| Previous changeset 3:830c6df59603 (2018-04-23) Next changeset 5:cee9cf693709 (2018-06-11) |
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Commit message:
planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/msi_segmentation commit 1c808d60243bb1eeda0cd26cb4b0a17ab05de2c0 |
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modified:
segmentation_tool.xml test-data/Analyze75.hdr test-data/Analyze75.img test-data/Analyze75.t2m test-data/cluster_skm.tabular test-data/loadings_pca.tabular test-data/pca_imzml.pdf test-data/scores_pca.tabular test-data/toplabels_skm.tabular test-data/toplabels_ssc.tabular |
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added:
test-data/centroids_rdata.pdf test-data/kmeans_analyze.pdf test-data/preprocessed.RData |
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removed:
test-data/centroids_imzml.pdf test-data/kmeans_imzml.pdf test-data/preprocessing_results1.RData |
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| diff -r 830c6df59603 -r aec189b0c64d segmentation_tool.xml --- a/segmentation_tool.xml Mon Apr 23 17:19:53 2018 -0400 +++ b/segmentation_tool.xml Mon May 28 12:39:28 2018 -0400 |
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| b'@@ -1,21 +1,20 @@\n-<tool id="mass_spectrometry_imaging_segmentations" name="MSI segmentation" version="1.7.0.3">\n+<tool id="mass_spectrometry_imaging_segmentations" name="MSI segmentation" version="1.10.0.0">\n <description>tool for spatial clustering</description>\n <requirements>\n- <requirement type="package" version="1.7.0">bioconductor-cardinal</requirement>\n+ <requirement type="package" version="1.10.0">bioconductor-cardinal</requirement>\n <requirement type="package" version="2.2.1">r-gridextra</requirement>\n- <requirement type="package" version="2.23-15">r-kernsmooth</requirement>\n <requirement type="package" version="0.20-35">r-lattice</requirement>\n </requirements>\n <command detect_errors="exit_code">\n <![CDATA[\n \n #if $infile.ext == \'imzml\'\n- cp \'${infile.extra_files_path}/imzml\' infile.imzML &&\n- cp \'${infile.extra_files_path}/ibd\' infile.ibd &&\n+ ln -s \'${infile.extra_files_path}/imzml\' infile.imzML &&\n+ ln -s \'${infile.extra_files_path}/ibd\' infile.ibd &&\n #elif $infile.ext == \'analyze75\'\n- cp \'${infile.extra_files_path}/hdr\' infile.hdr &&\n- cp \'${infile.extra_files_path}/img\' infile.img &&\n- cp \'${infile.extra_files_path}/t2m\' infile.t2m &&\n+ ln -s \'${infile.extra_files_path}/hdr\' infile.hdr &&\n+ ln -s \'${infile.extra_files_path}/img\' infile.img &&\n+ ln -s \'${infile.extra_files_path}/t2m\' infile.t2m &&\n #else\n ln -s $infile infile.RData &&\n #end if\n@@ -34,20 +33,19 @@\n \n library(Cardinal)\n library(gridExtra)\n-library(KernSmooth)\n library(lattice)\n \n ## Read MALDI Imaging dataset\n \n #if $infile.ext == \'imzml\'\n- msidata = readMSIData(\'infile.imzML\')\n+ msidata = readImzML(\'infile\')\n #elif $infile.ext == \'analyze75\'\n- msidata = readMSIData(\'infile.hdr\')\n+ msidata = readAnalyze(\'infile\')\n #else\n load(\'infile.RData\')\n #end if\n \n-###################################### file properties in numbers ######################\n+###################################### file properties in numbers ##############\n \n ## Number of features (mz)\n maxfeatures = length(features(msidata))\n@@ -145,9 +143,9 @@\n property_df = data.frame(properties, values)\n \n \n-######################################## PDF #############################################\n-##########################################################################################\n-##########################################################################################\n+######################################## PDF ###################################\n+################################################################################\n+################################################################################\n \n \n pdf("segmentationpdf.pdf", fonts = "Times", pointsize = 12)\n@@ -180,7 +178,7 @@\n pca = PCA(msidata, ncomp=$segm_cond.pca_ncomp, column = component_vector, superpose = FALSE, \n method = "$segm_cond.pca_method", scale = $segm_cond.pca_scale, layout = c(ncomp, 1))\n \n- print(image(pca, main="PCA image", lattice=TRUE, strip = strip.custom(bg="lightgrey", par.strip.text=list(col="black", cex=.9)), col=colourvector, ylim=c(maximumy+2, 0)))\n+ print(image(pca, main="PCA image", lattice=TRUE, strip = strip.custom(bg="lightgrey", par.strip.text=list(col="black", cex=.9)), col=colourvector))\n print(plot(pca, main="PCA plot", lattice=TRUE, col= colourvector, strip = strip.custom(bg="lightgrey", par.strip.text=list(col="black", cex=.9))))\n \n \n@@ -195,7 +193,7 @@\n ##k-means\n \n skm = spatialKMeans(msidata, r=c($segm_cond.kmeans_r), k=c($segm_cond.kmeans_k), method="$segm_cond.kmeans_method")\n- print(image(skm, key=TRUE, main="K-means clustering", lattice=TRUE, strip = strip.custom(bg="lightgrey", par.strip.text=list(col="black", cex=.9)), col= colourvector, ylim=c(maximumy+2,'..b'means"/>\n <param name="kmeans_r" value="1:3"/>\n <param name="kmeans_k" value="2,3"/>\n+ <param name="kmeans_toplabels" value="20"/>\n <repeat name="colours">\n <param name="feature_color" value="#ff00ff"/>\n </repeat>\n@@ -352,16 +351,16 @@\n <repeat name="colours">\n <param name="feature_color" value="#00C957"/>\n </repeat>\n- <output name="segmentationimages" file="kmeans_imzml.pdf" compare="sim_size" delta="20000"/>\n+ <output name="segmentationimages" file="kmeans_analyze.pdf" compare="sim_size" delta="20000"/>\n <output name="mzfeatures" file="toplabels_skm.tabular" compare="sim_size"/>\n <output name="pixeloutput" file="cluster_skm.tabular" compare="sim_size"/>\n </test>\n <test>\n- <param name="infile" value="preprocessing_results1.RData" ftype="rdata"/>\n+ <param name="infile" value="preprocessed.RData" ftype="rdata"/>\n <param name="segmentationtool" value="centroids"/>\n- <param name="centroids_r" ftype="text" value="1,2"/>\n- <param name="centroids_k" ftype="text" value="5"/>\n- <param name="centroids_toplabels" ftype="integer" value="100"/>\n+ <param name="centroids_r" value="1,2"/>\n+ <param name="centroids_k" value="5"/>\n+ <param name="centroids_toplabels" value="50"/>\n <repeat name="colours">\n <param name="feature_color" value="#0000FF"/>\n </repeat>\n@@ -377,7 +376,7 @@\n <repeat name="colours">\n <param name="feature_color" value="#848484"/>\n </repeat>\n- <output name="segmentationimages" file="centroids_imzml.pdf" compare="sim_size" delta="20000"/>\n+ <output name="segmentationimages" file="centroids_rdata.pdf" compare="sim_size" delta="20000"/>\n <output name="mzfeatures" file="toplabels_ssc.tabular" compare="sim_size"/>\n <output name="pixeloutput" file="classes_ssc.tabular" compare="sim_size"/>\n </test>\n@@ -385,16 +384,27 @@\n <help>\n <![CDATA[\n \n-Spatially aware segmentation of mass-spectrometry imaging data by unsupervised clustering algorithms. Underlying structures can be identified with the following tools: pca, k-means clustering and spatial shrunken centroids. The spatialShrunkenCentroids method allows the number of segments to decrease according to the data. This allows automatic selection of the number\n-of clusters.\n+Cardinal is an R package that implements statistical & computational tools for analyzing mass spectrometry imaging datasets. `More information on Cardinal <http://cardinalmsi.org//>`_\n+\n+This tool provides three different Cardinal functions for unsupervised clustering/spatial segmentation of mass-spectrometry imaging data.\n \n Input data: 3 types of input data can be used:\n \n-- imzml file (upload imzml and ibd file via the "composite" function) `Introduction to the imzml format <http://ms-imaging.org/wp/introduction/>`_\n+- imzml file (upload imzml and ibd file via the "composite" function) `Introduction to the imzml format <https://ms-imaging.org/wp/imzml/>`_\n - Analyze7.5 (upload hdr, img and t2m file via the "composite" function)\n - Cardinal "MSImageSet" data (with variable name "msidata", saved as .RData)\n \n-The output of this tool contains a pdf with plots from the segmentation tools. \n+Options: \n+\n+- PCA: principal component analysis\n+- k-means: patially-aware k-means clustering\n+- spatial shrunken centroids: Allows the number of segments to decrease according to the data. This allows automatic selection of the number of clusters\n+\n+Output: \n+\n+- Pdf with the heatmaps and plots for the segmentation\n+- Tabular file with information on masses and pixels: loadings/scores (PCA), toplabels/clusters (k-means), toplabels/classes (spatial shrunken centroids)\n+\n ]]>\n </help>\n <citations>\n' |
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| diff -r 830c6df59603 -r aec189b0c64d test-data/cluster_skm.tabular --- a/test-data/cluster_skm.tabular Mon Apr 23 17:19:53 2018 -0400 +++ b/test-data/cluster_skm.tabular Mon May 28 12:39:28 2018 -0400 |
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| @@ -1,10 +1,10 @@ r = 1, k = 2 r = 1, k = 3 r = 2, k = 2 r = 2, k = 3 r = 3, k = 2 r = 3, k = 3 -x = 1, y = 1 2 3 2 3 2 3 -x = 2, y = 1 2 2 2 2 2 2 -x = 3, y = 1 2 2 2 2 2 2 -x = 1, y = 2 2 2 2 2 2 2 -x = 2, y = 2 2 2 2 2 2 2 -x = 3, y = 2 2 2 2 2 2 2 -x = 1, y = 3 1 1 1 1 1 1 -x = 2, y = 3 2 2 2 2 2 2 -x = 3, y = 3 2 2 2 2 2 2 +x = 1, y = 1 2 2 2 2 2 3 +x = 2, y = 1 1 1 1 1 1 1 +x = 3, y = 1 1 3 2 3 2 3 +x = 1, y = 2 2 2 2 2 2 3 +x = 2, y = 2 1 1 1 1 1 1 +x = 3, y = 2 1 3 2 3 2 3 +x = 1, y = 3 2 2 2 2 2 2 +x = 2, y = 3 1 1 1 1 1 1 +x = 3, y = 3 1 3 2 3 2 3 |
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| diff -r 830c6df59603 -r aec189b0c64d test-data/loadings_pca.tabular --- a/test-data/loadings_pca.tabular Mon Apr 23 17:19:53 2018 -0400 +++ b/test-data/loadings_pca.tabular Mon May 28 12:39:28 2018 -0400 |
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| b'@@ -1,8400 +1,8400 @@\n \tPC1\tPC2\n-m/z = 100.08\t9.90729163498881e-20\t4.59277533206058e-20\n-m/z = 100.17\t2.00845732051153e-19\t9.18123264399873e-20\n-m/z = 100.25\t1.35845271585812e-19\t5.53326809669569e-20\n-m/z = 100.33\t2.55493158947028e-19\t1.19898033660565e-19\n-m/z = 100.42\t-5.89512410534757e-19\t-2.54431413480916e-19\n-m/z = 100.5\t-3.76372491720688e-19\t-1.80768937023138e-19\n-m/z = 100.58\t0.000104656106492354\t0.000353453375099746\n-m/z = 100.67\t0.00191806412960806\t0.00647784695026133\n-m/z = 100.75\t0.00180844223665619\t0.00610762270489929\n-m/z = 100.83\t-0.000360645067163729\t0.00196796460781247\n-m/z = 100.92\t-0.0149012861337974\t0.00227595239712745\n-m/z = 101\t-0.0175435591487301\t-0.0143023652391707\n-m/z = 101.08\t-0.031002747135816\t0.0122614203884685\n-m/z = 101.17\t-0.0338611449231094\t0.022335616247447\n-m/z = 101.25\t-0.00673689843629034\t0.00469328645894397\n-m/z = 101.33\t6.07248591968206e-06\t-7.6520641561839e-07\n-m/z = 101.42\t8.72856050015399e-09\t2.94788261464797e-08\n-m/z = 101.5\t-3.45834782551519e-07\t-1.20261345070234e-06\n-m/z = 101.58\t-3.23622451834655e-07\t-1.12537180515447e-06\n-m/z = 101.67\t-3.86117478477075e-06\t2.3794105462403e-06\n-m/z = 101.75\t-1.16030815032241e-06\t8.04388710714894e-07\n-m/z = 101.83\t-1.00752913826445e-07\t7.12295619842774e-08\n-m/z = 101.92\t1.83947813614356e-12\t6.21243674223187e-12\n-m/z = 102\t4.17262719983059e-14\t1.40919510071919e-13\n-m/z = 102.08\t-3.30267000485262e-11\t-1.14847769765773e-10\n-m/z = 102.17\t-2.71286069382752e-10\t1.38524371315797e-10\n-m/z = 102.25\t-1.61761088045922e-10\t1.07254854925938e-10\n-m/z = 102.33\t-2.76943735241003e-11\t1.93156957835288e-11\n-m/z = 102.42\t2.93026559036466e-16\t9.87918328559747e-16\n-m/z = 102.5\t3.4228312425446e-17\t1.14780782568051e-16\n-m/z = 102.58\t-2.02925048363114e-15\t-7.05361480783726e-15\n-m/z = 102.67\t-1.46134012093517e-15\t-5.08026120921935e-15\n-m/z = 102.75\t-1.81351056413504e-14\t1.13411451361179e-14\n-m/z = 102.83\t-4.99572017792383e-15\t3.46475279601262e-15\n-m/z = 102.92\t-3.55384370930201e-16\t2.51994535337172e-16\n-m/z = 103\t-4.47102391901371e-19\t-1.5617780253041e-19\n-m/z = 103.08\t5.11153177307495e-21\t2.47645614606643e-21\n-m/z = 103.17\t-4.66727985149933e-19\t-6.70989719323969e-19\n-m/z = 103.25\t-1.86969003318006e-18\t5.9532620693441e-19\n-m/z = 103.33\t-4.22200779491765e-19\t6.08695244185644e-19\n-m/z = 103.42\t4.79067948375503e-19\t3.42006550046159e-19\n-m/z = 103.5\t-3.92661474541954e-20\t-1.84050083420459e-20\n-m/z = 103.58\t-2.35057680577223e-20\t-1.08806033188615e-20\n-m/z = 103.67\t-1.43855477737791e-20\t-6.0328403288912e-21\n-m/z = 103.75\t5.51960663177684e-19\t2.57870664062437e-19\n-m/z = 103.83\t-2.32241772088309e-19\t-1.03114965950764e-19\n-m/z = 103.92\t-2.18487059126972e-19\t-1.05762775130515e-19\n-m/z = 104\t-2.01550008775819e-19\t-7.9141851697887e-20\n-m/z = 104.08\t3.14302523844468e-19\t1.49177401279148e-19\n-m/z = 104.17\t-4.32856643756388e-19\t-2.0011054908847e-19\n-m/z = 104.25\t4.02776339479584e-20\t1.77059910956213e-20\n-m/z = 104.33\t1.0540483624956e-18\t4.44512550315165e-19\n-m/z = 104.42\t2.5070723673165e-19\t1.03268109070773e-19\n-m/z = 104.5\t1.2049470217386e-19\t5.35414203407313e-20\n-m/z = 104.58\t1.5752654332008e-19\t6.83050582553353e-20\n-m/z = 104.67\t1.58020498686431e-19\t6.75491625977878e-20\n-m/z = 104.75\t4.56404574522317e-19\t2.05971517123876e-19\n-m/z = 104.83\t8.67160389711655e-19\t4.14963981855326e-19\n-m/z = 104.92\t-2.38205478044096e-19\t-9.63148288416525e-20\n-m/z = 105\t-2.3530005857964e-19\t-9.27796527740569e-20\n-m/z = 105.08\t-9.97029839342495e-20\t-4.12546669299762e-20\n-m/z = 105.17\t5.74367056694911e-19\t2.67065567400866e-19\n-m/z = 105.25\t-1.29232431583207e-19\t-5.75304715817354e-20\n-m/z = 105.33\t5.31209671111868e-19\t2.73463230250976e-19\n-m/z = 105.42\t-5.32973676461191e-20\t-2.29799379188203e-20\n-m/z = 105.5\t-5.13082723789061e-19\t-2.15752868090591e-19\n-m/z = 105.58\t-9.01881370562124e-20\t-4.041850223932e-20\n-m/z = 105.67\t-1.20470986513362e-19\t-5.26404980709873e-20\n-m/z = 105.75\t-4.01240834035114e-19\t-1.68831938839825e-19\n-m/z = 105.83\t-1.4029722650303e-19\t-6.39145162439972e-20\n-m/z = 105.92\t-3.01911798446646e-19\t-'..b'58863748e-07\t4.09983128265751e-07\n+m/z = 794.08\t-1.02067038583969e-07\t1.45258975022716e-07\n+m/z = 794.17\t-1.12379272174218e-08\t1.59935059497897e-08\n+m/z = 794.25\t-9.14897446176839e-19\t2.79442518637409e-19\n+m/z = 794.33\t3.14430201781195e-19\t-9.01707592387211e-20\n+m/z = 794.42\t3.05737321963616e-19\t-8.64979130087255e-20\n+m/z = 794.5\t7.25668962606243e-19\t-1.99460357863971e-19\n+m/z = 794.58\t-0.00477541680078601\t0.00754050838457741\n+m/z = 794.67\t0.00457278398695884\t0.0426132474217618\n+m/z = 794.75\t0.00833966396316633\t0.0346155215563305\n+m/z = 794.83\t0.000966644754810582\t0.00353481494848338\n+m/z = 794.92\t-0.00195096214440755\t-0.00658284942421849\n+m/z = 795\t-0.000640470913415037\t-0.00216305205292812\n+m/z = 795.08\t-5.99410062227032e-05\t-0.000202437790458373\n+m/z = 795.17\t-5.40140143889903e-07\t7.68712453672356e-07\n+m/z = 795.25\t7.01767360337832e-07\t3.35591597300961e-06\n+m/z = 795.33\t3.1879143954593e-07\t1.2061242591254e-06\n+m/z = 795.42\t-8.91159484674204e-08\t-2.96886191630526e-07\n+m/z = 795.5\t-8.7318875310921e-08\t-2.94900624758114e-07\n+m/z = 795.58\t-1.56512048723644e-08\t-5.28585609808716e-08\n+m/z = 795.67\t-3.01232744549964e-11\t4.28706074002659e-11\n+m/z = 795.75\t1.48510045852771e-11\t1.72677972014392e-10\n+m/z = 795.83\t3.85060912854192e-11\t1.57275898252204e-10\n+m/z = 795.92\t9.04270342497724e-12\t3.14452968238541e-11\n+m/z = 796\t-9.44016227008626e-12\t-3.18744881001784e-11\n+m/z = 796.08\t-2.76488452142864e-12\t-9.3377983532092e-12\n+m/z = 796.17\t-2.15725251513134e-13\t-7.28562542807839e-13\n+m/z = 796.25\t-2.5819557100454e-15\t3.67486691814698e-15\n+m/z = 796.33\t3.4957558119255e-15\t1.61265816774081e-14\n+m/z = 796.42\t1.40577254278782e-15\t5.24735981776376e-15\n+m/z = 796.5\t-5.43282595708674e-16\t-1.8177935360719e-15\n+m/z = 796.58\t-3.88205508093361e-16\t-1.3202355886368e-15\n+m/z = 796.67\t-0.0111966558774574\t0.00184642752951255\n+m/z = 796.75\t-0.00616492950080405\t0.00101665137094264\n+m/z = 796.83\t-0.000947322559573781\t0.000156221864141345\n+m/z = 796.92\t1.28806414466129e-18\t3.78213107878212e-19\n+m/z = 797\t1.28891644311995e-18\t-2.29340557339426e-19\n+m/z = 797.08\t-3.42011779893266e-19\t-5.89109479619659e-20\n+m/z = 797.17\t-6.18529780127072e-08\t1.02001028373739e-08\n+m/z = 797.25\t-7.22222614327437e-07\t1.19100893349472e-07\n+m/z = 797.33\t-1.81894222729326e-07\t2.99959652222182e-08\n+m/z = 797.42\t-9.21039873392769e-09\t1.51887616822272e-09\n+m/z = 797.5\t-3.83621340351476e-19\t1.18300980037402e-19\n+m/z = 797.58\t1.60199028478992e-19\t-5.14517985099815e-20\n+m/z = 797.67\t-2.39118937839286e-18\t7.03947681810223e-19\n+m/z = 797.75\t-6.19645980232478e-11\t1.02185101511102e-11\n+m/z = 797.83\t-2.72928138341956e-11\t4.50082615251368e-12\n+m/z = 797.92\t-0.00389097733292416\t0.00553753088718729\n+m/z = 798\t-0.00407514724498002\t0.00579963639658733\n+m/z = 798.08\t-0.000963732393772136\t0.00137155718100157\n+m/z = 798.17\t-3.59243592475195e-05\t5.11265505001434e-05\n+m/z = 798.25\t-1.58445964766381e-15\t2.61264851538924e-16\n+m/z = 798.33\t-3.32644439268362e-15\t5.48369855718841e-16\n+m/z = 798.42\t-7.69555521447935e-16\t1.26753510587904e-16\n+m/z = 798.5\t-3.69626374555642e-07\t5.26041992106527e-07\n+m/z = 798.58\t5.18525840962868e-05\t0.000407429722098967\n+m/z = 798.67\t0.00374645335939357\t0.0293383545235385\n+m/z = 798.75\t0.00291920523455585\t0.0228600645485005\n+m/z = 798.83\t0.000533036946002743\t0.00417417002686769\n+m/z = 798.92\t-1.85955649914773e-18\t5.1306812242025e-19\n+m/z = 799\t-1.39504201852358e-11\t1.98538512164551e-11\n+m/z = 799.08\t-1.86620414523111e-11\t2.65592998689507e-11\n+m/z = 799.17\t-4.06033385043022e-12\t5.77855696599996e-12\n+m/z = 799.25\t3.150163008892e-07\t2.46686834032124e-06\n+m/z = 799.33\t9.32606479034932e-08\t7.30316733349219e-07\n+m/z = 799.42\t7.585751720315e-09\t5.94034197795895e-08\n+m/z = 799.5\t-1.2069382594889e-18\t3.55975224276118e-19\n+m/z = 799.58\t0.00015220686092269\t0.00119191985005999\n+m/z = 799.67\t0.00174853153576809\t0.0136926117080105\n+m/z = 799.75\t0.00121053975241959\t0.00947964074305651\n+m/z = 799.83\t0.000239952374183484\t0.00187904800165069\n+m/z = 799.92\t8.69070160775984e-20\t-2.56200551241428e-20\n' |
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| diff -r 830c6df59603 -r aec189b0c64d test-data/pca_imzml.pdf |
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| Binary file test-data/pca_imzml.pdf has changed |
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| diff -r 830c6df59603 -r aec189b0c64d test-data/preprocessed.RData |
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| diff -r 830c6df59603 -r aec189b0c64d test-data/preprocessing_results1.RData |
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| diff -r 830c6df59603 -r aec189b0c64d test-data/scores_pca.tabular --- a/test-data/scores_pca.tabular Mon Apr 23 17:19:53 2018 -0400 +++ b/test-data/scores_pca.tabular Mon May 28 12:39:28 2018 -0400 |
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| @@ -1,10 +1,10 @@ PC1 PC2 -x = 1, y = 1 3.10354953946922 0.882256622295189 -x = 2, y = 1 -0.896503112130735 -2.9857544218837 -x = 3, y = 1 1.57899570254884 2.26797345312228 -x = 1, y = 2 -4.349387430717 -6.43242810521545 -x = 2, y = 2 4.25927282262866 -0.298723132409806 -x = 3, y = 2 3.62418135901873 0.627868439327952 -x = 1, y = 3 3.2784007788516 -1.9843074587809 -x = 2, y = 3 1.38097137864786 4.74863282848964 -x = 3, y = 3 -11.9794810383172 3.17448177505481 +x = 1, y = 1 -3.10354953946923 -0.882256622295125 +x = 2, y = 1 0.896503112130741 2.98575442188364 +x = 3, y = 1 -1.57899570254885 -2.26797345312224 +x = 1, y = 2 4.34938743071702 6.43242810521546 +x = 2, y = 2 -4.25927282262867 0.298723132409785 +x = 3, y = 2 -3.62418135901873 -0.627868439327959 +x = 1, y = 3 -3.2784007788516 1.98430745878089 +x = 2, y = 3 -1.38097137864788 -4.74863282848961 +x = 3, y = 3 11.9794810383172 -3.17448177505488 |
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| diff -r 830c6df59603 -r aec189b0c64d test-data/toplabels_skm.tabular --- a/test-data/toplabels_skm.tabular Mon Apr 23 17:19:53 2018 -0400 +++ b/test-data/toplabels_skm.tabular Mon May 28 12:39:28 2018 -0400 |
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| b'@@ -1,501 +1,21 @@\n \tmz\tr\tk\tcluster\tcenters\twithinss\tbetweenss\n-1\t1172.25219726562\t1\t3\t1\t146\t0\t18818.2857142857\n-2\t1172.25219726562\t1\t3\t2\t0.571428571428571\t3.71428571428571\t18818.2857142857\n-3\t1172.25219726562\t1\t3\t3\t0\t0\t18818.2857142857\n-4\t1172.25219726562\t2\t3\t1\t146\t0\t18818.2857142857\n-5\t1172.25219726562\t2\t3\t2\t0.571428571428571\t3.71428571428571\t18818.2857142857\n-6\t1172.25219726562\t2\t3\t3\t0\t0\t18818.2857142857\n-7\t1172.25219726562\t3\t3\t1\t146\t0\t18818.2857142857\n-8\t1172.25219726562\t3\t3\t2\t0.571428571428571\t3.71428571428571\t18818.2857142857\n-9\t1172.25219726562\t3\t3\t3\t0\t0\t18818.2857142857\n-10\t1172.25219726562\t1\t2\t1\t146\t0\t18818\n-11\t1172.25219726562\t1\t2\t2\t0.5\t4\t18818\n-12\t1172.25219726562\t2\t2\t1\t146\t0\t18818\n-13\t1172.25219726562\t2\t2\t2\t0.5\t4\t18818\n-14\t1172.25219726562\t3\t2\t1\t146\t0\t18818\n-15\t1172.25219726562\t3\t2\t2\t0.5\t4\t18818\n-16\t1172.27258300781\t1\t3\t1\t118\t0\t12272.5079365079\n-17\t1172.27258300781\t1\t3\t2\t0.428571428571429\t1.71428571428571\t12272.5079365079\n-18\t1172.27258300781\t1\t3\t3\t1\t0\t12272.5079365079\n-19\t1172.27258300781\t2\t3\t1\t118\t0\t12272.5079365079\n-20\t1172.27258300781\t2\t3\t2\t0.428571428571429\t1.71428571428571\t12272.5079365079\n-21\t1172.27258300781\t2\t3\t3\t1\t0\t12272.5079365079\n-22\t1172.27258300781\t3\t3\t1\t118\t0\t12272.5079365079\n-23\t1172.27258300781\t3\t3\t2\t0.428571428571429\t1.71428571428571\t12272.5079365079\n-24\t1172.27258300781\t3\t3\t3\t1\t0\t12272.5079365079\n-25\t1172.27258300781\t1\t2\t1\t118\t0\t12272.2222222222\n-26\t1172.27258300781\t1\t2\t2\t0.5\t2\t12272.2222222222\n-27\t1172.27258300781\t2\t2\t1\t118\t0\t12272.2222222222\n-28\t1172.27258300781\t2\t2\t2\t0.5\t2\t12272.2222222222\n-29\t1172.27258300781\t3\t2\t1\t118\t0\t12272.2222222222\n-30\t1172.27258300781\t3\t2\t2\t0.5\t2\t12272.2222222222\n-31\t1172.23168945312\t1\t3\t1\t111\t0\t10804.5714285714\n-32\t1172.23168945312\t1\t3\t2\t0.714285714285714\t3.42857142857143\t10804.5714285714\n-33\t1172.23168945312\t1\t3\t3\t1\t0\t10804.5714285714\n-34\t1172.23168945312\t2\t3\t1\t111\t0\t10804.5714285714\n-35\t1172.23168945312\t2\t3\t2\t0.714285714285714\t3.42857142857143\t10804.5714285714\n-36\t1172.23168945312\t2\t3\t3\t1\t0\t10804.5714285714\n-37\t1172.23168945312\t3\t3\t1\t111\t0\t10804.5714285714\n-38\t1172.23168945312\t3\t3\t2\t0.714285714285714\t3.42857142857143\t10804.5714285714\n-39\t1172.23168945312\t3\t3\t3\t1\t0\t10804.5714285714\n-40\t1172.23168945312\t1\t2\t1\t111\t0\t10804.5\n-41\t1172.23168945312\t1\t2\t2\t0.75\t3.5\t10804.5\n-42\t1172.23168945312\t2\t2\t1\t111\t0\t10804.5\n-43\t1172.23168945312\t2\t2\t2\t0.75\t3.5\t10804.5\n-44\t1172.23168945312\t3\t2\t1\t111\t0\t10804.5\n-45\t1172.23168945312\t3\t2\t2\t0.75\t3.5\t10804.5\n-46\t1526.24731445312\t1\t3\t1\t103\t0\t9361.84126984127\n-47\t1526.24731445312\t1\t3\t2\t0.428571428571429\t1.71428571428571\t9361.84126984127\n-48\t1526.24731445312\t1\t3\t3\t0\t0\t9361.84126984127\n-49\t1526.24731445312\t2\t3\t1\t103\t0\t9361.84126984127\n-50\t1526.24731445312\t2\t3\t2\t0.428571428571429\t1.71428571428571\t9361.84126984127\n-51\t1526.24731445312\t2\t3\t3\t0\t0\t9361.84126984127\n-52\t1526.24731445312\t3\t3\t1\t103\t0\t9361.84126984127\n-53\t1526.24731445312\t3\t3\t2\t0.428571428571429\t1.71428571428571\t9361.84126984127\n-54\t1526.24731445312\t3\t3\t3\t0\t0\t9361.84126984127\n-55\t1526.24731445312\t1\t2\t1\t103\t0\t9361.68055555556\n-56\t1526.24731445312\t1\t2\t2\t0.375\t1.875\t9361.68055555556\n-57\t1526.24731445312\t2\t2\t1\t103\t0\t9361.68055555556\n-58\t1526.24731445312\t2\t2\t2\t0.375\t1.875\t9361.68055555556\n-59\t1526.24731445312\t3\t2\t1\t103\t0\t9361.68055555556\n-60\t1526.24731445312\t3\t2\t2\t0.375\t1.875\t9361.68055555556\n-61\t1526.27062988281\t1\t3\t1\t95\t0\t8001.14285714286\n-62\t1526.27062988281\t1\t3\t2\t0.142857142857143\t0.857142857142857\t8001.14285714286\n-63\t1526.27062988281\t1\t3\t3\t0\t0\t8001.14285714286\n-64\t1526.27062988281\t2\t3\t1\t95\t0\t8001.14285714286\n-65\t1526.27062988281\t2\t3\t2\t0.142857142857143\t0.857142857142857\t8001.14285714286\n-66\t1526.27062988281\t2\t3\t3\t0\t0\t8001.14285714286\n-67\t1526.27062988281\t3\t3\t1\t95\t0\t8001.14285714286\n-68\t1526.27062988281\t3\t3\t2\t0.142857142857143\t0.857142857142857\t8001.14285714286\n-69\t1526.27062988281\t3\t3\t3\t0\t0\t8001.14285714286\n-70\t1526.27062988281\t1\t2\t1\t95\t0\t8001.125\n-71\t1526.27062988281\t1\t2\t2\t0.125\t0.875\t8001.125\n-72\t1526.27062988281\t2\t2\t1\t95\t0\t8001.125\n-73\t1526.27062988281\t2\t2\t2\t0.125\t0.875\t8001.125\n-74\t1'..b'8\t1172.35437011719\t1\t2\t2\t0.375\t1.875\t496.125\n-449\t1172.333984375\t2\t2\t1\t24\t0\t496.125\n-450\t1172.35437011719\t2\t2\t1\t24\t0\t496.125\n-451\t1172.333984375\t2\t2\t2\t0.375\t3.875\t496.125\n-452\t1172.35437011719\t2\t2\t2\t0.375\t1.875\t496.125\n-453\t1172.333984375\t3\t2\t1\t24\t0\t496.125\n-454\t1172.35437011719\t3\t2\t1\t24\t0\t496.125\n-455\t1172.333984375\t3\t2\t2\t0.375\t3.875\t496.125\n-456\t1172.354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|
| b |
| diff -r 830c6df59603 -r aec189b0c64d test-data/toplabels_ssc.tabular --- a/test-data/toplabels_ssc.tabular Mon Apr 23 17:19:53 2018 -0400 +++ b/test-data/toplabels_ssc.tabular Mon May 28 12:39:28 2018 -0400 |
| b |
| @@ -1,101 +1,51 @@ mz r k s classes centers tstatistics p.values adj.p.values -1 100.083335876465 1 5 2 1 0 0 1 1 -2 100.166664123535 1 5 2 1 0 0 1 1 -3 100.25 1 5 2 1 0 0 1 1 -4 100.333335876465 1 5 2 1 0 0 1 1 -5 100.416664123535 1 5 2 1 0 0 1 1 -6 100.5 1 5 2 1 0 0 1 1 -7 100.583335876465 1 5 2 1 0.0838252420306186 0 1 1 -8 100.666664123535 1 5 2 1 1.53629057379837 0 1 1 -9 100.75 1 5 2 1 1.44848794080804 0 1 1 -10 100.833335876465 1 5 2 1 0.404900097640283 0 1 1 -11 100.916664123535 1 5 2 1 1.34409180258773 0 1 1 -12 101 1 5 2 1 2.48301808582784 0 1 1 -13 101.083335876465 1 5 2 1 5.51024257360121 0 1 1 -14 101.166664123535 1 5 2 1 4.64545287689741 0 1 1 -15 101.25 1 5 2 1 0.932104556777491 0 1 1 -16 101.333335876465 1 5 2 1 0.00218602970233197 0 1 1 -17 101.416664123535 1 5 2 1 6.99121839139404e-06 0 1 1 -18 101.5 1 5 2 1 8.09688810211177e-05 0 1 1 -19 101.583335876465 1 5 2 1 7.57683990170273e-05 0 1 1 -20 101.666664123535 1 5 2 1 0.000504463410492984 0 1 1 -21 101.75 1 5 2 1 0.000156940406498443 0 1 1 -22 101.833335876465 1 5 2 1 1.48881831729453e-05 0 1 1 -23 101.916664123535 1 5 2 1 1.47334559873647e-09 0 1 1 -24 102 1 5 2 1 3.34205011379404e-11 0 1 1 -25 102.083335876465 1 5 2 1 7.7324059472956e-09 0 1 1 -26 102.166664123535 1 5 2 1 3.37431554155137e-08 0 1 1 -27 102.25 1 5 2 1 2.15837910923654e-08 0 1 1 -28 102.333335876465 1 5 2 1 3.85207690078581e-09 0 1 1 -29 102.416664123535 1 5 2 1 2.3423282675646e-13 0 1 1 -30 102.5 1 5 2 1 2.71910474949128e-14 0 1 1 -31 102.583335876465 1 5 2 1 4.74873956449894e-13 0 1 1 -32 102.666664123535 1 5 2 1 3.42028803988351e-13 0 1 1 -33 102.75 1 5 2 1 2.37927883055398e-12 0 1 1 -34 102.833335876465 1 5 2 1 6.77872381076583e-13 0 1 1 -35 102.916664123535 1 5 2 1 5.37881903718124e-14 0 1 1 -36 103 1 5 2 1 6.16874827340699e-18 0 1 1 -37 103.083335876465 1 5 2 1 1.41437086538825e-20 0 1 1 -38 103.166664123535 1 5 2 1 3.67140477964248e-17 0 1 1 -39 103.25 1 5 2 1 1.79518196370108e-16 0 1 1 -40 103.333335876465 1 5 2 1 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