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Solenopsins_tryp_raw_data
#********Script written by Eduardo GP Fox with R version 3.5.1 (1st Version: 2018-07-02; Final Version 2020-03-12), RStudio v.1.1.456*********
#Includes Raw Data, Statistical Analyses, and Scripts behind plots for the paper
#Venom alkaloids against Chagas disease parasite: Search for effective therapies, by Costa Silva et al. 2020
#Importing packages for data formatting and preparing plots
require(ggplot2)
require(plyr)
require(reshape2)
require(drc)
require(tidyr)
#New functions useful in this plotting script
#Functions to simulate colMeans with minimum and maximum values; obtained from SE @defoo
#link https://stackoverflow.com/questions/7824912/max-and-min-functions-that-are-similar-to-colmeans
#for maximum values per column
colMax <- function (colData) {
apply(colData, MARGIN=c(2), max)
}
#for minimum values per column
colMin <- function (colData) {
apply(colData, MARGIN=c(2), min)
}
#########RAW DATA #############
#Importing data from Prism 7 file provided originally by Norton Heise
#CL-Brener strain epimastigote tests
#[I am unsure why data structure looks so fragmented]
Table.6<-data.frame(
CL_Invicta_1 = c(0, 0, 0, NA, 0, NA, NA, 0, NA, NA, 0, NA, NA, NA,
NA, 31, 37, NA, NA, 85.7, NA, NA, NA, NA, NA, 98.2,
NA, NA, NA, 99.1, NA, NA, 100, 100, 100, NA, 100,
100, NA, 100, 100),
CL_Invicta_2 = c(0, NA, 0, NA, 0, NA, NA, 0, NA, NA, 0, NA, NA, NA,
NA, 12.5, 33, NA, NA, 88.7, NA, NA, NA, NA, NA,
98.6, NA, NA, NA, 99.3, NA, NA, 100, 100, 98, NA, 100,
NA, 100, 100, 100),
CL_Invicta_3 = c(0, NA, 0, NA, NA, NA, NA, 0, NA, NA, 25, NA, NA,
NA, NA, 18, 36, NA, NA, 98.2, NA, NA, NA, NA, NA,
98.2, NA, NA, NA, 100, NA, NA, 100, 100, 100, NA, 100,
NA, NA, 100, NA),
CL_Invicta_4 = c(0, NA, 0, NA, NA, NA, NA, 0, NA, NA, 0, NA, NA, NA,
NA, 33.8, 97.1, NA, NA, 98.6, NA, NA, NA, NA, NA,
98.6, NA, NA, NA, 100, NA, NA, 99.9, 100, 100, NA, 99,
NA, NA, 100, NA),
CL_PI3K.Invicta_1 = c(0, NA, 0, NA, NA, 0, NA, NA, 7.7, NA, 20.4, NA, NA,
NA, NA, 60, 96.6, NA, NA, 86.6, NA, NA, NA, NA, NA,
99.2, NA, 98.2, NA, 99.6, NA, NA, 100, NA, NA, 100,
NA, NA, NA, NA, NA),
CL_PI3K.Invicta_2 = c(0, NA, 0, NA, NA, 0, NA, NA, 11.6, NA, 16.9, NA,
NA, NA, NA, 53.5, 81.17, NA, NA, 95.8, NA, NA, NA,
NA, NA, 99.3, NA, 98.6, NA, 99.8, NA, NA, 100, NA,
NA, 100, NA, NA, NA, NA, NA),
CL_PI3K.Invicta_3 = c(0, NA, NA, NA, NA, NA, NA, NA, 0, NA, 0, NA, NA,
NA, NA, 53.6, 24.7, NA, NA, 85.5, NA, NA, NA, NA,
NA, 98.6, NA, 99.3, NA, 98.6, NA, NA, 100, NA, NA, NA,
NA, NA, NA, NA, NA),
CL_PI3K.Invicta_4 = c(NA, NA, NA, NA, NA, NA, NA, NA, 0, NA, 0, NA, NA,
NA, NA, 53, NA, NA, NA, 95.4, NA, NA, NA, NA, NA,
98.2, NA, 99.1, NA, 100, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA),
CL_Saevissima_1 = c(NA, NA, 0, NA, NA, 0, NA, NA, 0, NA, 18.7, 30, NA,
NA, NA, 59.4, 66.5, NA, NA, NA, 96.8, NA, NA, NA,
NA, 98.9, NA, NA, NA, 99.7, NA, NA, 99.7, NA, NA, NA,
NA, NA, NA, NA, NA),
CL_Saevissima_2 = c(NA, NA, NA, NA, NA, 0, NA, NA, 14, NA, 5.8, 29.5,
NA, NA, NA, 63.8, 39.9, NA, NA, NA, 98.9, NA, NA,
NA, NA, 99.7, NA, NA, NA, 100, NA, NA, 100, NA, NA,
NA, NA, NA, NA, NA, NA),
CL_Saevissima_3 = c(NA, NA, NA, NA, NA, NA, NA, NA, 0, NA, 0, 20.7, NA,
NA, NA, 32.4, 76, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, 100, NA, NA, 100, NA, NA, NA, NA, NA,
NA, NA, NA),
CL_Saevissima_4 = c(NA, NA, NA, NA, NA, NA, NA, NA, 0, NA, 43, 31.5,
NA, NA, NA, NA, 92.8, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, 100, NA, NA, 100, NA, NA, NA, NA,
NA, NA, NA, NA),
CL_PI3K.Saevissima_1 = c(NA, NA, 0, NA, 0, 0, NA, NA, 3.3, NA, NA, NA, 37.3,
NA, NA, 46.1, NA, 75.3, NA, NA, NA, NA, 99.6, NA,
NA, NA, 99.6, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA),
CL_PI3K.Saevissima_2 = c(NA, NA, 0, NA, 0, 2, NA, NA, 7.7, NA, NA, NA, 31.7,
NA, NA, 81.2, NA, 87.3, NA, NA, NA, NA, 99.2, NA,
NA, NA, 100, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA),
CL_PI3K.Saevissima_3 = c(NA, NA, NA, NA, NA, 3.5, NA, NA, 0, NA, NA, NA, NA,
NA, NA, NA, NA, 78.8, NA, NA, NA, NA, 99.2, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA),
CL_PI3K.Saevissima_4 = c(NA, NA, NA, NA, NA, 0, NA, NA, 0, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA)
)
#Reconstructing table from original in Prism 7 file
#Scaling x axis for Table 6 and Table 10 as Log
Log.scale<-c(0, 0.699, 1, 1.301, 1.602, 1.81, 1.875, 2, 2.11, 2.176, 2.301,
2.39, 2.41, 2.478, 2.574, 2.602, 2.699, 2.71, 2.778, 2.875, 2.903,
3, 3.01, 3.079, 3.176, 3.19, 3.3, 3.37, 3.398, 3.477, 3.574,
3.699, 3.778, 3.875, 4, 4.079, 4.301, 4.477, 4.602, 4.903, 5)
row.names(Table.6)<-Log.scale
#Dm-28c epimastigote tests; [Not sure why data structure looks fragmented]
Table.10<-data.frame(
Dm_28c_Invicta_1 = c(NA, NA, 0, NA, NA, NA, NA, NA, NA, NA, 18.7, NA,
NA, NA, NA, 86.4, NA, NA, NA, NA, 96.9, NA, NA,
NA, NA, 98.9, NA, NA, NA, 99.7, NA, NA, 99.7, NA, 100,
NA, NA, NA, 100, NA, 100),
Dm_28c_Invicta_2 = c(NA, NA, 0, NA, NA, NA, NA, NA, NA, NA, 5.9, NA,
NA, NA, NA, 32.4, NA, NA, NA, NA, 96.8, NA, NA,
NA, NA, 99, NA, NA, NA, 99.7, NA, NA, 99.7, NA, 100,
NA, NA, NA, 100, NA, 100),
Dm_28c_Invicta_3 = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, 67.6, NA, NA, NA,
NA, 99.1, NA, NA, NA, NA, NA, NA, 100, NA, NA, NA,
NA, NA, NA, NA, NA),
Dm_28c_Invicta_4 = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, 100, NA, NA, NA, NA,
NA, NA, NA, NA),
Dm_28c_Saevissima_1 = c(NA, NA, 0, NA, NA, 0, NA, NA, 23.9, NA, NA, NA,
33.8, NA, NA, NA, NA, 89.6, NA, NA, NA, NA, 98.9,
NA, NA, NA, 99.7, NA, NA, NA, NA, 100, NA, NA, NA,
100, NA, 100, NA, NA, 100),
Dm_28c_Saevissima_2 = c(NA, NA, 0, NA, NA, 0, NA, NA, 24, NA, NA, NA,
43.7, NA, NA, NA, NA, 89.6, NA, NA, NA, NA, 98.9,
NA, NA, NA, 99.8, NA, NA, NA, NA, 100, NA, NA, NA,
100, NA, 100, NA, NA, 100),
Dm_28c_Saevissima_3 = c(NA, NA, NA, NA, NA, 11.1, NA, NA, 0, NA, NA, NA,
29.4, NA, NA, NA, NA, 39.7, NA, NA, NA, NA, 99.7,
NA, NA, NA, 100, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA),
Dm_28c_Saevissima_4 = c(NA, NA, NA, NA, NA, NA, NA, NA, 0, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, 100, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA),
Dm_28c_Miltefosine_1 = c(0, 0, 0, 0, NA, NA, 0, 0, NA, 1.51, 3.4, NA, NA,
5.83, 10, 21.2, 0, NA, 11.56, 34, 37.12, 59, NA,
59, 65.71, NA, 67.05, NA, 78.6, 90, NA, 97.2, 99.14,
NA, 99.42, 99.52, 100, 100, 100, 100, 100),
Dm_28c_Miltefosine_2 = c(0, 0, 0, 0, NA, NA, 0, 0, NA, 1, NA, NA, NA, 0, 9,
7.57, 13.33, NA, 15, 35, NA, 38, NA, NA, 50, NA,
NA, NA, NA, 70, NA, 95.25, 91, NA, 96.86, 97, 99.61,
NA, NA, NA, NA),
Dm_28c_Miltefosine_3 = c(0, 0, 0, 0, NA, NA, 0, 0, NA, 0, NA, NA, NA, 6.12,
21, NA, NA, NA, 21.56, NA, NA, 29.41, NA, NA, 46,
NA, NA, NA, NA, 95, NA, 85.09, 84, NA, NA, 90, NA,
NA, NA, NA, NA),
Dm_28c_Miltefosine_4 = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, 33.75, NA, NA, NA, NA, NA,
86, NA, NA, NA, NA, NA, NA, NA, 100, NA, NA, NA,
NA, NA, NA, NA, NA),
Dm_28c_Benznidazole_1 = c(0, 0, 0, 0, 0, NA, NA, NA, NA, 0, NA, NA, NA, 0,
0, NA, 0, NA, 0, 10, NA, 11, NA, NA, 16, NA, NA,
NA, 14.08, NA, 29, 34.28, NA, 50, 57.14, NA, 62.85,
NA, 83, 95, 100),
Dm_28c_Benznidazole_2 = c(0, 0, 0, 0, 0, NA, NA, NA, NA, 0, NA, NA, NA, 0,
0, NA, 5, NA, 5, NA, NA, 14.28, NA, NA, NA, NA,
NA, NA, 22.53, NA, NA, 30.42, NA, 42, 65.63, NA,
74.65, NA, 95.63, 99, 100),
Dm_28c_Benznidazole_3 = c(0, 0, 0, 0, 0, NA, NA, NA, NA, 0, NA, NA, NA, 0,
0, NA, NA, NA, 5, NA, NA, 16.66, NA, NA, NA, NA,
NA, NA, 23.75, NA, NA, 24.51, NA, 60, 61.13, NA,
68.45, NA, 82.83, NA, 100),
Dm_28c_Benznidazole_4 = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, 39, NA, NA, 62, NA, 56, 65, NA, 98,
NA, 78, NA, NA),
Dm_28c_Benznidazole_5 = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, 88, NA, NA, NA, NA, NA,
NA, NA, NA, NA)
)
#Reconstructing the table like original data on Prism 7
row.names(Table.10)<-Log.scale
#CL cell toxicity assays -- Imported original data from Prism 7
Table.11<-data.frame(
Cells_CL_1 = c(2e+05, 180000, 355000, 595000, 1050000, 1520000,
1760000, 2480000, 4360000, 2e+05, 260000, 315000,
325000, 530000, 735000, 890000, 1050000, 1370000),
Cells_CL_2 = c(2e+05, 280000, 280000, 475000, 725000, 1460000,
2340000, 2880000, 3640000, 2e+05, 180000, 315000,
4e+05, 5e+05, 695000, 755000, 1133000, 1440000),
Cells_CL_3 = c(2e+05, 280000, 280000, 475000, 725000, 1460000,
2340000, 2880000, 3640000, 2e+05, 180000, 315000,
4e+05, 5e+05, 695000, 755000, 1133000, 1440000),
Cells_CL_Invicta_1 = c(2e+05, 190000, 245000, 320000, 355000, 320000,
250000, 450000, 290000, 2e+05, 320000, 3e+05,
380000, 640000, 730000, 905000, 1360000, 1940000),
Cells_CL_Invicta_2 = c(2e+05, 175000, 235000, 270000, 280000, 240000,
360000, 4e+05, 450000, 2e+05, 280000, 330000,
480000, 540000, 630000, 805000, 1060000, 1240000),
Cells_CL_Saevissima_1 = c(2e+05, 195000, 285000, 415000, 475000, 640000,
985000, 1200000, 1030000, 2e+05, 210000, 265000,
365000, 595000, 685000, 865000, 1075000, 1510000),
Cells_CL_Saevissima_2 = c(2e+05, 175000, 220000, 410000, 505000, 760000,
1065000, 930000, 1040000, 2e+05, 225000, 345000,
380000, 490000, 635000, 740000, 1340000, 1260000)
)
#Reconstructing the table as originally in Prism 7 file
#Scale to be used as x axis for plotting Table 11 (Days)
scale<-c(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17)
row.names(Table.11)<-scale
#formatting the tables to make them fit for plotting
#Table.6
long.Table.6<-melt(Table.6)
long.Table.6$L1<-Log.scale
L2<-c(rep("1",41), rep("2", 41), rep("3",41), rep("4", 41), rep("1",41), rep("2", 41),
rep("3",41), rep("4", 41), rep("1",41), rep("2", 41),rep("3",41), rep("4", 41), rep("1",41),
rep("2", 41), rep("3",41), rep("4", 41))
long.Table.6$L2<-L2
new.variable<-rep(c("Invicta", "PI3K.Invicta", "Saevissima", "PI3K.Saevissima"), rep(164,4))
long.Table.6$variable<-new.variable
summary.Table.6<-ddply(long.Table.6,
c("variable","L1"),
summarise,
min = min(value, na.rm=T),
max = max(value, na.rm=t),
mean = mean(value, na.rm=T),
sd = sd(value, na.rm=T),
se = sd/sqrt(length(value)))
#Notes: Table 6 on CL-Brener will not allow ligating points because of the numerous NA values.
# The preffered strategy was drc regression for a good fit to dose-response experiments
#function formula at method LL.4: f(x) = c + \frac{d-c}{1+\exp(b(\log(x)-\log(e)))} where e== inflection point
#an example of inflextion point estimation with Replicate 1 for personal assessment (remove hashtags for a functional code)
#x1<-na.omit(data.frame(rownames(Table.6), Table.6[,1]))[,1]
#y1<-na.omit(data.frame(rownames(Table.6), Table.6[,1]))[,2]
#plot(drm(formula = na.omit(data.frame(rownames(Table.6), Table.6[,1]))[,2] ~ as.numeric(as.vector(na.omit(data.frame(rownames(Table.6), Table.6[,1]))[,1])), logDose = exp(1), fct = LL2.4()), type="n",bty="n", xlim=c(1,5),lwd =2)
#Obtained inflection points of replicates
#CL.Invicta.1; e = 2.721571 = 526.7093
#CL.Invicta.2; e = 2.742 = 552.0774
#CL.Invicta.3; e = 2.7246 = 530.3957
#CL.Invicta.4; e = 2.616922 = 413.9253
#CL.PI3K.Invicta.1; e = 2.4898925 = 308.9531
#CL.PI3K.Invicta.2; e = 2.5430 = 349.1403
#CL.PI3K.Invicta.3; e = 2.709608 = 512.3987
#CL.PI3K.Invicta.4; e = 2.592081 = 390.9138
#CL.Saevissima.1; e = 2.550610 = 355.3121
#CL.Saevissima.2; e = 2.613 = 410.2041
#CL.Saevissima.3; e = 2.630977 = 427.5402
#CL.Saevissima.4; e = 2.429164 = 268.6359
#CL.PI3K.Saevissima.1; e = 2.574886 = 375.7388
#CL.PI3K.Saevissima.2; e = 2.476 = 299.2265
#CL.PI3K.Saevissima.3; e = 2.610 = 407.3803
#CL.PI3K.Saevissima.4; e = NO RESULT!
#Saving the data for stats
LD50.T6<-data.frame(
Invicta = c(526.7093, 552.0774, 530.3957, 413.9253),
PI3K.Invicta = c(308.9531, 349.1403, 512.3987, 390.9138),
Saevissima = c(355.3121, 410.2041, 427.5402, 268.6359),
PI3K.Saevissima = c(375.7388, 299.2265, 407.3803, NA)
)
lapply(LD50.T6, sd, na.rm=T)
lapply(LD50.T6, mean, na.rm=T)
#formatting the other table to make it fit for plotting
#Dm28c results with epimastigotes
long.Table.10<-melt(Table.10)
long.Table.10$L1<-Log.scale
L2<-c(rep("1",41), rep("2", 41), rep("3",41), rep("4", 41), rep("1",41), rep("2", 41),
rep("3",41), rep("4", 41),rep("1",41), rep("2", 41),rep("3",41), rep("4", 41), rep("1",41),
rep("2", 41), rep("3",41), rep("4", 41), rep("5", 41))
long.Table.10$L2<-L2
new.variable<-rep(c("Invicta", "Saevissima", "Miltefosine", "Benznidazole"),
c(164, 164, 164, 205))
long.Table.10$variable<-new.variable
summary.Table.10<-ddply(long.Table.10,
c("variable","L1"),
summarise,
min=min(value, na.rm=T),
max = max(value, na.rm=t),
mean = mean(value, na.rm=T),
sd = sd(value, na.rm=T),
se = sd/sqrt(length(value)))
#[Table 10 will also not allow for ligating points because of many NA values.]
#Notes: Finding out concentration at 50% (== inflexion points) and 90% inhibition
Dm28cI1<-drm(formula = na.omit(data.frame(rownames(Table.10), Table.10[,1]))[,2] ~ as.numeric(as.vector(na.omit(data.frame(rownames(Table.10), Table.10[,1]))[,1])), logDose = exp(1), fct = LL2.4())
Dm28cI2<-drm(formula = na.omit(data.frame(rownames(Table.10), Table.10[,2]))[,2] ~ as.numeric(as.vector(na.omit(data.frame(rownames(Table.10), Table.10[,2]))[,1])), logDose = exp(1), fct = LL2.4())
Dm28cI3<-drm(formula = na.omit(data.frame(rownames(Table.10), Table.10[,3]))[,2] ~ as.numeric(as.vector(na.omit(data.frame(rownames(Table.10), Table.10[,3]))[,1])), logDose = exp(1), fct = LL2.4())
Dm28cI4<-drm(formula = na.omit(data.frame(rownames(Table.10), Table.10[,4]))[,2] ~ as.numeric(as.vector(na.omit(data.frame(rownames(Table.10), Table.10[,4]))[,1])), logDose = exp(1), fct = LL2.4())
Dm28cS1<-drm(formula = na.omit(data.frame(rownames(Table.10), Table.10[,5]))[,2] ~ as.numeric(as.vector(na.omit(data.frame(rownames(Table.10), Table.10[,5]))[,1])), logDose = exp(1), fct = LL2.4())
Dm28cS2<-drm(formula = na.omit(data.frame(rownames(Table.10), Table.10[,6]))[,2] ~ as.numeric(as.vector(na.omit(data.frame(rownames(Table.10), Table.10[,6]))[,1])), logDose = exp(1), fct = LL2.4())
Dm28cS3<-drm(formula = na.omit(data.frame(rownames(Table.10), Table.10[,7]))[,2] ~ as.numeric(as.vector(na.omit(data.frame(rownames(Table.10), Table.10[,7]))[,1])), logDose = exp(1), fct = LL2.4())
Dm28cS4<-drm(formula = na.omit(data.frame(rownames(Table.10), Table.10[,8]))[,2] ~ as.numeric(as.vector(na.omit(data.frame(rownames(Table.10), Table.10[,8]))[,1])), logDose = exp(1), fct = LL2.4())
Dm28cM1<-drm(formula = na.omit(data.frame(rownames(Table.10), Table.10[,9]))[,2] ~ as.numeric(as.vector(na.omit(data.frame(rownames(Table.10), Table.10[,9]))[,1])), logDose = exp(1), fct = LL2.4())
Dm28cM2<-drm(formula = na.omit(data.frame(rownames(Table.10), Table.10[,10]))[,2] ~ as.numeric(as.vector(na.omit(data.frame(rownames(Table.10), Table.10[,10]))[,1])), logDose = exp(1), fct = LL2.4())
Dm28cM3<-drm(formula = na.omit(data.frame(rownames(Table.10), Table.10[,11]))[,2] ~ as.numeric(as.vector(na.omit(data.frame(rownames(Table.10), Table.10[,11]))[,1])), logDose = exp(1), fct = LL2.4())
Dm28cM4<-drm(formula = na.omit(data.frame(rownames(Table.10), Table.10[,12]))[,2] ~ as.numeric(as.vector(na.omit(data.frame(rownames(Table.10), Table.10[,12]))[,1])), logDose = exp(1), fct = LL2.4())
Dm28cB1<-drm(formula = na.omit(data.frame(rownames(Table.10), Table.10[,13]))[,2] ~ as.numeric(as.vector(na.omit(data.frame(rownames(Table.10), Table.10[,13]))[,1])), logDose = exp(1), fct = LL2.4())
Dm28cB2<-drm(formula = na.omit(data.frame(rownames(Table.10), Table.10[,14]))[,2] ~ as.numeric(as.vector(na.omit(data.frame(rownames(Table.10), Table.10[,14]))[,1])), logDose = exp(1), fct = LL2.4())
Dm28cB3<-drm(formula = na.omit(data.frame(rownames(Table.10), Table.10[,15]))[,2] ~ as.numeric(as.vector(na.omit(data.frame(rownames(Table.10), Table.10[,15]))[,1])), logDose = exp(1), fct = LL2.4())
Dm28cB4<-drm(formula = na.omit(data.frame(rownames(Table.10), Table.10[,16]))[,2] ~ as.numeric(as.vector(na.omit(data.frame(rownames(Table.10), Table.10[,16]))[,1])), logDose = exp(1), fct = LL2.4())
ED(Dm28cI1, c(50, 90))
ED(Dm28cI2, c(50, 90))
ED(Dm28cI3, c(50, 90))
ED(Dm28cI4, c(50, 90))
ED(Dm28cS1, c(50, 90))
ED(Dm28cS2, c(50, 90))
ED(Dm28cS3, c(50, 90))
ED(Dm28cS4, c(50, 90))
ED(Dm28cM1, c(50, 90))
ED(Dm28cM2, c(50, 90))
ED(Dm28cM3, c(50, 90))
ED(Dm28cM4, c(50, 90))
ED(Dm28cB1, c(50, 90))
ED(Dm28cB2, c(50, 90))
ED(Dm28cB3, c(50, 90))
ED(Dm28cB4, c(50, 90))
#Note: Final concentration obtainable with 10^e formula or using antilog function at start.
LD50.T10<-data.frame(
Invicta = c(271.0192, 456.352, 1101.539, NA),
Saevissima = c(288.6024, 257.0396, 555.9043, 527.2299),
Miltefosine = c(1091.445, 1459.97, 1285.926, 1294.196),
Benznidazole = c(10110.76, 9078.205, 8609.145, 10471.29)
)
lapply(LD50.T10, sd, na.rm=T)
lapply(LD50.T10, mean, na.rm=T)
#formatting tables to make them fit for plotting
#CL cell toxicity assay
long.Table.11<-melt(Table.11)
long.Table.11$L1<-scale
L2<-c(rep("1",18), rep("2",18), rep("3",18),rep("1",18), rep("2",18),rep("1",18), rep("2",18))
long.Table.11$L2<-L2
new.variable<-rep(c("CL", "Invicta", "Saevissima"), c(54,36,36))
long.Table.11$variable<-new.variable
summary.Table.11<-ddply(long.Table.11,
c("variable","L1"),
summarise,
min = min(value, na.rm=T),
max = max(value, na.rm=t),
mean = mean(value, na.rm=T),
sd = sd(value, na.rm=T),
se = sd/sqrt(length(value)))
#Note: LD50 cannot be estimated as design doesn't allow for drc regression
# WARNING: Apparently coauthors have decided to remove following data from the final manuscript version
# [I am keeping these here for the moment]
Brucei_2_dias<-data.frame(
Rep1 = c(325, 297, 268, 275, 235, 215, 138, 120, 95, 31, 9, 4),
Rep2 = c(350, 332, 281, 264, 238, 237, 178, 152, 83, 19, 12, 6),
Rep3 = c(337, 300, 298, 281, 235, 218, 195, 170, 102, 21, 16, 7),
Sol = as.factor(c("0", "0.204", "0.286", "0.322", "0.352", "0.41",
"0.462", "0.508", "0.588", "0.684", "0.806",
"1.207"))
)
#Making it for for plotting
# WARNING: Apparently coauthors have decided to remove these from the final manuscript version
# [I am keeping this here for the moment]
long.Brucei_2_dias<-melt(Brucei_2_dias)
summary.Brucei_2_dias<-ddply(long.Brucei_2_dias,
c("Sol"),
summarise,
min = min(value, na.rm=T),
max = max(value, na.rm=t),
mean = mean(value, na.rm=T),
sd = sd(value, na.rm=T),
se = sd/sqrt(length(value)))
#brucei 3 dias
#data import from Prism 7
# WARNING: Apparently coauthors have decided to remove these from the final manuscript version
# [I am keeping this here for the moment]
brucei_3<-list(
A=data.frame(
days=as.character(c(1,2,3)),
Rep1=c(5, 69, 325),
Rep2=c(5, 75, 350),
Rep3=c(5, 71, 337)
),
B=data.frame(
days=as.character(c(1,2,3)),
Rep1=c(5, 53, 297),
Rep2=c(5, 48, 332),
Rep3=c(5, 51, 300)
),
C=data.frame(
days=as.character(c(1,2,3)),
Rep1=c(5, 41, 268),
Rep2=c(5, 45, 281),
Rep3=c(5, 42, 298)
),
D=data.frame(
days=as.character(c(1,2,3)),
Rep1=c(5, 33, 275),
Rep2=c(5, 35, 264),
Rep3=c(5, 42, 281)
),
E=data.frame(
days=as.character(c(1,2,3)),
Rep1=c(5, 35, 235),
Rep2=c(5, 42, 238),
Rep3=c(5, 39, 235)
),
F=data.frame(
days=as.character(c(1,2,3)),
Rep1=c(5, 27, 215),
Rep2=c(5, 41, 237),
Rep3=c(5, 40, 218)
),
G=data.frame(
days=as.character(c(1,2,3)),
Rep1=c(5, 19, 138),
Rep2=c(5, 31, 178),
Rep3=c(5, 20, 195)
),
H=data.frame(
days=as.character(c(1,2,3)),
Rep1=c(5, 20 , 120),
Rep2=c(5, 26 , 152),
Rep3=c(5, 21, 170)
),
I=data.frame(
days=as.character(c(1,2,3)),
Rep1=c(5, 10, 95),
Rep2=c(5, 11, 83),
Rep3=c(5, 17, 102)
),
J=data.frame(
days=as.character(c(1,2,3)),
Rep1=c(5, 4, 31),
Rep2=c(5, 3, 19),
Rep3=c(5, 4, 21)
),
K=data.frame(
days=as.character(c(1,2,3)),
Rep1=c(5, 3, 9),
Rep2=c(5, 2, 12),
Rep3=c(5, 1, 16)
),
L=data.frame(
days=as.character(c(1,2,3)),
Rep1=c(5, 1, 4),
Rep2=c(5, 1, 6),
Rep3=c(5, 1, 7)
),
M=data.frame(
days=as.character(c(1,2,3)),
Rep1=c(5, 0, 0),
Rep2=c(5, 0, 0),
Rep3=c(5, 0, 0)
)
)
#Leishmania donovani -- Data imported from Prism 7
# WARNING: Apparently coauthors have decided to remove these from the final manuscript version
# [I am keeping this here for the moment]
#L donovani LC50
donovani<-data.frame(
Rep1 = c(5450, 4550, 1200, 600, 250, 5, 0),
Rep2 = c(5750, 3850, 1005, 50, 100, 0, 0),
Rep3 = c(6400, 3950, 1400, 30, 50, 0, 0),
Sol = as.factor(c("0", "2.5", "5", "7.5", "10", "12.5", "15"))
)
#making it fit for plotting
long.donovani<-melt(donovani)
summary.donovani<-ddply(long.donovani,
c("Sol"),
summarise,
min = min(value, na.rm=T),
max = max(value, na.rm=t),
mean = mean(value, na.rm=T),
sd = sd(value, na.rm=T),
se = sd/sqrt(length(value)))
#Note: There is a problem in converting strings back to characters because of factors conversion. Workaround was decided as below:
summary.donovani$Sol<-as.numeric(levels(summary.donovani$Sol))[summary.donovani$Sol]
#data Leishmania donovani 5 days
# WARNING: Apparently coauthors have decided to remove these from the final manuscript version
# [I am keeping this here for the moment]
leishmania_5<-list(
A=data.frame(
days=as.character(c(1,2,3,4,5)),
Rep1=c(200,800,3250,1005,5450),
Rep2=c(200,800,2500,3150,5750),
Rep3=c(200,550,2700,4150,6400)
),
B=data.frame(
days=as.character(c(1,2,3,4,5)),
Rep1=c(200,1000,1800,3700,4550),
Rep2=c(200,800,1800,2800,3850),
Rep3=c(200,1005,2100,3450,3950)
),
C=data.frame(
days=as.character(c(1,2,3,4,5)),
Rep1=c(200,650,1700,650,1200),
Rep2=c(200,550,1300,550,1005),
Rep3=c(200,720,900,400,1400)
),
D=data.frame(
days=as.character(c(1,2,3,4,5)),
Rep1=c(200,500,650,500,600),
Rep2=c(200,450,500,450,50),
Rep3=c(200,600,600,500,30)
),
E=data.frame(
days=as.character(c(1,2,3,4,5)),
Rep1=c(200,450,500,150,250),
Rep2=c(200,320,450,100,100),
Rep3=c(200,350,650,250,50)
),
F=data.frame(
days=as.character(c(1,2,3,4,5)),
Rep1=c(200,125,110,350,5),
Rep2=c(200,115,75,15,0),
Rep3=c(200,150,70,10,0)
),
G=data.frame(
days=as.character(c(1,2,3,4,5)),
Rep1=c(200,10,5,0,0),
Rep2=c(200,0,1,0,0),
Rep3=c(200,0,1,0,0)
)
)
#Making it fit for plotting
# WARNING: Apparently coauthors have decided to remove these from the final manuscript version
# [I am keeping this here for the moment]
long.leishmania_5<-melt(leishmania_5)
long.leishmania_5_newp1<-paste(long.leishmania_5$days, long.leishmania_5$L1, sep="-")
L2=long.leishmania_5_newp1
long.leishmania_5_new<-cbind(long.leishmania_5, L2)
#Importing data on Cell cycle Arrestment Analysis as provided by 1st author Rafael
CicloCelular1.NH<-data.frame(
CTL_1k.1n = c(88, 76),
CTL_2k.1n = c(6, 17),
CTL_2k.2n = c(6, 7),
INV_1k.1n = c(82.5, 80.5),
INV_2k.1n = c(3.5, 4.5),
INV_2k.2n = c(14, 15),
SAE_1k.1n = c(80, 80),
SAE_2k.1n = c(5, 5),
SAE_2k.2n = c(15, 15)
)
Ciclo1<-cbind(colsplit(melt(t(colMeans(CicloCelular1.NH)))$Var2, "_", c("trt", "type")),
melt(t(colMeans(CicloCelular1.NH))))
levels(Ciclo1$trt)<-c("SAE", "INV", "CTL")
CicloCelular2.NH<-data.frame(
CTL_1k.1n=c(79, 43),
CTL_2k.1n=c(10, 39),
CTL_2k.2n=c(11, 18),
INV_1k.1n=c(76.5, 70.5),
INV_2k.1n=c(6.5, 9.5),
INV_2k.2n=c(17, 20),
SAE_1k.1n=c(80, 72),
SAE_2k.1n=c(5, 9),
SAE_2k.2n=c(15, 19)
)
Ciclo2<-cbind(colsplit(melt(t(colMeans(CicloCelular2.NH)))$Var2,
"_", c("trt", "type")),
melt(t(colMeans(CicloCelular2.NH))))
levels(Ciclo2$trt)<-c("SAE", "INV", "CTL")
CicloCelular3.NH<-data.frame(
CTL_1k.1n=c(78, 54),
CTL_2k.1n=c(2, 25),
CTL_2k.2n=c(20, 21),
INV_1k.1n=c(87, 77),
INV_2k.1n=c(3, 3),
INV_2k.2n=c(10, 20),
SAE_1k.1n=c(70, 70),
SAE_2k.1n=c(15, 15),
SAE_2k.2n=c(15, 15)
)
Ciclo3<-cbind(colsplit(melt(t(colMeans(CicloCelular3.NH)))$Var2,
"_",
c("trt", "type")),
melt(t(colMeans(CicloCelular3.NH))))
levels(Ciclo3$trt)<-c("SAE", "INV", "CTL")
CicloCelular4.NH<-data.frame(
CTL_1k.1n=c(87, 92),
CTL_2k.1n=c(2, 5),
CTL_2k.2n=c(11, 3),
INV_1k.1n=c(83, 80),
INV_2k.1n=c(7.4, 10),
INV_2k.2n=c(9.6, 10),
SAE_1k.1n=c(86.6, 90),
SAE_2k.1n=c(6.4, 1),
SAE_2k.2n=c(7, 9)
)
#making it fit for plotting
CicloCelular<-list(CicloCelular1.NH,CicloCelular2.NH,CicloCelular3.NH,CicloCelular4.NH)
summary.CicloCelular<-ddply(melt(CicloCelular),
c("variable", "L1"),
summarise,
min = min(value, na.rm=T),
max = max(value, na.rm=t),
mean = mean(value, na.rm=T),
sd = sd(value, na.rm = T),
se = sd/sqrt(length(value)))
long.CicloCelular<-separate(data = melt(CicloCelular),
col = variable,
into = c("treat", "morph"),
sep = "_")
levels(long.CicloCelular$treat)<-c("SAE", "INV", "CTL")
#Importing Data on PoliP assays as provided by 1st author Rafael
#Rafa added extra data from personal notes (internal, dependent replicates)
#Note: Using list instead of data.frame as vectors have different lengths
PoliP.short<-list(
NEG=c(49.8, 49.89, 48.78, 31.05, 12.86, 4.44),
INV=c(20.06, 46.5, 46.23),
SAE=c(20.77, 10.2, 4.05)
)
#readjusted a posteriori
PoliP.long<-list(
NEG=c(0.065, 0.088, 0.101, 0.014, 0.236, 0.176,
0.173, 0.01, 0.167, 0.21, 0.225, 0.288, 0.269, 0.38, 0.217),
INV=c(0.592, 0.413, 0.569, 0.448, 0.414, 0.622, 0.794, 1.027),
SAE=c(1.027, 1.388, 0.88, 1.135, 0.27, 0.24, 1.363)
)
#Yet another version provided by 1st author... previous version considered to have internal replicates, dumped.
PoliP.short.INV<-list(
NEG=c(49.80, 49.89, 48.78),
INV=c(20.06, 46.50, 46.23)
)
PoliP.short.SAE<-list(
NEG=c(31.05, 12.86, 4.44),
SAE=c(20.77, 10.20, 4.05)
)
PoliP.short.INV.long<-melt(PoliP.short.INV, na.rm =TRUE)
PoliP.short.SAE.long<-melt(PoliP.short.SAE, na.rm =TRUE)
PoliP.long.long<-melt(PoliP.long, na.rm =TRUE)
PoliP.short.long<-melt(PoliP.short, na.rm =TRUE)
PoliP.short.long$L1<-factor(PoliP.short.long$L1,
levels = unique(PoliP.short.long$L1))
PoliP.long.long$L1<-factor(PoliP.long.long$L1,
levels = unique(PoliP.long.long$L1))
PoliP.soma<-data.frame((lapply(PoliP.soma,
"length<-", max(lengths(PoliP.soma)))))
summary.PoliP.soma<-ddply(melt(PoliP.soma),
c("variable"),
summarise,
min=min(value, na.rm=T),
max =max(value, na.rm=t),
mean=mean(value, na.rm=T),
sd=sd(value, na.rm=T),
se = sd/sqrt(length(value)))
#Importing data on Fluorescence Data - APOPTAG from Prism 7 as provided by senior author Norton
Apoptag<-list(
BHI=c(682.82, 295.46, 687.74, 401.84),
TX_100=c(759.9, 589, 674.4),
INV=c(2047.88, 825.38, 1792.64, 1155.3),
SAE=c(3241.1, 1550.9, 2396, 792.74)
)
#Making the data suitable for plotting
Apoptag<-data.frame((lapply(Apoptag,
"length<-", max(lengths(Apoptag)))))
summary.Apoptag<-ddply(melt(Apoptag),
c("variable"),
summarise,
min=min(value, na.rm=T),
max =max(value, na.rm=t),
mean=mean(value, na.rm=T),
sd=sd(value, na.rm=T),
se = sd/sqrt(length(value)))
#Importing data on Macrophage Infection from Prism 7 as provided by senior author Norton
Infected_Macrofage<-data.frame(
I_NEG=c(77.6, 86),
I_1.25=c(70, 85.97),
I_2.5=c(63.3, 74),
I_5.0=c(47, 21.5),
I_7.5=c(16.6, 1)
)
Amas_macrofage<-data.frame(
A_NEG=c(12.6, 10.31),
A_1.25=c(13, 10.48),
A_2.5=c(10.07, 9.25),
A_5.0=c(7.46, 3.75),
A_7.5=c(3.69, 1)
)
Macrophage.Infection.Amas<-data.frame(
M_0=c(70, 84.95),
M_1.25=c(77.6, 85.97),
M_2.5=c(63.3, 74),
M_5.0=c(47, 21.46),
M_7.5=c(16.6, 1)
)
# Importing data on Fluorescence from Prism 7 as provided by senior author Norton
Fluorescence<-list(
DMSO=c(100, 98, 97, 99, 97, 100),
Benzo_3uM=c(98, 101, 98, 100, 102, 99, 92, 100),
Benzo_5uM=c(106, 108, 96, 99),
Benzo_10uM=c(105, 107, 96, 98),
Benzo_20uM=c(105, 105, 100, 98, 96, 90, 95, 91),
Benzo_40uM=c(109, 107, 100, 98),
Benzo_80uM=c(110, 109, 101, 100),
Dapi_1.5uM=c(77, 84, 84.7),
Dapi_3uM=c(77, 75.4, 70),
Dapi_8uM=c(55, 50, 49),
Dapi_16uM=c(47, 47.2, 43),
Dapi_32uM=c(38.5, 38, 35),
Milte_6.8uM=c(91, 88, 100),
Milte_14uM=c(89, 90, 100),
Milte_68uM=c(89, 86, 100),
Sae_0.75uM=c(106, 104, 96),
Sae_1.5Mu=c(95, 92, 86),
Sae_3uM=c(85, 84, 78),
Sae_6uM=c(84, 85, 77),
Sae_20uMM=c(92, 93, 85),
Invi_0.15uM=c(103, 102, 94),
Invi_0.3uM=c(90, 88, 81),
Invi_1.5uM=c(89, 85, 80),
Invi_3uM=c(84, 84, 77),
Invi_20uM=c(90, 86, 80)
)
#Making the data fit for plotting
Fluorescence<-data.frame(lapply(Fluorescence, "length<-",
max(lengths(Fluorescence))))
summary.Fluorescence<-ddply(melt(Fluorescence),
c("variable"),
summarise,
min=min(value, na.rm=T),
max =max(value, na.rm=t),
mean=mean(value, na.rm=T),
sd=sd(value, na.rm=T),
se = sd/sqrt(length(value)))
# Importing data from Prism 7
MDC<-list(
BHI=c(100, 100, 100),
PBS=c(138.627, 158, 140),
INV=c(130, 115, 113),
SAE=c(112, 118, 127)
)
#Making the data fit for plotting
MDC<-data.frame((lapply(MDC, "length<-", max(lengths(MDC)))))
summary.MDC<-ddply(melt(MDC),
c("variable"),
summarise,
min=min(value, na.rm=T),
max =max(value, na.rm=t),
mean=mean(value, na.rm=T),
sd=sd(value, na.rm=T),
se = sd/sqrt(length(value)))
#Importing Data on MTT from Prism 7 as provided by senior author Norton
#Technical note: Constructing a table using vectors of different lengths
MTT<-list(
NEG=c(107.43, 130, 100, 100, 100, 100, 100, 100, 100),
POS=c(19, 12, 0, 0, 0, 0),
INV_0.5=c(95, 97, 100, 100, 100, 100, 130),
SAE_0.5=c(99.9, 125.53, 100),
INV_1.0=c(98, 120, 100, 99.8, 100, 100),
SAE_1.0=c(91.12, 103),
INV_2.5=c(70, 80.34, 100, 56.36, 100, 104, 99.8, 100, 100),
SAE_2.5=c(72.85, 100, 100),
INV_5=c(45, 70, 100, 100, 0, 0, 85.25, 78.76, 60.96, 100),
SAE_5=c(52, 70.32, 91.42, 100),
INV_10=c(100, 60.9, 0, 0, 0, 53.87, 0, 61.39 ),
SAE_10=c(100, 100, 0, 100, 30.3, 99.55, 0, 94.15, 0),
INV_20=c(70.39, 0, 0, 0, 0, 0, 0, 0, 0),
SAE_20=c(0, 28.85, 1.92, 0.06, 0, 69.56, 80.49, 46.37, 0)
)
#Making the data fit for plotting
MTT<-data.frame(lapply(MTT,
"length<-", max(lengths(MTT))))
sub("....","",colnames(MTT))
summary.MTT<-ddply(melt(MTT),
c("variable"),
summarise,
min=min(value, na.rm=T),
max =max(value, na.rm=t),
mean=mean(value, na.rm=T),
sd=sd(value, na.rm=T),
se = sd/sqrt(length(value)))
#Importing NEW MTT data from Prism 7 Won't this ordeal ever end
MTT.assay<-data.frame(
C=c(100, 100, 100),
I4.1=c(45, 70, 53.7),
I2.0=c(70, 81, 83.6),
I1.0=c(98, 120, 116),
S3.9=c(61.7, 52, 72.84),
S2.0=c(108, 91.12, 103),
S1.0=c(118, 99.9, 125.33)
)
#making it fit for plotting
long.MTT.assay<-melt(MTT.assay)
#Importing Data on LDH from Prism 7 as provided by senior author Norton
CHO<-list(
CHO_NEG=c(100, 95, 100),
CHO_POS=c(0.9, 5),
CHO_0.5=c(95, 97),
CHO_1.0=c(84, 95),
CHO_2.0=c(72, 84),
CHO_4=c(21, 72),
CHO_8=c(0, 21),
CHO_10=c(0.5, 5)
)
#Making the data fit for plotting
CHO<-data.frame(lapply(CHO, "length<-", max(lengths(CHO))))
summary.CHO<-ddply(melt(CHO),
c("variable"),
summarise,
min=min(value, na.rm=T),
max =max(value, na.rm=t),
mean=mean(value, na.rm=T),
sd=sd(value, na.rm=T),
se = sd/sqrt(length(value)))
#Importing further low-rep data by 1st author Rafa; no idea of what this is about anymore
BMDM<-list(
C_0.5=c(100, 100, 100),
INV_1.0=c(98, 120, 116),
INV_2.0=c(70, 81, 83.60),
INV_4.0=c(45, 70, 53.7),
SAE_4.0=c(61.7, 52, 72.84),
SAE_2.0=c(108, 91.12, 103),
SAE_1.0=c(118, 99.9, 125.33)
)
BMDM<-data.frame(lapply(BMDM, "length<-", max(lengths(BMDM))))
#Making it fit for plotting
summary.BMDM<-ddply(melt(BMDM),
c("variable"),
summarise,
min=min(value, na.rm=T),
max =max(value, na.rm=t),
mean=mean(value, na.rm=T),
sd=sd(value, na.rm=T),
se = sd/sqrt(length(value)))
## Importing yet further data on amastigotes as provided by senior author Norton, right after R1
Inf_Amas_alk<-data.frame(
C=c(8.82, 8.76, 8.78),
INV_1.2=c(7.81, 7.30, 2.29),
INV_2.4=c(3.50, 0.80, 0.17),
SAE_0.6=c(8.22, 8.40, 4.13),
SAE_1.2=c(0.59, 0.10, 0.82)
)
BENZ_amastigota<-
data.frame(
BENZ_C=c(6.225, 6.26, 5.15),
BENZ_2=c(6.06, 5.15, 5.1),
BENZ_10=c(2.465, 1.645, 5.19),
BENZ_20=c(0.985,0.9, 2.5),
BENZ_100=c(0,0,0)
)
Inf_Amas_Ben<-data.frame(
C=c(6.22, 6.26),
BEN_2=c(6.06, 5.15),
BEN_10=c(2.46, 1.65),
BEN_20=c(0.98, 0.9),
BEN_100=c(0.00, 0.00)
)
Amast_values<-data.frame(
INV=c(3.551, 2.9, NA),
Sae=c(3.00, 2.97, NA),
Ben=c(5.41, 3.93, 6.10)
)
Invicta_Amast<-
data.frame(
conc=c(0, 2, 4),
Rep1=c(8.82, 7.81, 3.50),
Rep2=c(8.76, 7.30, 0.80),
Rep3=c(8.78, 2.29, 0.17)
)
Saevissima_Amast<-
data.frame(
conc=c(0, 2, 4),
Rep1=c(8.82, 8.22, 0.59),
Rep2=c(8.76, 8.4, 0.1),
Rep3=c(8.78, 4.13, 0.82)
)
#format for plotting both together
Amast2_INV<-t(data.frame(
"0" =c(8.82, 8.76, 8.78),
"2" =c(7.81, 7.3, 2.29),
"4" =c(3.5, 0.8, 0.17), check.names=FALSE
))
Amast2_SAE<-t(data.frame(
"0" =c(8.82, 8.76, 8.78),
"2" =c(8.22, 8.4, 4.13),
"4" =c(0.59, 0.1, 0.82), check.names=FALSE
))
AMAST<-list(Amast2_INV, Amast2_SAE)
names(AMAST)<-c("INV", "SAE")
#IC50s Amastigotas
#IC50 for these compounds
ED(drm(Invicta_Amast[,c(2,1)], logDose = F, fct = LL.3()),50)
ED(drm(Invicta_Amast[,c(3,1)], logDose = F, fct = LL.3()),50)
ED(drm(Invicta_Amast[,c(4,1)], logDose = F, fct = LL.3()),50)
#LD50_Inv<-c(3.5556, 2.6609, 1.5569)
ED(drm(Saevissima_Amast[,c(2,1)], logDose = F, fct = LL.3()),50)
ED(drm(Saevissima_Amast[,c(3,1)], logDose = F, fct = LL.3()),50)
ED(drm(Saevissima_Amast[,c(4,1)], logDose = F, fct = LL.3()),50)
#LD50_Sae<-c(2.8251, 2.6645, 1.9251)
#Positive control formula
data.frame(
conc = c(0, 2, 10, 20, 100),
Rep1 = c(6.225, 6.06, 2.465, 0.985, 0),
Rep2 = c(6.26, 5.15, 1.645, 0.9, 0),
Rep3 = c(13.4, 15.1, 5.13, 2.5, 0)
)
#to check on regression details, merely plot the regression below
ED(drm(BEN_amast[,c(2,1)], logDose = F, fct = LL.3()),50)
ED(drm(BEN_amast[,c(3,1)], logDose = F, fct = LL.3()),50)
ED(drm(BEN_amast[,c(4,1)], logDose = F, fct = LL.3()),50)
#LD50_Ben<-c(8.22825, 5.32213, 8.3606)
Results for table obtainable from summary tabulation as below
#mean+/-sd
#Ben= 7.303660+/-1.7173308
#Sae=2.471567+/-0.4800182
#Inv=2.591133+/-1.0011748
long.AMAST<-melt(AMAST)
summary.AMAST<-ddply(long.AMAST, c("Var1","L1"), summarise, min=min(value, na.rm=T),
max =max(value, na.rm=t),mean=mean(value, na.rm=T),
sd=sd(value, na.rm=T), se = sd/sqrt(length(value)))
#Outputted final images available as RData object at FigShare: https://figshare.com/articles/Raw_data_and_analysis_of_venom_solenopsins_paper/11796462
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