I would like to have a multidimensional scaling plot according to the following table (this is just a shorter form of the whole table).
I have been trying to do it in R (am quite new here...) but now. I am not even sure about that this type of data is good for multidimensional scaling. The whole table should mirror a semantic (linguistic) map (Thats why I thought that MDS should be good) and the rows mean that informants saw some pictures and gave different expressions (columns) for the pictures, so they described them differently.
The numbers in the columns are no judgments in the sense that they are on a scale from 1 to 10 or something like that but they show how many people used the expression for pic1, pic2, and so forth.
Could anyone help me to explain that MDS is actually the appropriate model I am trying to use? (Sorry, I am just too much confused after reading a lot in the last days about different methods...)
If so, here is the coding I used (just to be sure).
Thanks a lot for any advice!
daten <- structure(list(photos = c("p1", "p5", "p8", "p13", "p19", "p23", "p29", "p34", "p36", "p40", "p59", "p2", "p14"), expression1 = c(18, 8, 11, 15, 14, 16, 10, 12, 15, 18, 18, 0, 0), expression2 = c(0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0), expression3 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1), expression4 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 15, 17), expression5 = c(0, 3, 5, 0, 0, 0, 1, 5, 1, 0, 0, 0, 0), expression6 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), expression7 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), expression8 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)), row.names = c(NA, -13L), class = c("tbl_df", "tbl", "data.frame"))
library("tibble")
has_rownames(daten)
cr<-column_to_rownames(daten, var="photo")
has_rownames(cr)
matr_cr <- as.matrix(cr[,-1])
matr_cr
d<-dist(matr_cr)
fit <- cmdscale(d, eig = TRUE, k = 2)
x <- fit$points[, 1]
y <- fit$points[, 2]
plot(x, y, xlab="Coordinate 1", ylab="Coordinate 2",
main="Multidimensional Scaling", type="n")
text(x, y, labels = row.names(matr_cr), cex=.6, col="red")
cr
Plotting multidimensional data is difficult and depending on the type of data and analysis is what to do. First of all, if you have several variables, it may be useful to cluster your data, one possible method is k-means that you can find it in the package "ClusterR". Another possible thing to do is to transform your variable by rotating the axis in order to lower the dimension with a Principal Component Analysis (PCA), you can find more about PCA in R in http://www.sthda.com/english/articles/31-principal-component-methods-in-r-practical-guide/
If you opp to plot your data as it is without a previous analysis, you may use ggplot2 package to make more useful and elegant plots. And to plot your different data attributes you can try changing size, color, shape, etc scales representing different dimensions. The problem with this option is that you can not plot several dimensions.
If I understand you well, you got pictures and people (informants) that make a review of the pictures. And the critics are separated in different levels (dimensions). If it is like that, you got as dimensions pictures, reviewers, and each level of the reviews, that make 2+N variables. Note that you can easily plot up to 5 dimensions in this kind of data, by setting x-axis and y-axis you got 2 dimensions, then you can use size scale for another dimension, color scale for another dimension, and the depending on your data and your preference you can use text or shape scale for the fifth dimension. I do not see in the table you provide the informants (reviewers) dimension. Further below you will found two examples of these plot using ggplot2, note that for shape scale a discrete variable must be used. In order to get beautiful plots and with meaning, you will have to try wich type of scale is better for each of your variables and will strongly depend on your data. Lastly, if you have several dimensions normally you should try first to assess if your data is clusterized or do a PCA.
library(ggplot2)
daten <- structure(list(photos = c("p1", "p5", "p8", "p13", "p19", "p23", "p29", "p34", "p36", "p40", "p59", "p2", "p14"), expression1 = c(18, 8, 11, 15, 14, 16, 10, 12, 15, 18, 18, 0, 0), expression2 = c(0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0), expression3 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1), expression4 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 15, 17), expression5 = c(0, 3, 5, 0, 0, 0, 1, 5, 1, 0, 0, 0, 0), expression6 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), expression7 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), expression8 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)), row.names = c(NA, -13L), class = c("tbl_df", "tbl", "data.frame"))
# with shape scale
ggplot(data = daten,aes(x=photos, y=expression1, col=expression2, size=expression3, shape=as.factor(expression4))) +
geom_point()
# with text scale
ggplot(data = daten,aes(x=expression4, y=expression1, col=expression2, size=expression3, label=photos)) +
geom_text()
I have plotted point graph using vegan package but I want to circle the similarly treated species. As shown in the figure, 3 colors for 3 treatments. I want to circle them too.
Here is my code.
library(vegan)
library(MASS)
library(readxl)
bray1 <- read_excel("bray1.xlsx")
cols <- c("red", "blue","blue", "green","green","red","blue","green","green","red","red","blue")
row.names(bray1) <- c("SI1", "SII0", "SI0", "SII2", "SI2", "SII1", "SIII0", "SIV2", "SIII2", "SIV1", "SIII1", "SIV0")
bcdist <- vegdist(bray1, "bray")
bcmds <- isoMDS(bcdist, k = 2)
plot(bcmds$points, type = "n", xlab = "", ylab = "")
text(bcmds$points, dimnames(bray1)[[1]],col = cols,size=10)
[My data
bray1<-structure(list(`Andropogon virginicus` = c(0, 0, 0, 0, 2.7, 31.5333333333333, 0, 0, 0, 0, 0, 0), `Oenothera parviflora` = c(61.6,30.3333333333333, 7.53333333333333, 0, 11.7333333333333, 0, 0, 0,75.4, 0, 0, 0), `Lespedeza cuneata` = c(0, 0, 0, 0, 0, 46.7333333333333, 0, 0, 3, 0, 0, 0), `Lespedeza pilosa` = c(0, 1.93333333333333, 0, 0, 1.73333333333333, 0, 0, 0, 0, 1.7, 0, 0), `Chamaesyce maculata` = c(0, 0, 0,4.733333333, 0, 0, 0, 0, 0, 0, 0, 0), `Chamaesyce nutans` = c(0,0, 0, 0, 0,0, 0.166666666666667, 0, 0, 0, 0, 0), `Bidens frondosa` = c(0, 0, 0,1.76666666666667, 1.03333333333333, 3.23333333333333, 0, 0, 0, 0, 0, 0), `Erigeron annuus` = c(0, 0, 0, 0, 0.4, 0, 0, 0, 0, 0, 0, 0), `Erigeron canadensis` = c(0, 0, 0, 0, 0, 4.33333333333333, 0, 0, 9.1, 2.066666667, 0,0), `Equisetum arvense` = c(46, 62.7333333333333, 0, 1.66666666666667, 0, 0.533333333333333, 0, 0, 0, 0, 0, 0), `Erigeron sumatrensis` = c(0, 0, 0, 0, 0, 16.4333333333333, 0, 4, 0, 6.633333333, 0, 0), `Hypochaeris radicata` = c(0, 3.76666666666667, 116.6, 0, 5.033333333, 9.76666666666667, 29, 0, 23.1666666666667, 82.16666667, 0, 0), `Lactuca indica` = c(10.26666667, 0, 1.566666667, 120.1333333, 44.36666667, 42.0333333333333, 0, 14.2333333333333, 0, 0, 14.36666667, 22.2), `Solidago altissima` = c(0, 1.06666666666667, 33.93333333, 0, 0, 0, 0, 0, 0, 6.6, 0, 0), `Sonchus asper` = c(0, 35.9, 0, 0, 0, 7.46666666666667,
29.6666666666667, 4.96666666666667, 0, 0, 0.23, 2.933333333 )), .Names = c("Andropogon virginicus", "Oenothera parviflora", "Lespedeza cuneata", "Lespedeza pilosa", "Chamaesyce maculata", "Chamaesyce nutans", "Bidens frondosa", "Erigeron annuus", "Erigeron canadensis", "Equisetum arvense", "Erigeron sumatrensis", "Hypochaeris radicata", "Lactuca indica", "Solidago altissima", "Sonchus asper"), row.names = c(NA, -12L), class = c("tbl_df", "tbl", "data.frame"))
Here are a couple of alternatives based on the dataEllipse function in the car package. I have made a few minor alterations to your base graph. I found it hard to read the pure "green" color text, so I switched it to "darkgreen". I changed the plotting limits so that the full ellipses would be in the picture. Also, your text statement included an argument size. text does not have an argument size so I replaced it with cex to set the font size.
library(car)
Group = c(1,2,2,3,3,1,2,3,3,1,1,2)
cols <- c("red", "blue","blue", "darkgreen","darkgreen","red","blue",
"darkgreen","darkgreen","red","red","blue")
In the first version, I did what I think you asked for, ellipses marking the treatment groups.
plot(bcmds$points, type = "n", xlab = "", ylab = "",
xlim=c(-0.8,0.8), ylim=c(-0.8,0.8), asp=1)
text(bcmds$points, dimnames(bray1)[[1]],col = cols, cex=0.8)
dataEllipse(bcmds$points[,1], bcmds$points[,2], factor(Group),
plot.points=F, add=T, col=c("red", "blue", "green"),
levels=rep(0.6, 3), center.pch=0, group.labels="", lwd=1)
In the second version, instead of using the outline of the ellipse, I use a transparent fill color to show the ellipses.
plot(bcmds$points, type = "n", xlab = "", ylab = "",
xlim=c(-0.8,0.8), ylim=c(-0.8,0.8), asp=1)
text(bcmds$points, dimnames(bray1)[[1]],col = cols, cex=0.8)
dataEllipse(bcmds$points[,1], bcmds$points[,2], factor(Group),
plot.points=F, add=T, col=c("red", "blue", "green"),
levels=rep(0.6, 3), center.pch=0, group.labels="",
lty=0, fill=TRUE, fill.alpha=0.04)
The majority of information I can find on side-by-side stacked barplots deals with instances in where some variable (number of side-by-side bars) are repeated for each variable along the x-axis - see: 1, 2, 3, 4, 5, 6. In these cases they use ggplot with besides=TRUE.
I have a more complex example which I believe will require faceting like these two examples: 7 & 8.
Quick background (for those interested in the why?):
I'm trying to compare the efficiency of a proteomics protocol that enriches for chromatin by comparing to the proportion of nuclear proteins found in the core/whole proteome experiments for 4 cell lines. to do this I used The Human Protein Atlas to annotate proteins by their subcellular location and compare nuclear proteins from chromatin-enrichment to whole-enrichment. However, the chromatin-enrichment protocol was 1D-shotgun while the whole proteome data was 2D-shotgun with 50 fractions. In layman terms this means the whole/core proteome data is a more expensive experiment done at higher coverage. Therefore, it wouldn't make sense to look at absolute proportion though because the overall amount of found proteins would be higher in the whole proteome pull-downs (see figure: absolute protein comparison sketch). To circumvent this issue I divided by the total number of proteins found in each pull-down to get relative proportion of proteins from each subcellular location.
Using these relative proportions I've produced a stacked barplot of the following data in my gist with the following code:
df1 <- read.csv("data.csv") # Load data.frame of the data
df2 <- melt(df1, # Reshape the data from
id.vars = "subcellular_location", # wide format into long format
variable.name = "cell_line", # (i.e. tidy data)
value.name = "relative_proportion")
For some reason this didn't change the variable name or value name (headers) - they are called "variable" and "value" still? So I had to rename column headers via the following.
names(df2) <- c("subcellular_location", "cell_line", "relative_proportion")
As there are many subcellular locations I needed to custom add colors, furthermore I grouped them by similar locations (e.g. nuclear in blue).
p <- ggplot() +
geom_bar(aes(x = cell_line, y = percentage, fill = subcellular_location),
data = df2, stat="identity")
p +
coord_flip() +
scale_fill_manual(values = c("#bd5db0","#9ae17c", "#be0024", "#7388ff", "#c456b7",
"#8ed470", "#7ec361", "#7d7304", "#f87a00", "#d543c7",
"#bead47", "#d148c3", "#da8836", "#e28504", "#d93eca",
"#c720b9", "#bc07ae", "#a40098", "#9a008e", "#e8d448",
"#104ed7", "#2c4ecc", "#00428c", "#393c6d", "#173b8f",
"#3f4c96", "#9ba2f5", "#727bcc", "#e59c5f", "#790000",
"#045d00", "#f9ad6f"))
See image here: stacked barplot
The core proteome pull-downs are highlighted in yellow. Ideally what I would like to do is facet this barplot into 4 sections - one for each cell line. I followed the instructions from reference 7 for faceting but am getting an error.
First I split my dataframe into 4 separate tidy dataframes (e.g. below):
K562 <- read.csv("K562-relative.csv")
K562 <- melt(K562, id.vars = "subcellular_location") # Reshape the data into tidy form
names(K562) <- c("subcellular_location", "cell_line", "relative_proportion")
etc.
Than I created a vector for cell line:
cell <- sample(c("HAP1","K562","A673","MDS"))
When I try the following code I get an error:
ref_by_cell <- data.frame(HAP1 = HAP1, K562 = K562, A673 = A673, MDS = MDS, cell = cell)
Error in data.frame(HAP1 = HAP1, K562 = K562, A673 = A673, MDS = MDS,
arguments imply differing number of rows: 576, 544, 64, 4
I would appreciate any help with faceting or alternative ideas for displaying this information.
Thank you!
I'm not entirely sure what you want, but if you want to facet by the first part of each cell_line value...
# add faceting variable to df2
df2 <- df2 %>%
mutate(cell = stringi::stri_extract_first_regex(cell_line, "^[^\\.|_]+"))
# facet by cell, specifying free scales / space on the y-axis
ggplot(data = df2,
aes(x = cell_line, y = relative_proportion, fill = subcellular_location)) +
geom_bar(stat = "identity") +
coord_flip() +
facet_grid(cell~., scales = "free_y", space = "free_y") +
scale_fill_manual(values = c("#bd5db0","#9ae17c", "#be0024", "#7388ff", "#c456b7",
"#8ed470", "#7ec361", "#7d7304", "#f87a00", "#d543c7",
"#bead47", "#d148c3", "#da8836", "#e28504", "#d93eca",
"#c720b9", "#bc07ae", "#a40098", "#9a008e", "#e8d448",
"#104ed7", "#2c4ecc", "#00428c", "#393c6d", "#173b8f",
"#3f4c96", "#9ba2f5", "#727bcc", "#e59c5f", "#790000",
"#045d00", "#f9ad6f")) +
theme_bw() +
theme(strip.text.y = element_text(angle = 0))
Data (copied from your gist link; next time please use dput so that others can reproduce your example more easily):
> dput(df1)
structure(list(subcellular_location = c("actinFilaments", "aggresome",
"cellJunctions", "centrosome", "cytokineticBridge", "cytoplasmicBodies",
"cytosol", "endoplasmicReticulum", "endosome", "focalAdhesion",
"golgiApparatus", "intermediateFilaments", "lipidDroplets", "lysosomes",
"microtubuleEnds", "microtubuleOrganizingCenter", "microtubules",
"midbodyRing", "midbody", "mitochondria", "mitoticSpindle", "nuclearBodies",
"nuclearMembrane", "nuclearSpeckles", "nucleliFibrallar", "nucleoli",
"nucleoplasm", "nucleus", "peroxisomes", "plasmaMembrane", "rodsAndRings",
"vesicles"), HAP1_P5242 = c(0.009581882, 0.000338753, 0.011033682,
0.015824623, 0.003774681, 0.00232288, 0.227013163, 0.024535424,
0.001258227, 0.005807201, 0.04229578, 0.008710801, 0.0014518,
0.001064654, 0.00029036, 0.006484708, 0.013646922, 0.000483933,
0.001064654, 0.063637244, 0.00087108, 0.02303523, 0.013646922,
0.024535424, 0.013259775, 0.054587689, 0.195509098, 0.101480836,
0.00174216, 0.058072009, 0.000822687, 0.071815718), HAP1.wt_P8255.1 = c(0.0176,
0, 0.0032, 0.0096, 0, 0.0032, 0.3664, 0.0912, 0.008, 0.0032,
0.0128, 0, 0, 0.0064, 0, 0.0032, 0.0288, 0, 0, 0.0528, 0, 0.0128,
0.0048, 0.0096, 0, 0.0496, 0.1552, 0.0576, 0, 0.064, 0.0016,
0.0384), HAP1.wt_P8255.2 = c(0.013179572, 0, 0, 0.008237232,
0, 0.004942339, 0.36738056, 0.098846788, 0.003294893, 0.003294893,
0.016474465, 0.001647446, 0, 0.004942339, 0, 0.003294893, 0.029654036,
0, 0, 0.05107084, 0, 0.009884679, 0.004942339, 0.011532125, 0,
0.044481054, 0.154859967, 0.05601318, 0, 0.064250412, 0.001647446,
0.046128501), HAP1.wt_P8254.1 = c(0.012841091, 0, 0, 0.006420546,
0.001605136, 0.004815409, 0.362760835, 0.08988764, 0.001605136,
0.004815409, 0.017656501, 0.003210273, 0, 0.003210273, 0, 0.004815409,
0.032102729, 0, 0, 0.04975923, 0, 0.011235955, 0.003210273, 0.011235955,
0, 0.04975923, 0.160513644, 0.060995185, 0, 0.069020867, 0.001605136,
0.036918138), HAP1.wt_P8254.2 = c(0.015873016, 0, 0, 0.00952381,
0.001587302, 0.004761905, 0.357142857, 0.103174603, 0.003174603,
0.003174603, 0.014285714, 0.001587302, 0.001587302, 0.003174603,
0, 0.003174603, 0.03015873, 0, 0, 0.055555556, 0, 0.012698413,
0.006349206, 0.012698413, 0, 0.050793651, 0.152380952, 0.063492063,
0, 0.057142857, 0.001587302, 0.034920635), HAP1.kd_P8253.1 = c(0,
0, 0, 0, 0, 0, 0.270270271, 0.027027028, 0, 0, 0.027027028, 0,
0, 0, 0, 0, 0.054054053, 0, 0, 0, 0, 0, 0, 0.054054053, 0, 0.054054053,
0.405405405, 0.027027028, 0, 0, 0, 0.081081081), HAP1.kd_P8253.2 = c(0.021381579,
0, 0.003289474, 0.013157895, 0, 0.003289474, 0.368421053, 0.100328947,
0.004934211, 0.003289474, 0.013157895, 0.003289474, 0, 0.006578947,
0, 0.001644737, 0.027960526, 0, 0, 0.046052632, 0, 0.011513158,
0.004934211, 0.009868421, 0, 0.050986842, 0.15131579, 0.050986842,
0, 0.065789474, 0.001644737, 0.036184211), HAP1.kd_P8252.1 = c(0.018518518,
0, 0.00308642, 0.010802469, 0, 0.00462963, 0.354938272, 0.092592593,
0.00617284, 0.00462963, 0.018518518, 0, 0.00154321, 0.00462963,
0, 0.00617284, 0.026234568, 0, 0, 0.043209877, 0, 0.015432099,
0.00308642, 0.015432099, 0, 0.049382716, 0.154320988, 0.061728395,
0, 0.063271605, 0.00154321, 0.040123457), HAP1.kd_P8252.2 = c(0.012965964,
0, 0, 0.011345219, 0.001620746, 0.003241491, 0.367909238, 0.095623987,
0.003241491, 0.004862237, 0.017828201, 0.003241491, 0, 0.004862237,
0, 0.003241491, 0.030794165, 0, 0, 0.051863857, 0, 0.016207455,
0.003241491, 0.009724473, 0, 0.04376013, 0.1636953, 0.055105348,
0, 0.064829822, 0.001620746, 0.02917342), HAP1.kd_P8249.1 = c(0.010309278,
0.001718213, 0, 0.006872852, 0, 0.005154639, 0.197594502, 0.091065292,
0.001718213, 0, 0.013745704, 0.005154639, 0.001718213, 0.001718213,
0, 0, 0.027491409, 0, 0, 0.054982818, 0, 0.017182131, 0.013745704,
0.060137457, 0, 0.082474227, 0.240549828, 0.094501718, 0, 0.04467354,
0, 0.027491409), HAP1.kd_P8249.2 = c(0.010752688, 0, 0, 0.007168459,
0, 0.003584229, 0.20609319, 0.084229391, 0.001792115, 0, 0.007168459,
0.005376344, 0, 0.001792115, 0, 0, 0.03046595, 0, 0, 0.069892473,
0, 0.019713262, 0.014336918, 0.064516129, 0, 0.08781362, 0.224014337,
0.096774194, 0, 0.039426523, 0, 0.025089606), HAP1.kd_P8248.1 = c(0.007207207,
0, 0.001801802, 0.007207207, 0, 0.003603604, 0.198198198, 0.099099099,
0, 0, 0.009009009, 0.007207207, 0, 0, 0, 0.001801802, 0.025225225,
0, 0, 0.061261261, 0, 0.021621622, 0.016216216, 0.068468468,
0, 0.079279279, 0.234234234, 0.093693694, 0.001801802, 0.028828829,
0, 0.034234234), HAP1.kd_P8248.2 = c(0.005272408, 0.001757469,
0, 0.008787346, 0, 0.005272408, 0.202108963, 0.09314587, 0, 0,
0.014059754, 0.005272408, 0, 0, 0, 0.001757469, 0.029876977,
0, 0, 0.056239016, 0, 0.021089631, 0.014059754, 0.065026362,
0, 0.086115993, 0.228471002, 0.094903339, 0.001757469, 0.036906854,
0, 0.028119508), HAP1.wt_P8247.1 = c(0.016333938, 0, 0, 0.001814882,
0, 0.005444646, 0.197822141, 0.09800363, 0.001814882, 0, 0.007259528,
0.005444646, 0, 0.001814882, 0, 0.001814882, 0.030852995, 0,
0, 0.061705989, 0, 0.021778584, 0.012704174, 0.065335753, 0,
0.087114338, 0.234119782, 0.096188748, 0, 0.029038113, 0, 0.023593466
), HAP1.wt_P8247.2 = c(0.011173184, 0, 0, 0.003724395, 0, 0.003724395,
0.197392924, 0.098696462, 0.001862197, 0, 0.009310987, 0.005586592,
0, 0.001862197, 0, 0.001862197, 0.029795158, 0, 0, 0.059590317,
0, 0.018621974, 0.013035382, 0.067039106, 0, 0.08566108, 0.240223464,
0.096834264, 0, 0.029795158, 0, 0.024208566), HAP1.wt_P8246.1 = c(0.008880995,
0, 0, 0.005328597, 0.003552398, 0.003552398, 0.195381883, 0.090586146,
0, 0, 0.005328597, 0.008880995, 0, 0, 0, 0.001776199, 0.030195382,
0, 0, 0.051509769, 0, 0.023090586, 0.01598579, 0.063943162, 0,
0.097690941, 0.245115453, 0.097690941, 0, 0.026642984, 0, 0.024866785
), HAP1.wt_P8246.2 = c(0.009025271, 0, 0.001805054, 0.005415162,
0, 0.003610108, 0.19133574, 0.088447653, 0, 0.001805054, 0.012635379,
0.007220217, 0, 0, 0, 0, 0.028880866, 0, 0, 0.048736462, 0, 0.019855596,
0.027075812, 0.066787004, 0, 0.084837545, 0.241877256, 0.09566787,
0, 0.028880866, 0, 0.036101083), HAP1_P7964.1 = c(0.010040907,
0, 0.007437709, 0.017106731, 0.002975084, 0.003346969, 0.211230941,
0.040535515, 0.002603198, 0.005950167, 0.023056898, 0.00818148,
0.001115656, 0.002231313, 0.000743771, 0.005950167, 0.014503533,
0, 0.000743771, 0.065451841, 0.001115656, 0.023056898, 0.018966158,
0.031610264, 0, 0.065451841, 0.223875046, 0.105243585, 0.002603198,
0.051692079, 0.001487542, 0.051692079), MDS_P7246 = c(0.008080031,
0.000384763, 0.005386687, 0.012889573, 0.002885725, 0.002500962,
0.204116968, 0.035013467, 0.002116199, 0.00461716, 0.030973451,
0.008272412, 0.001539053, 0.001539053, 0.000192382, 0.003270489,
0.01250481, 0.000192382, 0.000961908, 0.082724125, 0.000577145,
0.025971528, 0.018661023, 0.030011543, 0.013851481, 0.065217391,
0.214313197, 0.108310889, 0.002116199, 0.048095421, 0.000577145,
0.052135437), MDS.L_P7246.1 = c(0.008308003, 0.000202634, 0.006079027,
0.013373858, 0.003039513, 0.002228976, 0.207294805, 0.036068891,
0.002026342, 0.004660587, 0.030800401, 0.008308003, 0.001621074,
0.001621074, 0.000202634, 0.003039513, 0.012563322, 0.000202634,
0.001013171, 0.081458956, 0.000405268, 0.026950351, 0.018034445,
0.030395133, 1.34e-07, 0.065450852, 0.218642321, 0.109017209,
0.002228976, 0.050050652, 0.000810537, 0.053900702), A673_P6591 = c(0.01081944,
0.000354736, 0.008158922, 0.013125222, 0.003015254, 0.003015254,
0.202554097, 0.035118836, 0.002128414, 0.006207875, 0.036183044,
0.006917347, 0.000886839, 0.001596311, 0.000709471, 0.004788932,
0.013657325, 0.000177368, 0.001064207, 0.069882937, 0.000709471,
0.025186236, 0.015253636, 0.029265697, 0.013125222, 0.06385243,
0.207875133, 0.106598084, 0.002305782, 0.056580348, 0.000354736,
0.058531394), A673_P6591.1 = c(0.011204482, 0.000186741, 0.008403361,
0.01363212, 0.003174603, 0.002614379, 0.203361345, 0.036414566,
0.002054155, 0.006162465, 0.036788049, 0.006722689, 0.000933707,
0.001680672, 0.000746965, 0.004668534, 0.01363212, 0.000186741,
0.001120448, 0.069467787, 0.000560224, 0.025957049, 0.014752568,
0.029505135, 0, 0.064239029, 0.212885154, 0.108496732, 0.002427638,
0.05751634, 0.000373483, 0.060130719), K562_P535 = c(0.008616975,
0.000143616, 0.007755278, 0.011202068, 0.002441476, 0.003303174,
0.278471923, 0.038776389, 0.00229786, 0.006031883, 0.033031739,
0.00689358, 0.003159558, 0.00229786, 0.000287233, 0.004164871,
0.012638231, 0.000287233, 0.000574465, 0.090621858, 0.000574465,
0.015941405, 0.009478673, 0.021255206, 0.009909522, 0.047536981,
0.181243717, 0.083871894, 0.002441476, 0.055292259, 0.00114893,
0.0583082), K562_P5494.1 = c(0.008692853, 0.000321957, 0.008692853,
0.012395364, 0.002736639, 0.002736639, 0.212813909, 0.032356729,
0.001448809, 0.004990341, 0.033000644, 0.007405023, 0.001448809,
0.001448809, 0.000160979, 0.004990341, 0.013039279, 0, 0.001126851,
0.074050225, 0.000643915, 0.027849324, 0.01545396, 0.029459111,
0, 0.065035415, 0.216355441, 0.111719253, 0.002092724, 0.051996137,
0.000804894, 0.054732775), K562_P5464.1 = c(0.009412153, 0.000495376,
0.008256275, 0.013705416, 0.002476882, 0.002476882, 0.20673712,
0.032529723, 0.001486129, 0.004788639, 0.034180978, 0.007595773,
0.001155878, 0.001321004, 0.000330251, 0.005284016, 0.012714663,
0.000330251, 0.000990753, 0.073811096, 0.000660502, 0.02823646,
0.016017173, 0.029722589, 0, 0.06489432, 0.217635403, 0.110634082,
0.002146631, 0.052179657, 0.000825627, 0.056968296), K562_P5359.1 = c(0.00740349,
0, 0.005288207, 0.005288207, 0.001057641, 0.003172924, 0.225806452,
0.063987308, 0.003172924, 0.004230566, 0.022739291, 0.005817028,
0.002644104, 0.002644104, 0, 0.003701745, 0.013749339, 0, 0.000528821,
0.099947118, 0, 0.015864622, 0.020095188, 0.037546272, 0, 0.080909572,
0.196192491, 0.090957166, 0.002115283, 0.040719196, 0.000528821,
0.043892121), K562_P5359.2 = c(0.007903056, 0, 0.004741834, 0.006322445,
0.001580611, 0.002107482, 0.223393045, 0.062170706, 0.002634352,
0.004741834, 0.023709168, 0.005795574, 0.002634352, 0.002634352,
0, 0.003688093, 0.014752371, 0, 0, 0.103266596, 0, 0.017386723,
0.021601686, 0.036354057, 0, 0.079030558, 0.192834563, 0.090621707,
0.002107482, 0.042676502, 0.00052687, 0.044783983), K562_P5358.1 = c(0.007462687,
0, 0.00533049, 0.005863539, 0.001599147, 0.003731343, 0.229744136,
0.064498934, 0.003198294, 0.004264392, 0.024520256, 0.005863539,
0.003198294, 0.002132196, 0, 0.003731343, 0.015458422, 0, 0,
0.101812367, 0, 0.015991471, 0.019189765, 0.036247335, 0, 0.077292111,
0.191364606, 0.087953092, 0.002132196, 0.041577825, 0.000533049,
0.045309168), K562_P5358.2 = c(0.006546645, 0, 0.005455537, 0.007637752,
0.001636661, 0.003273322, 0.225859247, 0.063829787, 0.003273322,
0.003818876, 0.024549918, 0.007092199, 0.002182215, 0.002727769,
0, 0.003818876, 0.015275505, 0, 0, 0.106382979, 0, 0.016912166,
0.01745772, 0.038188762, 0, 0.074195308, 0.195853792, 0.089470813,
0.001636661, 0.040370977, 0.000545554, 0.042007638), K562_P5357.1 = c(0.007057546,
0, 0.004885993, 0.00597177, 0.001085776, 0.003257329, 0.231813246,
0.06514658, 0.003257329, 0.004343105, 0.024972856, 0.00597177,
0.003257329, 0.002171553, 0, 0.003800217, 0.014115092, 0, 0,
0.118892508, 0, 0.01194354, 0.016829533, 0.030944625, 0, 0.07383279,
0.184039088, 0.086862106, 0.002171553, 0.049402823, 0.000542888,
0.043431053), K562_P5357.2 = c(0.008086253, 0, 0.003773585, 0.006469003,
0.001617251, 0.003234501, 0.23180593, 0.063072776, 0.003773585,
0.003234501, 0.023180593, 0.005929919, 0.003234501, 0.002695418,
0, 0.003773585, 0.014555256, 0, 0, 0.116442049, 0, 0.01509434,
0.017789757, 0.035579515, 0, 0.071698113, 0.189757412, 0.085714286,
0.002156334, 0.044743935, 0.000539084, 0.042048518), K562_P5356.1 = c(0.006292906,
0, 0.005148741, 0.004576659, 0.001716247, 0.002860412, 0.215675057,
0.070366133, 0.003432494, 0.003432494, 0.025743707, 0.005720824,
0.003432494, 0.002860412, 0, 0.004576659, 0.016018307, 0, 0,
0.127002288, 0, 0.01201373, 0.016590389, 0.03375286, 0, 0.076659039,
0.183638444, 0.086956522, 0.001716247, 0.044622426, 0.000572082,
0.044622426), K562_P5356.2 = c(0.00755814, 0, 0.004069767, 0.004651163,
0.001744186, 0.002325581, 0.21627907, 0.070930233, 0.002906977,
0.004069767, 0.025, 0.005813953, 0.003488372, 0.002906977, 0,
0.004069767, 0.015697674, 0, 0, 0.125581395, 0, 0.013953488,
0.015697674, 0.035465116, 0, 0.073837209, 0.190697674, 0.08372093,
0.001744186, 0.045348837, 0.000581395, 0.041860465), K562_P5355.1 = c(0.009320175,
0, 0.003289474, 0.007675439, 0.001096491, 0.002741228, 0.285087719,
0.059210526, 0.003837719, 0.006030702, 0.023026316, 0.003837719,
0.003837719, 0.003289474, 0, 0.003289474, 0.016995614, 0.000548246,
0, 0.094298246, 0, 0.014254386, 0.012061404, 0.026315789, 0,
0.052631579, 0.175438596, 0.08497807, 0.001096491, 0.057017544,
0.000548246, 0.048245614), K562_P5355.2 = c(0.008210181, 0, 0.004378763,
0.009304871, 0.001094691, 0.002189382, 0.280788177, 0.056376574,
0.003284072, 0.005473454, 0.024630542, 0.004378763, 0.003284072,
0.003284072, 0, 0.004378763, 0.016967707, 0, 0, 0.100164204,
0, 0.014778325, 0.012588944, 0.028461959, 0, 0.053639847, 0.172961138,
0.084838533, 0.001642036, 0.054187192, 0.000547345, 0.048166393
), K562_P5269.1 = c(0.007308161, 0, 0.003045067, 0.007917174,
0.001218027, 0.00365408, 0.228989038, 0.071863581, 0.00365408,
0.004263094, 0.017661389, 0.007308161, 0.002436054, 0.003045067,
0, 0.002436054, 0.017052375, 0, 0, 0.107186358, 0, 0.015834348,
0.020097442, 0.033495737, 0, 0.085261876, 0.18453106, 0.091961023,
0.002436054, 0.040194884, 0.001218027, 0.03593179), K562_P5269.2 = c(0.006234414,
0, 0.00436409, 0.006234414, 0.002493766, 0.002493766, 0.224438903,
0.073566085, 0.003117207, 0.002493766, 0.018703242, 0.006857855,
0.002493766, 0.003117207, 0, 0.001246883, 0.015586035, 0, 0,
0.109725686, 0, 0.015586035, 0.018703242, 0.034289277, 0, 0.082294264,
0.195760598, 0.092892768, 0.003117207, 0.039276808, 0.000623441,
0.034289277), K562_P5268.1 = c(0.004635762, 0, 0.00397351, 0.007284768,
0.001986755, 0.002649007, 0.214569536, 0.071523179, 0.00397351,
0.002649007, 0.01986755, 0.007284768, 0.003311258, 0.003311258,
0, 0.003311258, 0.016556291, 0, 0, 0.104635762, 0, 0.017880795,
0.018543046, 0.039735099, 0, 0.090066225, 0.195364238, 0.091390728,
0.001986755, 0.039735099, 0.000662252, 0.033112583), K562_P5268.2 = c(0.005242464,
0, 0.002621232, 0.00655308, 0.002621232, 0.00327654, 0.216251638,
0.070117955, 0.00327654, 0.00327654, 0.020969856, 0.008519004,
0.002621232, 0.001965924, 0, 0.002621232, 0.018348624, 0, 0,
0.108781127, 0, 0.015727392, 0.020314548, 0.040629096, 0, 0.087811271,
0.190039319, 0.090432503, 0.001965924, 0.040629096, 0.000655308,
0.034731324)), .Names = c("subcellular_location", "HAP1_P5242",
"HAP1.wt_P8255.1", "HAP1.wt_P8255.2", "HAP1.wt_P8254.1", "HAP1.wt_P8254.2",
"HAP1.kd_P8253.1", "HAP1.kd_P8253.2", "HAP1.kd_P8252.1", "HAP1.kd_P8252.2",
"HAP1.kd_P8249.1", "HAP1.kd_P8249.2", "HAP1.kd_P8248.1", "HAP1.kd_P8248.2",
"HAP1.wt_P8247.1", "HAP1.wt_P8247.2", "HAP1.wt_P8246.1", "HAP1.wt_P8246.2",
"HAP1_P7964.1", "MDS_P7246", "MDS.L_P7246.1", "A673_P6591", "A673_P6591.1",
"K562_P535", "K562_P5494.1", "K562_P5464.1", "K562_P5359.1",
"K562_P5359.2", "K562_P5358.1", "K562_P5358.2", "K562_P5357.1",
"K562_P5357.2", "K562_P5356.1", "K562_P5356.2", "K562_P5355.1",
"K562_P5355.2", "K562_P5269.1", "K562_P5269.2", "K562_P5268.1",
"K562_P5268.2"), class = "data.frame", row.names = c(NA, -32L
))