How to plot a multiple line graph with Mean and Std Error for following dataset?

Question

[WUSCHEL]

I have a data set of >100 different samples. Samples are from different genotypes (e.g. X, Y, Z) and 4 different time points (T0,1,2,3) with 3 biological replicates (R1,2,3). I'm measuring values for 50 different genes (in raws)

structure(list(Gene = structure(1:2, .Label = c("A", "B"), class = "factor"), 
X_T0_R1 = c(1.46559502, 0.220140568), X_T0_R2 = c(1.087642983, 
0.237500819), X_T0_R3 = c(1.424945196, 0.21066267), X_T1_R1 = c(1.289943948, 
0.207778662), X_T1_R2 = c(1.376535013, 0.488774258), X_T1_R3 = c(1.833390311, 
0.182798731), X_T2_R1 = c(1.450753714, 0.247576125), X_T2_R2 = c(1.3094609, 
0.390028842), X_T2_R3 = c(0.5953716, 1.007079177), X_T3_R1 = c(0.7906009, 
0.730242116), X_T3_R2 = c(1.215333041, 1.012914813), X_T3_R3 = c(1.069312467, 
0.780421013), Y_T0_R1 = c(0.053317766, 3.316414959), Y_T0_R2 = c(0.506623748, 
3.599442788), Y_T0_R3 = c(0.713670106, 2.516735845), Y_T1_R1 = c(0.740998252, 
1.444496448), Y_T1_R2 = c(0.648231834, 0.097957459), Y_T1_R3 = c(0.780499252, 
0.187840968), Y_T2_R1 = c(0.35344654, 1.190274584), Y_T2_R2 = c(0.220223951, 
1.367784148), Y_T2_R3 = c(0.432856978, 1.403057729), Y_T3_R1 = c(0.234963735, 
1.232129062), Y_T3_R2 = c(0.353770497, 0.885122768), Y_T3_R3 = c(0.396091395, 
1.333921747), Z_T0_R1 = c(0.398000559, 1.286528398), Z_T0_R2 = c(0.384759325, 
1.122251177), Z_T0_R3 = c(1.582230097, 0.697419716), Z_T1_R1 = c(1.136843842, 
0.804552001), Z_T1_R2 = c(1.275683837, 1.227821594), Z_T1_R3 = c(0.963349308, 
0.968589683), Z_T2_R1 = c(3.765036263, 0.477443352), Z_T2_R2 = c(1.901023385, 
0.832736132), Z_T2_R3 = c(1.407713024, 0.911920317), Z_T3_R1 = c(0.988333629, 
1.095130142), Z_T3_R2 = c(0.618606729, 0.497458337), Z_T3_R3 = c(0.429823986, 
    0.471389536)), .Names = c("Gene", "X_T0_R1", "X_T0_R2", "X_T0_R3", 
"X_T1_R1", "X_T1_R2", "X_T1_R3", "X_T2_R1", "X_T2_R2", "X_T2_R3", 
"X_T3_R1", "X_T3_R2", "X_T3_R3", "Y_T0_R1", "Y_T0_R2", "Y_T0_R3", 
"Y_T1_R1", "Y_T1_R2", "Y_T1_R3", "Y_T2_R1", "Y_T2_R2", "Y_T2_R3", 
"Y_T3_R1", "Y_T3_R2", "Y_T3_R3", "Z_T0_R1", "Z_T0_R2", "Z_T0_R3", 
"Z_T1_R1", "Z_T1_R2", "Z_T1_R3", "Z_T2_R1", "Z_T2_R2", "Z_T2_R3", 
"Z_T3_R1", "Z_T3_R2", "Z_T3_R3"), class = "data.frame", row.names = c(NA, 
-2L))

For each gene (i.e. for each column), I want to plot a graph with an average of replicates of each genotype + SE Expected line graph pattern plot with SE

i.e. for an e.g. For Gene A, at a particular time point (0/1/3/5) I want to draw a graph with all the genotypes (X, Y, Z); should be the3 lines in the plot looking like above plots.

How is this possible using R? How can I include Std Error? Using loops can I generate 50 graphs (for each raw separate graph)?

[cpad0112]

Values you furnished above large deviations. See if following plot works. Data is taken from OP:

df=read.csv("df1.txt", sep="\t",stringsAsFactors = F)
library(tidyr)
df1=gather(df,"TP","Values",-Gene)
library(stringr)
df2=cbind(df1,str_split_fixed(df1$TP,"_",3))
colnames(df2)[4:6]=c("genotype","time","replicate")
library(Rmisc)
df4=summarySE(df2, measurevar="Values", groupvars=c("time","Gene","genotype"))

  ggplot(df4, aes(time, Values, group = genotype, color = genotype)) +
  geom_line() +
  geom_point() +
  facet_wrap( ~ Gene) +
  labs(title = "Gene expression over 16 hr", x = "Time (hr)", y = "Measurement") +
  theme_linedraw() +
  theme(
    plot.title = element_text(hjust = 0.5, size = 20),
    strip.text = element_text(size = 20),
    axis.title.y = element_text(size = 20),
    axis.title.x = element_text(size = 20),
    axis.text.x = element_text(size = 14),
    axis.text.y = element_text(size = 14)
  ) +
  geom_ribbon(aes(ymax = Values + sd, ymin = Values - sd),
              alpha = 0.5,
              fill = "grey70",
              colour=NA
              )

[WUSCHEL]

Thank you cpad0112

I'm working with your codes. Im having a error after df4=summarySE(df2, measurevar="Values", groupvars=c("time","Gene","genotype"))

Error in summarySE(df2, measurevar = "Values", groupvars = c("time", "Gene", : could not find function "summarySE"

Could you please help me with this

[cpad0112]

sorry..forgot to add following line: library(Rmisc). SummarySE function is from Rmisc library. Load Rmisc library. Updated the code.

[WUSCHEL]

Thanks, cpad0112 , Sorry to bother I get another error message

Error in combine_vars(data, params$plot_env, vars, drop = params$drop) : At least one layer must contain all variables used for facetting

Would appreciate your help :)

[cpad0112]

well, if you could post script here (ggplot) it would help. Check if you have recent versions of ggplot.

[WUSCHEL]

I see, I do not have ggplot. Is this different form ggplot2.

Where can I get this package? Googling doesn't help :(

df <- read.csv("SI_AVG_Line.csv")

library(tidyr) df1 <- gather(df,"Transitions","Values",-Targets) library(stringr) df2 <- cbind(df1,str_split_fixed(df1$Transitions,"_",3)) colnames(df2)[4:6]=c("genotype","time","replicate") library(Rmisc) df4 <- summarySE(df2, measurevar="Values", groupvars=c("time","Targets","genotype")) ggplot(df4, aes(time, Values, group = genotype, color = genotype)) + geom_line() + geom_point() + facet_wrap( ~ Gene) + labs(title = "Gene Expression vs time", x = "Time (d)", y = "Area_counts") + theme_linedraw() + theme( plot.title = element_text(hjust = 0.5, size = 20), strip.text = element_text(size = 20), axis.title.y = element_text(size = 20), axis.title.x = element_text(size = 20), axis.text.x = element_text(size = 14), axis.text.y = element_text(size = 14) ) + geom_ribbon(aes(ymax = Values + sd, ymin = Values - sd), alpha = 0.5, fill = "grey70", colour=NA )

@ cpad0112 Given below is the working .csv file where error comes , if in a case this is the problem!

[WUSCHEL]

structure(list(Targets = c("A", "B", "C", "nor"), X_S1_0d_1 = c(1.940487232,

1.079594087, 1.459871602, 1), X_S1_0d_2 = c(1.940630815, 0.790986517, 0.836386383, 1), X_S1_0d_3 = c(2.05030161, 0.721115111, 0.802144144, 1), X_S1_1d_1 = c(0.927368618, 1.186737277, 0.765095737, 1), X_S1_1d_2 = c(1.159347963, 1.427045976, 1.196499915, 1), X_S1_1d_3 = c(1.009271935, 1.049367585, 0.748728559, 1), X_S1_3d_1 = c(0.794781558, 1.072762904, 1.288591327, 1), X_S1_3d_2 = c(0.698642658, 0.971534921, 0.923846091, 1), X_S1_3d_3 = c(0.938922191, 0.80228642, 1.433899521, 1), X_S1_5d_1 = c(0.768844884, 1.458863535, 0.880239008, 1), X_S1_5d_2 = c(0.586314866, 1.027767798, 0.831469797, 1), X_S1_5d_3 = c(0.604124099, 1.502330028, 1.101895903, 1), mut2_S1_0d_1 = c(2.085432338, 0.861943427, 0.509210189, 1), mut2_S1_0d_2 = c(1.774970153, 1.074569974, 3.128664718, 1), mut2_S1_0d_3 = c(2.003870102, 0.753483213, 1.047020362, 1), mut2_S1_1d_1 = c(1.168381858, 1.15001272, 0.580462548, 1), mut2_S1_1d_2 = c(1.33284456, 0.450460567, 0.959430252, 1), mut2_S1_1d_3 = c(1.106332747, 0.466636391, 0.660254618, 1), mut2_S1_3d_1 = c(0.859543853, 1.188445442, 1.044546139, 1), mut2_S1_3d_2 = c(1.022929555, 1.259366417, 1.776709656, 1), mut2_S1_3d_3 = c(0.917527143, 2.137370791, 0.669765284, 1), mut2_S1_5d_1 = c(0.642810843, 0.496709803, 0.801885112, 1), mut2_S1_5d_2 = c(0.879777521, 1.170165217, 1.793443182, 1), mut2_S1_5d_3 = c(0.816650769, 0.864352103, 0.768312731, 1), mut5_S1_0d_1 = c(1.936291138, 0.721197246, 1.885982652, 1), mut5_S1_0d_2 = c(2.136240851, 0.925363277, 0.282462799, 1), mut5_S1_0d_3 = c(1.986120429, 0.677085837, 0.124936834, 1), mut5_S1_1d_1 = c(1.346339786, 0.989266319, 1.396700558, 1), mut5_S1_1d_2 = c(1.489199506, 1.269083963, 1.48921516, 1), mut5_S1_1d_3 = c(1.584229502, 0.88246637, 2.25267634, 1), mut5_S1_3d_1 = c(0.755948531, 1.451613602, 0.898362008, 1), mut5_S1_3d_2 = c(0.824308907, 0.5962476, 0.523055204, 1), mut5_S1_3d_3 = c(0.753359409, 0.753222103, 0.948441646, 1), mut5_S1_5d_1 = c(0.788525215, 1.85338769, 0.951693842, 1), mut5_S1_5d_2 = c(1.010417043, 1.983625345, 1.086768544, 1), mut5_S1_5d_3 = c(0.630454563, 1.439599004, 1.416591771, 1), mut7_S1_0d_1 = c(1.672072567, 0.611243763, 0.705364938, 1), mut7_S1_0d_2 = c(1.738837658, 0.503828595, 0.499147343, 1), mut7_S1_0d_3 = c(2.149037252, 1.192787265, 1.226895377, 1), mut7_S1_1d_1 = c(1.421761015, 1.084490092, 0.497815065, 1), mut7_S1_1d_2 = c(1.068782794, 0.584950798, 0.38078948, 1), mut7_S1_1d_3 = c(1.229045044, 0.822348277, 0.449995849, 1), mut7_S1_3d_1 = c(0.890386073, 0.802513638, 0.757190729, 1), mut7_S1_3d_2 = c(1.022619118, 0.806565748, 0.645204575, 1), mut7_S1_3d_3 = c(0.80348663, 0.753993198, 0.593817113, 1), mut7_S1_5d_1 = c(0.780575903, 0.724989068, 0.92248483, 1), mut7_S1_5d_2 = c(0.743592574, 1.279872561, 1.201887432, 1), mut7_S1_5d_3 = c(0.522476113, 0.751493063, 0.899865367, 1), mut9_S1_0d_1 = c(1.247510942, 0.762934403, 2.009134613, 1), mut9_S1_0d_2 = c(1.159843529, 0.684622155, 0.499925077, 1), mut9_S1_0d_3 = c(1.247510942, 0.762934403, 2.205521099, 1), mut9_S1_1d_1 = c(1.139288266, 0.530593446, 0.767442607, 1), mut9_S1_1d_2 = c(1.257958733, 0.780701299, 0.77153391, 1), mut9_S1_1d_3 = c(1.230762109, 0.536139676, 0.742313942, 1), mut9_S1_3d_1 = c(0.809093089, 0.59528538, 0.804481151, 1), mut9_S1_3d_2 = c(0.853017549, 0.826757331, 1.141960538, 1), mut9_S1_3d_3 = c(0.813029821, 0.748971384, 1.964723247, 1), mut9_S1_5d_1 = c(0.797277294, 1.327830526, 0.943500196, 1), mut9_S1_5d_2 = c(0.669946954, 1.011869145, 0.979867227, 1), mut9_S1_5d_3 = c(0.525670301, 1.067407334, 0.76001394, 1)), .Names = c("Targets", "X_S1_0d_1", "X_S1_0d_2", "X_S1_0d_3", "X_S1_1d_1", "X_S1_1d_2", "X_S1_1d_3", "X_S1_3d_1", "X_S1_3d_2", "X_S1_3d_3", "X_S1_5d_1", "X_S1_5d_2", "X_S1_5d_3", "mut2_S1_0d_1", "mut2_S1_0d_2", "mut2_S1_0d_3", "mut2_S1_1d_1", "mut2_S1_1d_2", "mut2_S1_1d_3", "mut2_S1_3d_1", "mut2_S1_3d_2", "mut2_S1_3d_3", "mut2_S1_5d_1", "mut2_S1_5d_2", "mut2_S1_5d_3", "mut5_S1_0d_1", "mut5_S1_0d_2", "mut5_S1_0d_3", "mut5_S1_1d_1", "mut5_S1_1d_2", "mut5_S1_1d_3", "mut5_S1_3d_1", "mut5_S1_3d_2", "mut5_S1_3d_3", "mut5_S1_5d_1", "mut5_S1_5d_2", "mut5_S1_5d_3", "mut7_S1_0d_1", "mut7_S1_0d_2", "mut7_S1_0d_3", "mut7_S1_1d_1", "mut7_S1_1d_2", "mut7_S1_1d_3", "mut7_S1_3d_1", "mut7_S1_3d_2", "mut7_S1_3d_3", "mut7_S1_5d_1", "mut7_S1_5d_2", "mut7_S1_5d_3", "mut9_S1_0d_1", "mut9_S1_0d_2", "mut9_S1_0d_3", "mut9_S1_1d_1", "mut9_S1_1d_2", "mut9_S1_1d_3", "mut9_S1_3d_1", "mut9_S1_3d_2", "mut9_S1_3d_3", "mut9_S1_5d_1", "mut9_S1_5d_2", "mut9_S1_5d_3"), class = "data.frame", row.names = c(NA, -4L))

[cpad0112]

Issue with the code is here: df2 <- cbind(df1,str_split_fixed(df1$Transitions,"_",3)). When you split the string (example: mut9_S1_3d_2) with _, you would get 4 strings and you are selecting 3. I meant ggplot2 not ggplot.

code:

library(tidyr)
df1=gather(test,"TP","Values",-Targets)

library(stringr)
df2=cbind(df1,str_split_fixed(df1$TP,"_",4))
colnames(df2)[4:7]=c("a","b","time","replicate")
df2$mut = paste(df2$a, df2$b, sep="_")

library(dplyr)
df3=select(df2, -c(a,b))

library(Rmisc)
names(df3)
df4=summarySE(df3, measurevar="Values", groupvars=c("time","mut","Targets"))
View(df4)
View(df)

library(ggplot2)
ggplot(df4, aes(time, Values, group = mut, color = mut)) +
  geom_line() +
  geom_point() +
  facet_wrap( ~ Targets) +
  labs(title = "Gene expression ", x = "Time (hr)", y = "Measurement") +
  theme_linedraw() +
  theme(
    plot.title = element_text(hjust = 0.5, size = 20),
    strip.text = element_text(size = 20),
    axis.title.y = element_text(size = 20),
    axis.title.x = element_text(size = 20),
    axis.text.x = element_text(size = 14),
    axis.text.y = element_text(size = 14)
  ) +
  geom_ribbon(aes(ymax = Values + sd, ymin = Values - sd),
              alpha = 0.3,
              fill = "grey70",
              colour=NA
  )

with error bar only (mean +/- SD):

For error bars, replace following code in the code with one that is highlighted in red below:

geom_ribbon(aes(ymax = Values + sd, ymin = Values - sd),
              alpha = 0.3,
              fill = "grey70",
              colour=NA
  )

with geom_pointrange(aes(ymax=Values+sd, ymin=Values-sd))

[WUSCHEL]

Thank you cpad0112 Thank you for helping me this much even though you do not know me personally. I will never forget your help. When the right time comes I promise I'll acknowledge all the people help me without knowing me. I wish if I know who you are (being anonymous is the best for me like dummy only). I appreciate your time and kindness!!! Wishing you the best!!!

[cpad0112]

No problem and thank you. Which ever post helped you in resolving issue, mark it as answer. Good luck with your research.

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