\overline x)^{2}} \]
More about the Shapiro-Wilk test statistic can be found here.
\(W\) values closer to 1 indicate normality, with \(W = 1\) being perfectly normal.
Shapiro-Wilk normality test
data: Female_Starvation
W = 0.97662, p-value = 0.001826
Shapiro-Wilk normality test
data: Male_Starvation
W = 0.98745, p-value = 0.07023At an \(\alpha\) level of 0.05, we have sufficient evidence to reject that the Female population is normally distributed as p < \(\alpha\) (0.001826 < 0.05).
At an \(\alpha\) level of 0.05, we do not have sufficient evidence to reject that the Male population is normally distributed as p > \(\alpha\) (0.07023 > 0.05). Notably the correlation is not strong as the p-value is close to 0.05.
Group selection
The two groups of interest were extreme values on both ends
Along with this, I wanted to look at the highest and lowest averages. Twenty lines from the top and bottom were partitioned for further analysis. With these four groups, I sought to look for difference in genotype and fold-change in gene expression hereLINK to PHASE ??? not sure yet
sessionInfo()R version 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)
Matrix products: default
BLAS/LAPACK: /opt/ohpc/pub/Software/openblas_0.3.10/lib/libopenblas_haswellp-r0.3.10.dev.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] tibble_3.1.6 dplyr_1.0.8 tidyr_1.2.0
loaded via a namespace (and not attached):
[1] Rcpp_1.0.8.3 highr_0.9 pillar_1.7.0 compiler_4.0.3
[5] bslib_0.3.1 later_1.3.0 jquerylib_0.1.4 git2r_0.30.1
[9] workflowr_1.7.0 tools_4.0.3 digest_0.6.29 jsonlite_1.8.0
[13] evaluate_0.15 lifecycle_1.0.1 pkgconfig_2.0.3 rlang_1.0.4
[17] DBI_1.1.2 cli_3.3.0 rstudioapi_0.13 yaml_2.3.5
[21] xfun_0.30 fastmap_1.1.0 stringr_1.4.0 knitr_1.38
[25] generics_0.1.2 fs_1.5.2 vctrs_0.4.1 sass_0.4.1
[29] tidyselect_1.1.2 rprojroot_2.0.3 glue_1.6.2 R6_2.5.1
[33] fansi_1.0.3 rmarkdown_2.16 purrr_0.3.4 magrittr_2.0.3
[37] whisker_0.4 promises_1.2.0.1 ellipsis_0.3.2 htmltools_0.5.2
[41] assertthat_0.2.1 httpuv_1.6.5 utf8_1.2.2 stringi_1.7.6
[45] crayon_1.5.1