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1. S100A7

This document outlines the analysis of gene expression data for several genes, including S100A7, SPRR2A, UGT1A10, and KLK12, utilizing MATLAB code. The process involves reading coverage data, normalizing it, conducting Principal Component Analysis (PCA), and performing k-means clustering to classify data into groups. The code implements statistical measures to manage missing data and assess variations among the genes. Results are visualized to highlight significant expressions and coverage levels across the analyzed samples.

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1. S100A7

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  1. 1. S100A7

  2. 1. S100A7 110624_UNC11-SN627_0111_BD0CT8ABXX_4_ 110223_UNC9-SN296_0154_B81LPPABXX_6_ scale = 0-461 blue scale = 0-30 red chr1:153,430,220-153,433,137

  3. 2. SPRR2A

  4. 2. SPRR2A 110630_UNC11-SN627_0112_AD0CVJABXX_8_ 110308_UNC9-SN296_0157_AB05N2ABXX_5_ scale = 0-12507 red scale = 0-309 blue chr1:153,028,596-153,029,988

  5. 3. UGT1A10

  6. 3. UGT1A10 UGT1A10 exons 110218_SN254_0194_B81D2DABXX_5_ 110427_UNC11-SN627_0080_AC016RABXX_5_ scale = 0-973 blue scale = 0-892 red chr2:234,526,291-234,681,945

  7. 8. KLK12 previously 159/195

  8. 8. KLK12 110624_UNC14-SN744_0134_AD0CVTABXX_3_ 110214_SN627_0059_A81fmwabxx_4_ scale = 0-487 red scale = 0-571 blue chr19:51,532,348-51,538,148

  9. 8. KLK12 110426_UNC10-SN254_0213_BB065FABXX_5_ 110624_UNC11-SN627_0111_BD0CT8ABXX_7_ highest curves scale = 0-5504 red scale = 0-12306 blue chr19:51,532,348-51,538,148

  10. Matlab code (for Patrick) data = textread('/Users/pkimes/Desktop/S100A7_coverage.txt') ; data = data' ; [d,n] = size(data) ; medcov = zeros(n,1) ; percov = zeros(n,1) ; for i = 1:n ; temp = data(:,i) ; tempflag = (temp == 0) ; percov(i) = 1 - sum(tempflag)/d ; medcov(i) = median(temp(~tempflag)) ; end ; medcov(isnan(medcov)) = 0 ; flag = (medcov < 5) | (percov < .10) ; sum(~flag) coverage = sum(data,1) ; datanorm = data(:,~flag) ./ ... vec2matSM(coverage(~flag)+1,d) ... * median(coverage(~flag)) ; logdata = log10(datanorm+1) ; paramstruct = struct('npc',1,... 'iscreenwrite',1,... 'viout',[0 0 0 0 1]) ; outstruct = pcaSM(logdata,paramstruct) ; vpc = outstruct.mpc ; vclass = kmeans(vpc',2,'EmptyAction', ... 'singleton','Replicates',10) ; labSF = logical(vclass-1) ; sum(labSF) sum(~labSF) testn1 = sum(labSF) ; testn2 = sum(~labSF) ; testn0 = n - (testn1+testn2) ; datanorm2 = (data+1) ; logdata2 = log10(datanorm2) ; screen = find(~flag) ; c1 = find(flag) ; c2 = screen(labSF) ; c3 = screen(~labSF) ; medn1 = median(logdata2(:,c1),2) ; medn2 = median(logdata2(:,c2),2) ; medn3 = median(logdata2(:,c3),2) ; fid = fopen('/Users/pkimes/Desktop/samplenames177.txt') ; output = textscan(fid,'%s') ; fclose(fid) ; samples = output{1} ; sdiff1 = sum(abs(logdata2(:,c2) - vec2matSM(medn2,length(c2))),1) ; good1 = find(sdiff1 == min(sdiff1)) sdiff2 = sum(abs(logdata2(:,c3) - vec2matSM(medn3,length(c3))),1) ; good2 = find(sdiff2 == min(sdiff2)) figure(1) ; clf ; hold on ; plot(logdata2(:,c1),'-','color',[.4 .4 .4]) ; plot(logdata2(:,c2),'-','color',[1 .3 .3]) ; plot(logdata2(:,c3),'-','color',[.3 .3 1]) ; plot(medn1,'-','color',[0 0 0],'linewidth',3) ; plot(medn2,'-','color',[.6 0 0],'linewidth',3) ; plot(medn3,'-','color',[0 0 .6],'linewidth',3) ; plot(logdata2(:,c2(good1)),'-g','linewidth',5) ; plot(logdata2(:,c3(good2)),'-g','linewidth',5) ; samples(c2(good1)) samples(c3(good2)) area1 = sum(logdata2(:,c2)) ; top1 = find(area1 == max(area1)) ; area2 = sum(logdata2(:,c3)) ; top2 = find(area2 == max(area2)) ;

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