Analyze growth of LCLs

Last updated: 2018-03-16

Code version: b7db3f0

library(dplyr)


Attaching package: 'dplyr'

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

library(tidyr)
library(ggplot2)

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)

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")

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")

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)

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)

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)

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)

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)

days:

1: saturday
2: sunday
3: monday
4: tuesday
5: wednesday
6: thursday

Before feeding was Feeding happend monday. I will add a verticle line this day.

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)

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)

add a day post split / feed column so I can plot by this:

days_post=c(1,2,3,1,2,3)
days_post= as.factor(days_post)

growth_e_DP= cbind(days_post,growth_e)

Session information

sessionInfo()

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  ggplot2_2.2.1 tidyr_0.7.2   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  stringi_1.1.6    compiler_3.4.2   pillar_1.1.0    
[29] scales_0.5.0     backports_1.1.2  pkgconfig_2.0.1

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Analyze growth of LCLs

Briana Mittleman

2018-03-16

Session information