plot_elbow: plots Elbow curve data.
In ELBOW: ELBOW - Evaluating foLd change By the lOgit Way
Description
Usage
Arguments
Examples
Description
Plots an elbow curve and its associated data:
the upper and lower elbow limits for the curve
the upper, lower, and median initial condition
Elbow plots
the \logχ^2 p-value for the
Elbow curve
the variance for the upper and lower
Elbow cut-off values
Usage
1
2
3
4
plot_elbow(my_data, upper_limit, lower_limit, pvalue1,
prowmin, prowmax, prowmedian, max_upper_variance,
min_upper_variance, max_lower_variance,
min_lower_variance, gtitle = "")
Arguments
my_data
A table (data.frame) to plot. The columns
in the table should be as follows:
probes
— one column containing the names of the probes
fold — the fold values for the table
upper_limit
the upper limit/cut-off for the
elbow.
lower_limit
the lower limit/cut-off for the
elbow.
pvalue1
the \logχ^2 p-value for the elbow
curve.
prowmin
the error limit based on the maximum value
for each probe.
prowmax
the error limit based on the minimum value
for each probe.
prowmedian
the null (median) value for each
probe.
max_upper_variance
the maximum upper elbow limit
(most positive).
min_upper_variance
the minimum upper elbow limit.
max_lower_variance
the maximum lower elbow limit.
min_lower_variance
the minimum lower elbow limit
(most negative).
gtitle
the title to display for the graph.
Examples
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# read in the EcoliMutMA sample data from the package
data(EcoliMutMA, package="ELBOW")
csv_data <- EcoliMutMA
# - OR - Read in a CSV file (uncomment - remove the #'s
# - from the line below and replace 'filename' with
# the CSV file's filename)
# csv_data <- read.csv(filename)
# set the number of initial and final condition replicates both to three
init_count <- 3
final_count <- 3
# Parse the probes, intial conditions and final conditions
# out of the CSV file. Please see: extract_working_sets
# for more information.
#
# init_count should be the number of columns associated with
# the initial conditions of the experiment.
# final_count should be the number of columns associated with
# the final conditions of the experiment.
working_sets <- extract_working_sets(csv_data, init_count, final_count)
probes <- working_sets[[1]]
initial_conditions <- working_sets[[2]]
final_conditions <- working_sets[[3]]
# Uncomment to output the plot to a PNG file (optional)
# png(file="output_plot.png")
my_data <- replicates_to_fold(probes, initial_conditions, final_conditions)
# compute the elbow for the dataset
limits <- do_elbow(data.frame(my_data$fold))
plus_minus <- elbow_variance(probes, initial_conditions, final_conditions)
max_upper_variance <- plus_minus$max_upper
min_upper_variance <- plus_minus$min_upper
max_lower_variance <- plus_minus$max_lower
min_lower_variance <- plus_minus$min_lower
# rounded number for nice appearance graph
upper_limit <- round(limits[[1]],digits=2)
# rounded number nice appearance for graph
lower_limit <- round(limits[[2]],digits=2)
p_limits <- null_variance(my_data, upper_limit, lower_limit, initial_conditions)
prowmin <- p_limits[[1]]
prowmax <- p_limits[[2]]
prowmedian <- p_limits[[3]]
pvalue1 <- get_pvalue(my_data, upper_limit, lower_limit)
plot_elbow(my_data, upper_limit, lower_limit, pvalue1, prowmin, prowmax, prowmedian, max_upper_variance, min_upper_variance, max_lower_variance, min_lower_variance, "Title of the ELBOW Plot")
Example output
[1] "rowsums"
[1] "fold"
[1] 0.06873333 -0.08933333 0.34013333 0.19313333 0.00940000 -0.02596667
[1] "bound data"
ID_REF fold
1 1001_115 0.06873333
2 1002_33 -0.08933333
3 1003_942 0.34013333
4 1004_552 0.19313333
5 1005_657 0.00940000
6 1006_393 -0.02596667
[1] "firsta_data"
ID_REF fold
1 1001_115 0.06873333
2 1002_33 -0.08933333
3 1003_942 0.34013333
4 1004_552 0.19313333
5 1005_657 0.00940000
6 1006_393 -0.02596667
[1] "sorted"
[1] "headers"
[1] "rowsums"
[1] "fold"
[1] 0.2138 -0.2044 0.1604 0.1214 -0.1342 -0.0477
[1] "bound data"
ID_REF fold
1 1001_115 0.2138
2 1002_33 -0.2044
3 1003_942 0.1604
4 1004_552 0.1214
5 1005_657 -0.1342
6 1006_393 -0.0477
[1] "firsta_data"
ID_REF fold
1 1001_115 0.2138
2 1002_33 -0.2044
3 1003_942 0.1604
4 1004_552 0.1214
5 1005_657 -0.1342
6 1006_393 -0.0477
[1] "sorted"
[1] "headers"
[1] "rowsums"
[1] "fold"
[1] 0.2071 0.0046 0.0323 0.3172 0.0230 -0.0754
[1] "bound data"
ID_REF fold
1 1001_115 0.2071
2 1002_33 0.0046
3 1003_942 0.0323
4 1004_552 0.3172
5 1005_657 0.0230
6 1006_393 -0.0754
[1] "firsta_data"
ID_REF fold
1 1001_115 0.2071
2 1002_33 0.0046
3 1003_942 0.0323
4 1004_552 0.3172
5 1005_657 0.0230
6 1006_393 -0.0754
[1] "sorted"
[1] "headers"
[1] "rowsums"
[1] "fold"
[1] 0.2611 0.0195 0.0503 0.3531 -0.0252 -0.0441
[1] "bound data"
ID_REF fold
1 1001_115 0.2611
2 1002_33 0.0195
3 1003_942 0.0503
4 1004_552 0.3531
5 1005_657 -0.0252
6 1006_393 -0.0441
[1] "firsta_data"
ID_REF fold
1 1001_115 0.2611
2 1002_33 0.0195
3 1003_942 0.0503
4 1004_552 0.3531
5 1005_657 -0.0252
6 1006_393 -0.0441
[1] "sorted"
[1] "headers"
[1] "rowsums"
[1] "fold"
[1] 0.0346 -0.2222 0.5502 0.0479 -0.0125 0.0560
[1] "bound data"
ID_REF fold
1 1001_115 0.0346
2 1002_33 -0.2222
3 1003_942 0.5502
4 1004_552 0.0479
5 1005_657 -0.0125
6 1006_393 0.0560
[1] "firsta_data"
ID_REF fold
1 1001_115 0.0346
2 1002_33 -0.2222
3 1003_942 0.5502
4 1004_552 0.0479
5 1005_657 -0.0125
6 1006_393 0.0560
[1] "sorted"
[1] "headers"
[1] "rowsums"
[1] "fold"
[1] 0.0279 -0.0132 0.4221 0.2437 0.1447 0.0283
[1] "bound data"
ID_REF fold
1 1001_115 0.0279
2 1002_33 -0.0132
3 1003_942 0.4221
4 1004_552 0.2437
5 1005_657 0.1447
6 1006_393 0.0283
[1] "firsta_data"
ID_REF fold
1 1001_115 0.0279
2 1002_33 -0.0132
3 1003_942 0.4221
4 1004_552 0.2437
5 1005_657 0.1447
6 1006_393 0.0283
[1] "sorted"
[1] "headers"
[1] "rowsums"
[1] "fold"
[1] 0.0819 0.0017 0.4401 0.2796 0.0965 0.0596
[1] "bound data"
ID_REF fold
1 1001_115 0.0819
2 1002_33 0.0017
3 1003_942 0.4401
4 1004_552 0.2796
5 1005_657 0.0965
6 1006_393 0.0596
[1] "firsta_data"
ID_REF fold
1 1001_115 0.0819
2 1002_33 0.0017
3 1003_942 0.4401
4 1004_552 0.2796
5 1005_657 0.0965
6 1006_393 0.0596
[1] "sorted"
[1] "headers"
[1] "rowsums"
[1] "fold"
[1] -0.0828 -0.2743 0.5480 -0.0174 -0.0913 -0.0621
[1] "bound data"
ID_REF fold
1 1001_115 -0.0828
2 1002_33 -0.2743
3 1003_942 0.5480
4 1004_552 -0.0174
5 1005_657 -0.0913
6 1006_393 -0.0621
[1] "firsta_data"
ID_REF fold
1 1001_115 -0.0828
2 1002_33 -0.2743
3 1003_942 0.5480
4 1004_552 -0.0174
5 1005_657 -0.0913
6 1006_393 -0.0621
[1] "sorted"
[1] "headers"
[1] "rowsums"
[1] "fold"
[1] -0.0895 -0.0653 0.4199 0.1784 0.0659 -0.0898
[1] "bound data"
ID_REF fold
1 1001_115 -0.0895
2 1002_33 -0.0653
3 1003_942 0.4199
4 1004_552 0.1784
5 1005_657 0.0659
6 1006_393 -0.0898
[1] "firsta_data"
ID_REF fold
1 1001_115 -0.0895
2 1002_33 -0.0653
3 1003_942 0.4199
4 1004_552 0.1784
5 1005_657 0.0659
6 1006_393 -0.0898
[1] "sorted"
[1] "headers"
[1] "rowsums"
[1] "fold"
[1] -0.0355 -0.0504 0.4379 0.2143 0.0177 -0.0585
[1] "bound data"
ID_REF fold
1 1001_115 -0.0355
2 1002_33 -0.0504
3 1003_942 0.4379
4 1004_552 0.2143
5 1005_657 0.0177
6 1006_393 -0.0585
[1] "firsta_data"
ID_REF fold
1 1001_115 -0.0355
2 1002_33 -0.0504
3 1003_942 0.4379
4 1004_552 0.2143
5 1005_657 0.0177
6 1006_393 -0.0585
[1] "sorted"
[1] "headers"
[1] "upper elbow limit = 0.82 (replicate variance error 0.67 to 1.05 )"
[1] "lower elbow limit = -0.45 ( replicate variance error -0.37 to -0.63 )"
[1] "log chi squared p = 1.08e-44"
ELBOW documentation built on Nov. 8, 2020, 8:14 p.m.
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Usage Arguments Examples
Description
Plots an elbow curve and its associated data:
the upper and lower elbow limits for the curve
the upper, lower, and median initial condition Elbow plots
the \logχ^2 p-value for the Elbow curve
the variance for the upper and lower Elbow cut-off values
Usage
1 2 3 4 | plot_elbow(my_data, upper_limit, lower_limit, pvalue1,
prowmin, prowmax, prowmedian, max_upper_variance,
min_upper_variance, max_lower_variance,
min_lower_variance, gtitle = "")
|
Arguments
my_data |
A table (data.frame) to plot. The columns in the table should be as follows:
|
upper_limit |
the upper limit/cut-off for the elbow. |
lower_limit |
the lower limit/cut-off for the elbow. |
pvalue1 |
the \logχ^2 p-value for the elbow curve. |
prowmin |
the error limit based on the maximum value for each probe. |
prowmax |
the error limit based on the minimum value for each probe. |
prowmedian |
the null (median) value for each probe. |
max_upper_variance |
the maximum upper elbow limit (most positive). |
min_upper_variance |
the minimum upper elbow limit. |
max_lower_variance |
the maximum lower elbow limit. |
min_lower_variance |
the minimum lower elbow limit (most negative). |
gtitle |
the title to display for the graph. |
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 | # read in the EcoliMutMA sample data from the package
data(EcoliMutMA, package="ELBOW")
csv_data <- EcoliMutMA
# - OR - Read in a CSV file (uncomment - remove the #'s
# - from the line below and replace 'filename' with
# the CSV file's filename)
# csv_data <- read.csv(filename)
# set the number of initial and final condition replicates both to three
init_count <- 3
final_count <- 3
# Parse the probes, intial conditions and final conditions
# out of the CSV file. Please see: extract_working_sets
# for more information.
#
# init_count should be the number of columns associated with
# the initial conditions of the experiment.
# final_count should be the number of columns associated with
# the final conditions of the experiment.
working_sets <- extract_working_sets(csv_data, init_count, final_count)
probes <- working_sets[[1]]
initial_conditions <- working_sets[[2]]
final_conditions <- working_sets[[3]]
# Uncomment to output the plot to a PNG file (optional)
# png(file="output_plot.png")
my_data <- replicates_to_fold(probes, initial_conditions, final_conditions)
# compute the elbow for the dataset
limits <- do_elbow(data.frame(my_data$fold))
plus_minus <- elbow_variance(probes, initial_conditions, final_conditions)
max_upper_variance <- plus_minus$max_upper
min_upper_variance <- plus_minus$min_upper
max_lower_variance <- plus_minus$max_lower
min_lower_variance <- plus_minus$min_lower
# rounded number for nice appearance graph
upper_limit <- round(limits[[1]],digits=2)
# rounded number nice appearance for graph
lower_limit <- round(limits[[2]],digits=2)
p_limits <- null_variance(my_data, upper_limit, lower_limit, initial_conditions)
prowmin <- p_limits[[1]]
prowmax <- p_limits[[2]]
prowmedian <- p_limits[[3]]
pvalue1 <- get_pvalue(my_data, upper_limit, lower_limit)
plot_elbow(my_data, upper_limit, lower_limit, pvalue1, prowmin, prowmax, prowmedian, max_upper_variance, min_upper_variance, max_lower_variance, min_lower_variance, "Title of the ELBOW Plot")
|
Example output
[1] "rowsums"
[1] "fold"
[1] 0.06873333 -0.08933333 0.34013333 0.19313333 0.00940000 -0.02596667
[1] "bound data"
ID_REF fold
1 1001_115 0.06873333
2 1002_33 -0.08933333
3 1003_942 0.34013333
4 1004_552 0.19313333
5 1005_657 0.00940000
6 1006_393 -0.02596667
[1] "firsta_data"
ID_REF fold
1 1001_115 0.06873333
2 1002_33 -0.08933333
3 1003_942 0.34013333
4 1004_552 0.19313333
5 1005_657 0.00940000
6 1006_393 -0.02596667
[1] "sorted"
[1] "headers"
[1] "rowsums"
[1] "fold"
[1] 0.2138 -0.2044 0.1604 0.1214 -0.1342 -0.0477
[1] "bound data"
ID_REF fold
1 1001_115 0.2138
2 1002_33 -0.2044
3 1003_942 0.1604
4 1004_552 0.1214
5 1005_657 -0.1342
6 1006_393 -0.0477
[1] "firsta_data"
ID_REF fold
1 1001_115 0.2138
2 1002_33 -0.2044
3 1003_942 0.1604
4 1004_552 0.1214
5 1005_657 -0.1342
6 1006_393 -0.0477
[1] "sorted"
[1] "headers"
[1] "rowsums"
[1] "fold"
[1] 0.2071 0.0046 0.0323 0.3172 0.0230 -0.0754
[1] "bound data"
ID_REF fold
1 1001_115 0.2071
2 1002_33 0.0046
3 1003_942 0.0323
4 1004_552 0.3172
5 1005_657 0.0230
6 1006_393 -0.0754
[1] "firsta_data"
ID_REF fold
1 1001_115 0.2071
2 1002_33 0.0046
3 1003_942 0.0323
4 1004_552 0.3172
5 1005_657 0.0230
6 1006_393 -0.0754
[1] "sorted"
[1] "headers"
[1] "rowsums"
[1] "fold"
[1] 0.2611 0.0195 0.0503 0.3531 -0.0252 -0.0441
[1] "bound data"
ID_REF fold
1 1001_115 0.2611
2 1002_33 0.0195
3 1003_942 0.0503
4 1004_552 0.3531
5 1005_657 -0.0252
6 1006_393 -0.0441
[1] "firsta_data"
ID_REF fold
1 1001_115 0.2611
2 1002_33 0.0195
3 1003_942 0.0503
4 1004_552 0.3531
5 1005_657 -0.0252
6 1006_393 -0.0441
[1] "sorted"
[1] "headers"
[1] "rowsums"
[1] "fold"
[1] 0.0346 -0.2222 0.5502 0.0479 -0.0125 0.0560
[1] "bound data"
ID_REF fold
1 1001_115 0.0346
2 1002_33 -0.2222
3 1003_942 0.5502
4 1004_552 0.0479
5 1005_657 -0.0125
6 1006_393 0.0560
[1] "firsta_data"
ID_REF fold
1 1001_115 0.0346
2 1002_33 -0.2222
3 1003_942 0.5502
4 1004_552 0.0479
5 1005_657 -0.0125
6 1006_393 0.0560
[1] "sorted"
[1] "headers"
[1] "rowsums"
[1] "fold"
[1] 0.0279 -0.0132 0.4221 0.2437 0.1447 0.0283
[1] "bound data"
ID_REF fold
1 1001_115 0.0279
2 1002_33 -0.0132
3 1003_942 0.4221
4 1004_552 0.2437
5 1005_657 0.1447
6 1006_393 0.0283
[1] "firsta_data"
ID_REF fold
1 1001_115 0.0279
2 1002_33 -0.0132
3 1003_942 0.4221
4 1004_552 0.2437
5 1005_657 0.1447
6 1006_393 0.0283
[1] "sorted"
[1] "headers"
[1] "rowsums"
[1] "fold"
[1] 0.0819 0.0017 0.4401 0.2796 0.0965 0.0596
[1] "bound data"
ID_REF fold
1 1001_115 0.0819
2 1002_33 0.0017
3 1003_942 0.4401
4 1004_552 0.2796
5 1005_657 0.0965
6 1006_393 0.0596
[1] "firsta_data"
ID_REF fold
1 1001_115 0.0819
2 1002_33 0.0017
3 1003_942 0.4401
4 1004_552 0.2796
5 1005_657 0.0965
6 1006_393 0.0596
[1] "sorted"
[1] "headers"
[1] "rowsums"
[1] "fold"
[1] -0.0828 -0.2743 0.5480 -0.0174 -0.0913 -0.0621
[1] "bound data"
ID_REF fold
1 1001_115 -0.0828
2 1002_33 -0.2743
3 1003_942 0.5480
4 1004_552 -0.0174
5 1005_657 -0.0913
6 1006_393 -0.0621
[1] "firsta_data"
ID_REF fold
1 1001_115 -0.0828
2 1002_33 -0.2743
3 1003_942 0.5480
4 1004_552 -0.0174
5 1005_657 -0.0913
6 1006_393 -0.0621
[1] "sorted"
[1] "headers"
[1] "rowsums"
[1] "fold"
[1] -0.0895 -0.0653 0.4199 0.1784 0.0659 -0.0898
[1] "bound data"
ID_REF fold
1 1001_115 -0.0895
2 1002_33 -0.0653
3 1003_942 0.4199
4 1004_552 0.1784
5 1005_657 0.0659
6 1006_393 -0.0898
[1] "firsta_data"
ID_REF fold
1 1001_115 -0.0895
2 1002_33 -0.0653
3 1003_942 0.4199
4 1004_552 0.1784
5 1005_657 0.0659
6 1006_393 -0.0898
[1] "sorted"
[1] "headers"
[1] "rowsums"
[1] "fold"
[1] -0.0355 -0.0504 0.4379 0.2143 0.0177 -0.0585
[1] "bound data"
ID_REF fold
1 1001_115 -0.0355
2 1002_33 -0.0504
3 1003_942 0.4379
4 1004_552 0.2143
5 1005_657 0.0177
6 1006_393 -0.0585
[1] "firsta_data"
ID_REF fold
1 1001_115 -0.0355
2 1002_33 -0.0504
3 1003_942 0.4379
4 1004_552 0.2143
5 1005_657 0.0177
6 1006_393 -0.0585
[1] "sorted"
[1] "headers"
[1] "upper elbow limit = 0.82 (replicate variance error 0.67 to 1.05 )"
[1] "lower elbow limit = -0.45 ( replicate variance error -0.37 to -0.63 )"
[1] "log chi squared p = 1.08e-44"
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