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<h1 class="title toc-ignore">Analyze growth of LCLs</h1>
<h4 class="author"><em>Briana Mittleman</em></h4>
<h4 class="date"><em>2018-03-16</em></h4>
</div>
<!-- The file analysis/chunks.R contains chunks that define default settings
shared across the workflowr files. -->
<!-- Update knitr chunk options -->
<!-- Insert the date the file was last updated -->
<p><strong>Last updated:</strong> 2018-03-21</p>
<!-- Insert the code version (Git commit SHA1) if Git repository exists and R
package git2r is installed -->
<p><strong>Code version:</strong> 93a4722</p>
<!-- Add your analysis here -->
<pre class="r"><code>library(dplyr)</code></pre>
<pre><code>
Attaching package: 'dplyr'</code></pre>
<pre><code>The following objects are masked from 'package:stats':
filter, lag</code></pre>
<pre><code>The following objects are masked from 'package:base':
intersect, setdiff, setequal, union</code></pre>
<pre class="r"><code>library(tidyr)
library(ggplot2)
library(reshape2)</code></pre>
<pre><code>Warning: package 'reshape2' was built under R version 3.4.3</code></pre>
<pre><code>
Attaching package: 'reshape2'</code></pre>
<pre><code>The following object is masked from 'package:tidyr':
smiths</code></pre>
<pre class="r"><code>growth=read.csv("../data/growth_curve_3.16.csv", header = TRUE)
#filter out control
growth_e= growth %>% filter(control=="e") %>% mutate(avg_h=(h_18486+ h_18499 + h_18502 + h_18504 + h_18510 + h_18517 + h_18523)/7) %>% mutate(avg_c=(c_c3641 + c_pt30 + c_pt91 + c_3610 + c_3659 + c_3622 + c_18358 + c_18359)/8)</code></pre>
<pre class="r"><code>plot(growth_e$avg_h, xlab="Day", ylim=c(0,2), ylab="cells/ml 10^6", main="Human growth by day")
lines(growth_e$h_18486, col="red")
lines(growth_e$h_18499, col="orange")
lines(growth_e$h_18502, col="yellow")
lines(growth_e$h_18504, col="green")
lines(growth_e$h_18510, col= "blue")
lines(growth_e$h_18517, col="purple")
lines(growth_e$h_18523, col="pink")</code></pre>
<p><img src="figure/LCL_growth.Rmd/unnamed-chunk-3-1.png" width="672" style="display: block; margin: auto;" /></p>
<pre class="r"><code>plot(growth_e$avg_c, xlab="Day", ylim=c(0,3), ylab="cells/ml 10^6", main="Chimp Growth by day")
lines(growth_e$c_c3641, col="red")
lines(growth_e$c_pt30, col="orange")
lines(growth_e$c_pt91, col="yellow")
lines(growth_e$c_3610, col="green")
lines(growth_e$c_3659, col= "blue")
lines(growth_e$c_18358, col="purple")
lines(growth_e$c_18359, col="pink")
lines(growth_e$c_3622, col="black")</code></pre>
<p><img src="figure/LCL_growth.Rmd/unnamed-chunk-4-1.png" width="672" style="display: block; margin: auto;" /></p>
<pre class="r"><code>alive=read.csv("../data/perc_alive_3.16.csv", header=TRUE)
alive_e= alive %>% filter(control=="e") %>% mutate(avg_h=(h_18486+ h_18499 + h_18502 + h_18504 + h_18510 + h_18517 + h_18523)/7) %>% mutate(avg_c=(c_c3641 + c_pt30 + c_pt91 + c_3610 + c_3659 + c_3622 + c_18358 + c_18359)/8)</code></pre>
<pre class="r"><code>plot(alive_e$avg_h, xlab="Day", ylim=c(50,100), ylab="Percent living", main="Human perc living by day")
lines(alive_e$h_18486, col="red")
lines(alive_e$h_18499, col="orange")
lines(alive_e$h_18502, col="yellow")
lines(alive_e$h_18504, col="green")
lines(alive_e$h_18510, col= "blue")
lines(alive_e$h_18517, col="purple")
lines(alive_e$h_18523, col="pink")
abline(v=3,lwd=3, lty=2)</code></pre>
<p><img src="figure/LCL_growth.Rmd/unnamed-chunk-6-1.png" width="672" style="display: block; margin: auto;" /></p>
<pre class="r"><code>plot(alive_e$avg_c, xlab="Day", ylim=c(0,100), ylab="Percent living", main="Chimp perc living by day")
lines(alive_e$c_c3641, col="red")
lines(alive_e$c_pt30, col="orange")
lines(alive_e$c_pt91, col="yellow")
lines(alive_e$c_3610, col="green")
lines(alive_e$c_3659, col= "blue")
lines(alive_e$c_18358, col="purple")
lines(alive_e$c_18359, col="pink")
lines(alive_e$c_3622, col="black")
abline(v=3,lwd=3, lty=2)</code></pre>
<p><img src="figure/LCL_growth.Rmd/unnamed-chunk-7-1.png" width="672" style="display: block; margin: auto;" /></p>
<pre class="r"><code>plot(alive_e$avg_h ~ growth_e$avg_h, ylab="Percent living", xlab="Concentration", ylim=c(50,100), pch=16, main="Human Percent living vs Concentration")
points(alive_e$h_18486 ~ growth_e$h_18486, col="red", pch=16)
points(alive_e$h_18499 ~ growth_e$h_18499, col="orange", pch=16)
points(alive_e$h_18502 ~ growth_e$h_18502, col="yellow", pch=16)
points(alive_e$h_18504 ~ growth_e$h_18504, col="green", pch=16)
points(alive_e$h_18510 ~ growth_e$h_18510, col="blue", pch=16)
points(alive_e$h_18517 ~ growth_e$h_18517, col="purple", pch=16)
points(alive_e$h_18523 ~ growth_e$h_18523, col="pink", pch=16)
reg= lm(alive_e$avg_h~ growth_e$avg_h)
coeff=coefficients(reg)
eq = paste0("y = ", round(coeff[2],1), "*x +", round(coeff[1],1))
abline(reg)
mtext(eq, side=4)</code></pre>
<p><img src="figure/LCL_growth.Rmd/unnamed-chunk-8-1.png" width="672" style="display: block; margin: auto;" /></p>
<pre class="r"><code>plot(alive_e$avg_c ~ growth_e$avg_c, ylab="Percent living", xlab="Concentration", ylim=c(0,100), pch=16, main="Chimp Percent living vs Concentration")
points(alive_e$c_c3641 ~ growth_e$c_c3641, col="red", pch=16)
points(alive_e$c_pt30 ~ growth_e$c_pt30, col="orange", pch=16)
points(alive_e$c_pt91 ~ growth_e$c_pt91, col="yellow", pch=16)
points(alive_e$c_3610 ~ growth_e$c_3610, col="green", pch=16)
points(alive_e$c_3659 ~ growth_e$c_3659, col="blue", pch=16)
points(alive_e$c_18358 ~ growth_e$c_18358, col="purple", pch=16)
points(alive_e$c_18359 ~ growth_e$c_18359, col="pink", pch=16)
points(alive_e$c_3622 ~ growth_e$c_3622, col="grey", pch=16)
reg= lm(alive_e$avg_c~ growth_e$avg_c)
coeff=coefficients(reg)
eq = paste0("y = ", round(coeff[2],1), "*x +", round(coeff[1],1))
abline(reg)
mtext(eq, side=4)</code></pre>
<p><img src="figure/LCL_growth.Rmd/unnamed-chunk-9-1.png" width="672" style="display: block; margin: auto;" /> days:</p>
<ul>
<li><p>1: saturday</p></li>
<li><p>2: sunday</p></li>
<li><p>3: monday</p></li>
<li><p>4: tuesday</p></li>
<li><p>5: wednesday</p></li>
<li><p>6: thursday</p></li>
</ul>
<p>Before feeding was Feeding happend monday. I will add a verticle line this day.</p>
<pre class="r"><code>plot(growth_e$avg_h, xlab="Day", ylim=c(0,2), ylab="cells/ml 10^6", main="Human growth by day")
lines(growth_e$h_18486, col="red")
lines(growth_e$h_18499, col="orange")
lines(growth_e$h_18502, col="yellow")
lines(growth_e$h_18504, col="green")
lines(growth_e$h_18510, col= "blue")
lines(growth_e$h_18517, col="purple")
lines(growth_e$h_18523, col="pink")
abline(v=3, lwd=3, lty=2)</code></pre>
<p><img src="figure/LCL_growth.Rmd/unnamed-chunk-10-1.png" width="672" style="display: block; margin: auto;" /></p>
<pre class="r"><code>plot(growth_e$avg_c, xlab="Day", ylim=c(0,3), ylab="cells/ml 10^6", main="Chimp Growth by day")
lines(growth_e$c_c3641, col="red")
lines(growth_e$c_pt30, col="orange")
lines(growth_e$c_pt91, col="yellow")
lines(growth_e$c_3610, col="green")
lines(growth_e$c_3659, col= "blue")
lines(growth_e$c_18358, col="purple")
lines(growth_e$c_18359, col="pink")
lines(growth_e$c_3622, col="black")
abline(v=3,lwd=3, lty=2)</code></pre>
<p><img src="figure/LCL_growth.Rmd/unnamed-chunk-11-1.png" width="672" style="display: block; margin: auto;" /></p>
<p>add a day post split / feed column so I can plot by this:</p>
<pre class="r"><code>days_post=c(1,2,3,1,2,3)
days_post= as.factor(days_post)
growth_e_DP= cbind(days_post,growth_e)</code></pre>
<div id="redo-analysis-on-321" class="section level3">
<h3>Redo analysis on 3/21</h3>
<pre class="r"><code>growth_3.21=read.csv("../data/cell_growth_3.21.18.csv", stringsAsFactors = FALSE)
growth_e1=growth_3.21 %>% filter(Type=="e1")
growth_e2=growth_3.21 %>% filter(Type=="e2")</code></pre>
<p>Melt the data:</p>
<pre class="r"><code>growth_e2_long=melt(growth_e2, variable.name = "key",value.names = "value", id.vars = c("Line", "Day")) %>% filter(key != "Type")</code></pre>
<p>Plot day vs allive:</p>
<pre class="r"><code>growth_e2_long_live=growth_e2_long %>% filter(key=="X..Live.average")
ggplot(growth_e2_long, aes(x=as.factor(Day), y=as.numeric(value), group_by(Line)))+ geom_violin()</code></pre>
<pre><code>Warning in fun(x, ...): NAs introduced by coercion</code></pre>
<pre><code>Warning in FUN(X[[i]], ...): NAs introduced by coercion</code></pre>
<pre><code>Warning: Removed 168 rows containing non-finite values (stat_ydensity).</code></pre>
<p><img src="figure/LCL_growth.Rmd/unnamed-chunk-15-1.png" width="672" style="display: block; margin: auto;" /></p>
<pre class="r"><code>growth_e1_day_group=growth_e1[order(growth_e1$Day),]
growth_e2_day_group=growth_e2[order(growth_e2$Day),]</code></pre>
<pre class="r"><code>par(mfrow=c(1,2))
plot(as.factor(growth_e1_day_group$Day),as.numeric(growth_e1_day_group$X..Live.average), main="Percent living by day, E1", ylab="Percent alive" )</code></pre>
<pre><code>Warning in is.factor(y): NAs introduced by coercion</code></pre>
<pre class="r"><code>plot(as.factor(growth_e2_day_group$Day),as.numeric(growth_e2_day_group$X..Live.average), main="Percent living by day, E2", ylab="Percent alive")</code></pre>
<p><img src="figure/LCL_growth.Rmd/unnamed-chunk-17-1.png" width="672" style="display: block; margin: auto;" /></p>
<pre class="r"><code>growth_e1_long_av=melt(growth_e1, variable.name = "key",value.names = "value", id.vars = c("Line", "Day")) %>% filter(key=="Average")
ggplot(growth_e1_long_av,aes(x=as.factor(Day), y=as.numeric(value))) + geom_point(aes(col=Line)) + labs(x="Day", y="cells/ml in 10^6") + ggtitle("Cell density")</code></pre>
<pre><code>Warning in fun(x, ...): NAs introduced by coercion</code></pre>
<pre><code>Warning in FUN(X[[i]], ...): NAs introduced by coercion</code></pre>
<pre><code>Warning: Removed 1 rows containing missing values (geom_point).</code></pre>
<p><img src="figure/LCL_growth.Rmd/unnamed-chunk-18-1.png" width="672" style="display: block; margin: auto;" /></p>
<pre class="r"><code>growth_e1_long_live=melt(growth_e1, variable.name = "key",value.names = "value", id.vars = c("Line", "Day")) %>% filter(key=="X..Live.average")
ggplot(growth_e1_long_live,aes(x=as.factor(Day), y=as.numeric(value))) + geom_point(aes(col=Line)) + labs(x="Day", y="Percent Alive") + ggtitle("% Alive by day and line")</code></pre>
<pre><code>Warning in fun(x, ...): NAs introduced by coercion</code></pre>
<pre><code>Warning in FUN(X[[i]], ...): NAs introduced by coercion</code></pre>
<pre><code>Warning: Removed 1 rows containing missing values (geom_point).</code></pre>
<p><img src="figure/LCL_growth.Rmd/unnamed-chunk-19-1.png" width="672" style="display: block; margin: auto;" /></p>
<pre class="r"><code>species=c(rep("H", 84), rep("C", 84))
palette(c("red", "blue"))
growth_e1=cbind(growth_e1, species)
plot(as.numeric(growth_e1$Average)~as.numeric(growth_e1$X..Live.average),col=as.factor(growth_e1$species),pch=16, main="Density vs Percent alive", ylab="Cells/ML in 10^6", xlab="Percent Alive")</code></pre>
<pre><code>Warning in eval(predvars, data, env): NAs introduced by coercion
Warning in eval(predvars, data, env): NAs introduced by coercion</code></pre>
<pre class="r"><code>legend("topleft", legend=c("Humans", "Chimps"),
col=c("red", "blue"), pch=16, cex=1, title="Species")</code></pre>
<p><img src="figure/LCL_growth.Rmd/unnamed-chunk-20-1.png" width="672" style="display: block; margin: auto;" /></p>
</div>
<div id="session-information" class="section level2">
<h2>Session information</h2>
<!-- Insert the session information into the document -->
<pre class="r"><code>sessionInfo()</code></pre>
<pre><code>R version 3.4.2 (2017-09-28)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Sierra 10.12.6
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] bindrcpp_0.2 reshape2_1.4.3 ggplot2_2.2.1 tidyr_0.7.2
[5] dplyr_0.7.4
loaded via a namespace (and not attached):
[1] Rcpp_0.12.15 knitr_1.18 bindr_0.1 magrittr_1.5
[5] munsell_0.4.3 colorspace_1.3-2 R6_2.2.2 rlang_0.1.6
[9] plyr_1.8.4 stringr_1.2.0 tools_3.4.2 grid_3.4.2
[13] gtable_0.2.0 git2r_0.21.0 htmltools_0.3.6 lazyeval_0.2.1
[17] yaml_2.1.16 rprojroot_1.3-2 digest_0.6.14 assertthat_0.2.0
[21] tibble_1.4.2 purrr_0.2.4 glue_1.2.0 evaluate_0.10.1
[25] rmarkdown_1.8.5 labeling_0.3 stringi_1.1.6 compiler_3.4.2
[29] pillar_1.1.0 scales_0.5.0 backports_1.1.2 pkgconfig_2.0.1 </code></pre>
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