This notebook replicates the analysis workflow created by Marc Rurik. The workflow wrapped in KNIME nodes can be found here, and is illustrated in the picture below. The workflow is developed to analyze HPLC-Orbitrap measurements split into separate or alternating m/z scan ranges. The input expects to be OpenMS consensusXML files, generated by the TextExporter in OpenMS. The workflow further combines the scan ranges, filter them ultilizing blank samples and QC measurements, and then performs statistical analysis to retrieve reliable features.
The aim of this notebook is to give an user friendly and interactive environment where you can get on-the-fly information about the progress of your data. After each step of the pipeline, we provide you with live feedback, consisting of informative plots.
Illustration by: Marc Rurik
In [1]:
low <- read.table("C:\\Users\\Milos\\Desktop\\reproducing\\alternate\\transformed_KNIME_low.csv",
fill=TRUE,
sep=",",
header=TRUE)
high <- read.table("C:\\Users\\Milos\\Desktop\\reproducing\\alternate\\transformed_KNIME_high.csv",
fill=TRUE,
sep=",",
header=TRUE)
In [2]:
print("Head and tail of low mass range")
head(low)
tail(low)
print("Head and tail of high mass range")
head(high)
tail(high)
[1] "Head and tail of low mass range"
Col0 Col1 Col2 Col3 Col4 Col5 Col6 Col7 Col8 Col9 ... Col202 Col203 Col204 Col205 Col206 Col207 Col208 Col209 Col210 Col211 MAP 0 C:/Users/UU/AppData/Local/Temp/2016-09-07_100519_The-big-daddy_17952_1/TOPPAS_tmp/pipeline_OpenMS/003_FeatureFinderMetabo/out/002_CRa_H9M5_M470_Pool_01_alternate_pos_low_mr.featureXML 4267 NA NA NA NA NA NA ... NA NA NA NA NA NA NA NA NA NA MAP 1 C:/Users/UU/AppData/Local/Temp/2016-09-07_100519_The-big-daddy_17952_1/TOPPAS_tmp/pipeline_OpenMS/003_FeatureFinderMetabo/out/003_CRa_H9M5_M470_Pool_02_alternate_pos_low_mr.featureXML 4350 NA NA NA NA NA NA ... NA NA NA NA NA NA NA NA NA NA MAP 2 C:/Users/UU/AppData/Local/Temp/2016-09-07_100519_The-big-daddy_17952_1/TOPPAS_tmp/pipeline_OpenMS/003_FeatureFinderMetabo/out/005_CRa_H9M5_M470_Blank_02_alternate_pos_low_mr.featureXML 3927 NA NA NA NA NA NA ... NA NA NA NA NA NA NA NA NA NA MAP 3 C:/Users/UU/AppData/Local/Temp/2016-09-07_100519_The-big-daddy_17952_1/TOPPAS_tmp/pipeline_OpenMS/003_FeatureFinderMetabo/out/006_CRa_H9M5_M470_H01_K1_alternate_pos_low_mr.featureXML 4307 NA NA NA NA NA NA ... NA NA NA NA NA NA NA NA NA NA MAP 4 C:/Users/UU/AppData/Local/Temp/2016-09-07_100519_The-big-daddy_17952_1/TOPPAS_tmp/pipeline_OpenMS/003_FeatureFinderMetabo/out/007_CRa_H9M5_M470_L01_K2_alternate_pos_low_mr.featureXML 4264 NA NA NA NA NA NA ... NA NA NA NA NA NA NA NA NA NA MAP 5 C:/Users/UU/AppData/Local/Temp/2016-09-07_100519_The-big-daddy_17952_1/TOPPAS_tmp/pipeline_OpenMS/003_FeatureFinderMetabo/out/009_CRa_H9M5_M470_Blank_03_alternate_pos_low_mr.featureXML 3997 NA NA NA NA NA NA ... NA NA NA NA NA NA NA NA NA NA
Col0 Col1 Col2 Col3 Col4 Col5 Col6 Col7 Col8 Col9 ... Col202 Col203 Col204 Col205 Col206 Col207 Col208 Col209 Col210 Col211 8194 CONSENSUS 320.79320 128.054465994324 5328.57 0 0 0.106416 NA NA NA ... NA NA NA 0 NA NA NA NA 0 NA 8195 CONSENSUS 40.72396 144.042934001551 10669.20 0 0 0.124759 NA NA NA ... NA NA NA 0 NA 40.72404 144.0429 6078.43 0 0 8196 CONSENSUS 42.65683 89.0083534707451 5822.09 0 0 0.124560 NA NA NA ... NA NA NA 0 NA NA NA NA 0 NA 8197 CONSENSUS 110.45890 123.048824655094 3500.60 0 0 0.101707 NA NA NA ... NA NA NA 0 NA NA NA NA 0 NA 8198 CONSENSUS 704.71000 102.14014940238 43513.70 0 0 0.105895 NA NA NA ... 698.292 102.1402 12688.2 0 0 702.42600 102.1401 14414.10 0 0 8199 CONSENSUS 39.75792 196.154363506471 4906.58 0 0 0.117545 NA NA NA ... NA NA NA 0 NA NA NA NA 0 NA
[1] "Head and tail of high mass range"
Col0 Col1 Col2 Col3 Col4 Col5 Col6 Col7 Col8 Col9 ... Col197 Col198 Col199 Col200 Col201 Col202 Col203 Col204 Col205 Col206 MAP 0 C:/Users/UU/AppData/Local/Temp/2016-09-07_100519_The-big-daddy_17952_1/TOPPAS_tmp/pipeline_OpenMS/003_FeatureFinderMetabo/out/001_CRa_H9M5_M470_Blank_01_alternate_pos_high_mr.featureXML 15839 NA NA NA NA NA NA ... NA NA NA NA NA NA NA NA NA NA MAP 1 C:/Users/UU/AppData/Local/Temp/2016-09-07_100519_The-big-daddy_17952_1/TOPPAS_tmp/pipeline_OpenMS/003_FeatureFinderMetabo/out/002_CRa_H9M5_M470_Pool_01_alternate_pos_high_mr.featureXML 17801 NA NA NA NA NA NA ... NA NA NA NA NA NA NA NA NA NA MAP 2 C:/Users/UU/AppData/Local/Temp/2016-09-07_100519_The-big-daddy_17952_1/TOPPAS_tmp/pipeline_OpenMS/003_FeatureFinderMetabo/out/005_CRa_H9M5_M470_Blank_02_alternate_pos_high_mr.featureXML 14937 NA NA NA NA NA NA ... NA NA NA NA NA NA NA NA NA NA MAP 3 C:/Users/UU/AppData/Local/Temp/2016-09-07_100519_The-big-daddy_17952_1/TOPPAS_tmp/pipeline_OpenMS/003_FeatureFinderMetabo/out/006_CRa_H9M5_M470_H01_K1_alternate_pos_high_mr.featureXML 18258 NA NA NA NA NA NA ... NA NA NA NA NA NA NA NA NA NA MAP 4 C:/Users/UU/AppData/Local/Temp/2016-09-07_100519_The-big-daddy_17952_1/TOPPAS_tmp/pipeline_OpenMS/003_FeatureFinderMetabo/out/008_CRa_H9M5_M470_H14_K1_alternate_pos_high_mr.featureXML 17573 NA NA NA NA NA NA ... NA NA NA NA NA NA NA NA NA NA MAP 5 C:/Users/UU/AppData/Local/Temp/2016-09-07_100519_The-big-daddy_17952_1/TOPPAS_tmp/pipeline_OpenMS/003_FeatureFinderMetabo/out/009_CRa_H9M5_M470_Blank_03_alternate_pos_high_mr.featureXML 15106 NA NA NA NA NA NA ... NA NA NA NA NA NA NA NA NA NA
Col0 Col1 Col2 Col3 Col4 Col5 Col6 Col7 Col8 Col9 ... Col197 Col198 Col199 Col200 Col201 Col202 Col203 Col204 Col205 Col206 30783 CONSENSUS 528.8558 759.628902945475 15729.90 0 0 0.0909835 NA NA NA ... 529.1640 759.6276 15163.60 0 0 534.0042 759.6283 46217.1 0 0 30784 CONSENSUS 626.1360 884.50867693131 16945.10 0 0 0.0665289 NA NA NA ... 624.8340 884.5052 20155.30 0 0 630.6420 884.5074 37142.7 0 0 30785 CONSENSUS 668.2470 569.490709081264 8645.73 0 0 0.0944540 NA NA NA ... 666.8880 569.4906 10020.00 0 0 667.1580 569.4920 12565.4 0 0 30786 CONSENSUS 626.3010 906.491936111008 11101.10 0 0 0.1062230 NA NA NA ... NA NA NA 0 NA 630.6420 906.4915 11463.6 0 0 30787 CONSENSUS 503.9165 743.576225796737 9055.52 0 0 0.0945130 NA NA NA ... 506.9706 743.5762 8418.28 0 0 NA NA NA 0 NA 30788 CONSENSUS 573.3078 824.651543877865 13796.90 0 0 0.0850217 NA NA NA ... 581.3484 824.6526 6436.42 0 0 576.5256 824.6491 12015.5 0 0
In [1]:
source('C:/Users/Milos/Desktop/reproducing/Functions.R')
[1] "The functions were succesfully loaded"
In [4]:
low_parsed <- Parse(low)
high_parsed <- Parse(high)
In [5]:
head(low_parsed)
mz rt 002_CRa_H9M5_M470_Pool_01_alternate_pos_low_mr.featureXML 003_CRa_H9M5_M470_Pool_02_alternate_pos_low_mr.featureXML 005_CRa_H9M5_M470_Blank_02_alternate_pos_low_mr.featureXML 006_CRa_H9M5_M470_H01_K1_alternate_pos_low_mr.featureXML 007_CRa_H9M5_M470_L01_K2_alternate_pos_low_mr.featureXML 009_CRa_H9M5_M470_Blank_03_alternate_pos_low_mr.featureXML 010_CRa_H9M5_M470_L03_K3_alternate_pos_low_mr.featureXML 011_CRa_H9M5_M470_C14_K2_alternate_pos_low_mr.featureXML ... 037_CRa_H9M5_M470_Blank_09_alternate_pos_low_mr.featureXML 038_CRa_H9M5_M470_L14_K3_alternate_pos_low_mr.featureXML 039_CRa_H9M5_M470_C03_K2_alternate_pos_low_mr.featureXML 040_CRa_H9M5_M470_L03_K1_alternate_pos_low_mr.featureXML 041_CRa_H9M5_M470_Blank_10_alternate_pos_low_mr.featureXML 042_CRa_H9M5_M470_Pool_06_alternate_pos_low_mr.featureXML 044_CRa_H9M5_M470_L14_K1_alternate_pos_low_mr.featureXML 045_CRa_H9M5_M470_L14_K2_alternate_pos_low_mr.featureXML 046_CRa_H9M5_M470_H03_K3_alternate_pos_low_mr.featureXML 047_CRa_H9M5_M470_Blank_11_alternate_pos_low_mr.featureXML 7785 55.018053688842 573.95 NA NA NA NA NA NA 2220.18994140625 NA ... 8783.259765625 NA NA NA NA NA NA NA 6772.740234375 NA 5654 55.0418538981374 566.919466666667 NA NA NA NA NA NA 17487.099609375 NA ... NA NA NA 3574.56005859375 6458.25 NA NA NA NA 5303.68994140625 6284 55.0418546450561 537.75225 NA NA NA NA NA NA 8910.9404296875 NA ... NA NA NA 7683.830078125 2244.01000976563 NA NA NA NA 8516.5498046875 4163 55.0418547740727 521.62125 NA NA NA 5419.83984375 NA NA 6018.509765625 NA ... NA NA NA 12854.599609375 10305 NA NA NA NA 15439.099609375 6414 55.0418626800408 444.041775 NA NA NA NA NA NA NA NA ... NA NA NA 1925.11999511719 10511.2001953125 NA NA NA NA NA 5675 55.0418645991614 467.4084 NA NA NA NA NA NA 4923.56005859375 NA ... NA NA NA NA 9295.6298828125 NA NA NA NA 10789.2998046875
In [8]:
low_renamed <- renameColumns(low_parsed)
cat("Number of samples existing in the low range: ",length(colnames(low_renamed)), "\n")
high_renamed <- renameColumns(high_parsed)
cat("Number of samples existing in the high range: ",length(colnames(high_renamed)), "\n")
cat("Number of samples existing in both ranges: ",
length(intersect(colnames(low_renamed),colnames(high_renamed))))
Number of samples existing in the low range: 43
Number of samples existing in the high range: 42
Number of samples existing in both ranges: 39
In [9]:
cat("Samples missing in the low mass range: ",
names(high_renamed)[!names(high_renamed) %in% names(low_renamed)], "\n") #low
cat("Samples missing in the high mass range: ",
names(low_renamed)[!names(low_renamed) %in% names(high_renamed)]) #high
# Extending the high mass range with the missing samples
missing_high <- names(low_renamed)[!names(low_renamed) %in% names(high_renamed)]
extend_high <- data.frame(matrix(,nrow=nrow(high_renamed), ncol=length(missing_high)))
names(extend_high) <- missing_high
extend_high <- cbind(high_renamed,
extend_high)
# Extending the low mass range with the missing samples
missing_low <- names(high_renamed)[!names(high_renamed) %in% names(low_renamed)]
extend_low <- data.frame(matrix(,nrow=nrow(low_renamed), ncol=length(missing_low)))
names(extend_low) <- missing_low
extend_low <- cbind(low_renamed,extend_low)
extend_high <- extend_high[,names(extend_low)]
# Concatenating the scan ranges
full_range <- rbind(extend_low, extend_high)
names(full_range)<-gsub("_",".",names(full_range))
Samples missing in the low mass range: Blank_01 H14_K1 Blank_05
Samples missing in the high mass range: Pool_02 L01_K2 H03_K1 Pool_05
In [10]:
blankFilterPassed = 20 #Number of samples the feature has to be present in
full_range <- blankFilter(full_range,blankFilterPassed)
399 number of features passed
In [11]:
full_range_norm <- consensusMapNormalization(full_range)
[1] "mz" "rt" "Pool.01" "Pool.02" "Blank.02" "H01.K1"
[7] "L01.K2" "Blank.03" "L03.K3" "C14.K2" "C01.K1" "Blank.04"
[13] "Pool.03" "H14.K2" "H01.K2" "H03.K1" "C01.K3" "C14.K3"
[19] "L03.K2" "Blank.06" "Pool.04" "C03.K1" "H03.K2" "H14.K3"
[25] "Blank.07" "L01.K3" "C14.K1" "H01.K3" "Blank.08" "Pool.05"
[31] "L01.K1" "C01.K2" "C03.K3" "Blank.09" "L14.K3" "C03.K2"
[37] "L03.K1" "Blank.10" "Pool.06" "L14.K1" "L14.K2" "H03.K3"
[43] "Blank.11" "Blank.01" "H14.K1" "Blank.05"
Non-normalized columns:
[1] "mz" "rt" "Blank.02" "Blank.03" "Blank.04" "Blank.06"
[7] "Blank.07" "Blank.08" "Blank.09" "Blank.10" "Blank.11" "Blank.01"
[13] "Blank.05"
Feature count:
[1] Pool.01: 364
[1] Pool.02: 82
[1] H01.K1: 377
[1] L01.K2: 80
[1] L03.K3: 386
[1] C14.K2: 315
[1] C01.K1: 381
[1] Pool.03: 376
[1] H14.K2: 372
[1] H01.K2: 346
[1] H03.K1: 87
[1] C01.K3: 385
[1] C14.K3: 382
[1] L03.K2: 386
[1] Pool.04: 383
[1] C03.K1: 387
[1] H03.K2: 378
[1] H14.K3: 370
[1] L01.K3: 383
[1] C14.K1: 376
[1] H01.K3: 385
[1] Pool.05: 86
[1] L01.K1: 387
[1] C01.K2: 377
[1] C03.K3: 384
[1] L14.K3: 384
[1] C03.K2: 366
[1] L03.K1: 390
[1] Pool.06: 381
[1] L14.K1: 383
[1] L14.K2: 370
[1] H03.K3: 385
[1] H14.K1: 275
Most features:
[1] L03.K1: 390
Normalization ratios (map with most features / other map):
Method: mean
Outlier: 0.68 < ratio < 1.5
[1] L03.K1 / Pool.01 = 1.08928910927012
[1] L03.K1 / Pool.02 = 1.17832723118759
[1] L03.K1 / H01.K1 = 1.04107503996371
[1] L03.K1 / L01.K2 = 1.12903598273475
[1] L03.K1 / L03.K3 = 1.03683948472117
[1] L03.K1 / C14.K2 = 1.07780443878374
[1] L03.K1 / C01.K1 = 0.963061556890648
[1] L03.K1 / Pool.03 = 1.00711848901558
[1] L03.K1 / H14.K2 = 1.0182715337612
[1] L03.K1 / H01.K2 = 1.06152301081573
[1] L03.K1 / H03.K1 = 1.01502654285714
[1] L03.K1 / C01.K3 = 1.08803333306876
[1] L03.K1 / C14.K3 = 1.09334669707088
[1] L03.K1 / L03.K2 = 0.898088371576557
[1] L03.K1 / Pool.04 = 0.971219473087716
[1] L03.K1 / C03.K1 = 1.09002474234736
[1] L03.K1 / H03.K2 = 0.978076774457934
[1] L03.K1 / H14.K3 = 1.01857067049187
[1] L03.K1 / L01.K3 = 1.04901761220858
[1] L03.K1 / C14.K1 = 1.09793062534627
[1] L03.K1 / H01.K3 = 1.07047912069505
[1] L03.K1 / Pool.05 = 1.12182697238477
[1] L03.K1 / L01.K1 = 1.09010624978688
[1] L03.K1 / C01.K2 = 0.920983887230634
[1] L03.K1 / C03.K3 = 1.07182831045502
[1] L03.K1 / L14.K3 = 1.07414217948978
[1] L03.K1 / C03.K2 = 1.04000992733607
[1] L03.K1 / L03.K1 = 1
[1] L03.K1 / Pool.06 = 0.969192039234425
[1] L03.K1 / L14.K1 = 1.06004367584247
[1] L03.K1 / L14.K2 = 1.03887257936087
[1] L03.K1 / H03.K3 = 1.01863162471971
[1] L03.K1 / H14.K1 = 1.03614947597529
In [12]:
# Boxplots before and after normalization
n <- ncol(data.matrix(full_range[,-which(names(full_range) %in% full_range_norm$ignoredCols)])) + 7
cc <- rep("black", length(names(full_range[,-which(names(full_range) %in% full_range_norm$ignoredCols)])))
cc[grep("Pool", names(full_range[,-which(names(full_range) %in% full_range_norm$ignoredCols)]))] <- topo.colors(5)[1]
cc[grep("C", names(full_range[,-which(names(full_range) %in% full_range_norm$ignoredCols)]))] <- topo.colors(5)[2]
cc[grep("L", names(full_range[,-which(names(full_range) %in% full_range_norm$ignoredCols)]))] <- topo.colors(5)[3]
cc[grep("H", names(full_range[,-which(names(full_range) %in% full_range_norm$ignoredCols)]))] <- topo.colors(5)[4]
par(mfrow=c(2,1),oma = c(0, 0, 0, 0))
boxplot(log2(data.matrix(full_range[,-which(names(full_range) %in% full_range_norm$ignoredCols)])),
main="Before normalization",
col=cc,las=2, cex.axis=0.8)
boxplot(log2(data.matrix(full_range_norm$df[,-which(names(full_range_norm$df) %in% full_range_norm$ignoredCols)])),
main="After normalization",
col=cc, las=2, cex.axis=0.8)
par(mfrow=c(1,1))
In [16]:
head(full_range)
head(full_range_norm$df)
mz rt Pool.01 Pool.02 Blank.02 H01.K1 L01.K2 Blank.03 L03.K3 C14.K2 ... L03.K1 Blank.10 Pool.06 L14.K1 L14.K2 H03.K3 Blank.11 Blank.01 H14.K1 Blank.05 712 62.0239318918682 42.65704875 16140.5 15609.2001953125 NA 21198.099609375 15190.7998046875 NA 24647.80078125 26306.900390625 ... 22416.69921875 NA 25208.69921875 17198.099609375 23959 30239.099609375 NA NA NA NA 713 62.0601584853407 47.4887748387097 10214.7001953125 8620.3095703125 NA 13054 7253.27978515625 NA 10014.5 7460.14013671875 ... 10149.2998046875 NA 9552.6201171875 10845.099609375 9779.5400390625 9602.1298828125 NA NA NA NA 716 64.0158880576918 45.5561178947368 57882.8984375 43353 5723.18994140625 28183.30078125 65687.1015625 3016.330078125 48044.6015625 42436.5 ... 39669.8984375 NA 45387.6015625 35494.5 38717.6015625 30159.5 NA NA NA NA 761 71.0291595196379 42.6571151351351 60555.3984375 55938.3984375 NA 61958.6015625 61907.5 4892.2998046875 79584.703125 92068.8984375 ... 82708.6015625 NA 83737.296875 61751.30078125 80694.203125 100121 NA NA NA NA 795 73.5318403862944 42.6571168421053 91739.796875 90443.296875 7529.31982421875 104276 97930.796875 7592.02978515625 131104 147737 ... 133079 NA 128848 93771.3984375 119503 187634 NA NA NA NA 796 74.0236917045672 47.488775625 47201.80078125 46736.80078125 NA 50628.6015625 54791.6015625 NA 66483.203125 71014 ... 71762 NA 51586.6015625 67929.5 76926.5 6910.35009765625 NA NA NA NA
mz rt Pool.01 Pool.02 Blank.02 H01.K1 L01.K2 Blank.03 L03.K3 C14.K2 ... L03.K1 Blank.10 Pool.06 L14.K1 L14.K2 H03.K3 Blank.11 Blank.01 H14.K1 Blank.05 62.0239318918682 42.65704875 17581.67 18392.75 NA 22068.81 17150.960 NA 25555.81 28353.694 ... 22416.7 NA 24432.071 18230.74 24890.35 30802.503 NA NA NA NA 62.0601584853407 47.4887748387097 11126.76 10157.55 NA 13590.19 8189.214 NA 10383.43 8040.572 ... 10149.3 NA 9258.323 11496.28 10159.70 9781.033 NA NA NA NA 64.0158880576918 45.5561178947368 63051.21 51084.02 5723.18994140625 29340.93 74163.101 3016.330078125 49814.54 45738.248 ... 39669.9 NA 43989.302 37625.72 40222.65 30721.420 NA NA NA NA 71.0291595196379 42.6571151351351 65962.34 65913.74 NA 64503.55 69895.795 4892.2998046875 82516.56 99232.267 ... 82708.6 NA 81157.522 65459.08 83830.99 101986.417 NA NA NA NA 73.5318403862944 42.6571168421053 99931.16 106571.80 7529.31982421875 108559.14 110567.393 7592.02978515625 135933.80 159231.594 ... 133079.0 NA 124878.456 99401.78 124148.39 191129.926 NA NA NA NA 74.0236917045672 47.488775625 51416.41 55071.25 NA 52708.17 61861.690 NA 68932.41 76539.204 ... 71762.0 NA 49997.324 72008.24 79916.83 7039.101 NA NA NA NA
In [18]:
full_range_filt <- QAFilter(full_range_norm$df)
cat(nrow(full_range_filt), "number of features left")
70 number of features left
In [26]:
full_range_imputed <- missingValues(full_range_filt)
In [37]:
cat("Number of detected features: ", nrow(full_range_imputed))
Number of detected features: 399
In [35]:
low_mass <- sum(as.numeric(full_range_imputed$mz)<200)
mid_mass <- sum(as.numeric(full_range_imputed$mz)>200 & as.numeric(full_range_imputed$mz)<400)
high_mass <- sum(as.numeric(full_range_imputed$mz)>400 & as.numeric(full_range_imputed$mz)<1000)
data <- data.frame(x=c("50-200", "200-400", "400-1000"),
y=c(low_mass,mid_mass,high_mass))
#data$y <- c(70,138,89)
ylimit <- c(0, 1.2*max(data$y))
par(oma=c(2,5,3,5))
xx <- barplot(data$y,
names.arg=data$x,
ylim=ylimit,
ylab="No. of features",
xlab="m/z range",
col=topo.colors(6))
text(x=xx,
y=data$y,
label=data$y,
pos = 3)
title("Number of features detected in selected mass segment", outer = TRUE)
#pie(data$y, col=topo.colors(6))
In [29]:
stat_features <- statisticalAnalysis(full_range_imputed)
In [30]:
source('~/Klinisk farm/R scripts/multiplot.R')
FoldChange <- data.frame(stat_features[,grep("fc", names(stat_features))])
pvalues <- data.frame(stat_features[,grep("pval", names(stat_features))])
header <- gsub("_pval", "",names(pvalues))
par(mfrow=c(2,length(FoldChange)/2), oma= c(0,0,11,0), cex.main= 2)
for (i in 1:length(FoldChange)) {
plot(log2(FoldChange[,i]),
-log10(pvalues[,i]),
pch=20,
xlim=c(-max(log2(FoldChange[,i])),max(log2(FoldChange[,i]))),
main = header[i],
cex.main= 1.5,
xlab = "log2(FoldChange)",
ylab = "-log10(pvalue)")
fc <- which(abs(log2(FoldChange[,i]))>max(log2(FoldChange[,i]))/2)
p <- which(-log10(pvalues[,i])>max(-log10(pvalues[,i]))/2)
fc_p <- intersect(fc,p)
points(log2(FoldChange[fc,i]), # orange = high fold change
-log10(pvalues[fc,i]),
col="orange", pch=20)
points(log2(FoldChange[p,i]), # red = low p-value
-log10(pvalues[p,i]),
col="red", pch=20)
points(log2(FoldChange[fc_p,i]), # green = high fold change and low p_value
-log10(pvalues[fc_p,i]),
col="darkgreen", pch=20)
}
title("Volcano plots", outer=TRUE)
In [25]:
write.csv(stat_features,"C:\\Users\\Milos\\Desktop\\reproducing\\alternate\\realiable_features.csv", row.names = F)
Content source: phnmnl/MTBLS233-POP
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