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====Did I miss any gene by over-filtering==== *In the previous analyses, I filtered the raw expression matrix by removing any gene that has >30% missing values. Was that too stringent? What if certain genes express exclusive in a sub-group of neurons? *To answer this question, I repeated the filtering by keeping genes with as high as 70% missing values. ../scripts/filterExprMatrix.pl 2014_1st_Quater_18_expr_matrix.txt ../summary_stats/2014_1st_Quater_18_expr_matrix_1N_excluded_ids.txt > 2014_1st_Quater_18_1N_expr_matrix_filtered_70pct.txt & ../scripts/exprMatrix2overDispersedGenes.pl 2014_1st_Quater_18_1N_expr_matrix_filtered_70pct.txt 1 > 2014_1st_Quater_18_1N_70pct_Z1_overDispersed_genes.txt *This time I obtain 537 over-dispersed genes, slightly less than the number 564 that I got before. 325 genes were shared between the two lists. By eye-balling the genes no present in the previous list, I did find a number of interesting ones. So I decided to analyze this list further. ../scripts/extract_gene_set_ENSG.pl 2014_1st_Quater_18_1N_70pct_Z1_overDispersed_genes.txt 2014_1st_Quater_18_1N_expr_matrix_filtered_70pct.txt > 2014_1st_Quater_18_1N_70pct_Z1_overDispersed_expr_matrix.txt x=read.table("2014_1st_Quater_18_1N_70pct_Z1_overDispersed_expr_matrix.txt",header=TRUE,row.names=1); x.cor = cor(log10(x+1),use="pairwise.complete.obs",method="pearson") hm<-heatmap.2(as.matrix(x.cor), col=bluered(128), scale="none", cexCol=0.1, cexRow=0.1 ,key=T, symkey=F,density.info="histogram",trace="none",dendrogram="both",Rowv=TRUE,Colv=TRUE) [[Image:2014_1st_Quater_18_70pct_Z1_overDispersed_genes_sample_clustering.png|400px]] x[is.na(x)] <- 0 y=log10(x+1) tys = scale(t(y)) k_5=kmeans(tys, 5) k_4=kmeans(tys, 4) k_3=kmeans(tys, 3) k_2=kmeans(tys, 2) plotcluster(tys,k_2$cluster) plotcluster(tys,k_3$cluster) plotcluster(tys,k_4$cluster) plotcluster(tys,k_5$cluster) write.table(k_2$cluster, file="2014_1st_Quater_18_1N_70pct_Z1_overDispersed_k-mean_2_clusters.txt", sep = "\t", row.names=TRUE, col.names=TRUE) [[Image:2014_1st_Quater_18_70pct_over-dispersed-Z1_k2_clusters.png|300px]][[Image:2014_1st_Quater_18_70pct_over-dispersed-Z1_k3_clusters.png|300px]] [[Image:2014_1st_Quater_18_70pct_over-dispersed-Z1_k4_clusters.png|300px]][[Image:2014_1st_Quater_18_70pct_over-dispersed-Z1_k5_clusters.png|300px]] *I repeated the SAM analysis on the two clusters based on K-mean(k=2). Comparison of top-15 up-regulated genes in each cluster. Apparently a few known brain related genes, such as CXCL14, GRIK1 and KCNIP1 showed up in the list when I relaxed the filtering criterion. {| {{table}} | align="center" style="background:#f0f0f0;"|'''<30pct missing''' | align="center" style="background:#f0f0f0;"|''' ''' | align="center" style="background:#f0f0f0;"|'''<70pct missing''' | align="center" style="background:#f0f0f0;"|''' ''' |- | C2_up||C1_up||C2_up||C1_up |- | SLC6A1||TESPA1||SLC6A1||TESPA1 |- | GAD2||ZBTB18||GAD1||EGR1 |- | GAD1||CTC-552D5||GAD2||TTYH1 |- | DGKD||RORB||ADRA1A||ADAMTS16 |- | ATP1B2||ADAMTS3||MAF||COL5A2 |- | ANKRD55||EGR1||CXCL14||PWWP2B |- | NRIP3||COL11A1||ARX||CBLN2 |- | TOX3||SERPINE2||ERBB4||RORB |- | IGF1||TTYH1||GRIK1||FAM19A1 |- | RAB3IP||SLC38A11||RBMS3||CA10 |- | GOLIM4||DYSF||KCNIP1||MLIP |- | TACR1||ADAMTS16||BTBD11||SATB2 |- | LYPD6B||DUSP6||MYO16||PTPRK |- | RASSF8||COL5A2||DNER||GULP1 |- | FREM1||SLC22A24||PTPRM||ZBTB18 |- | |} *Second round analysis of the sub-clusters in C1. ../scripts/extractSampleClusterExprMatrix.pl 2014_1st_Quater_18_1N_expr_matrix_filtered_70pct.txt 2014_1st_Quater_18_1N_70pct_Z1_overDispersed_k-mean_2_clusters.txt 1 > 2014_1st_Quater_18_1N_70pct_expr_matrix_k-mean2_c1.txt ../scripts/exprMatrix2overDispersedGenes.pl 2014_1st_Quater_18_1N_70pct_expr_matrix_k-mean2_c1.txt 1 > 2014_1st_Quater_18_1N_70pct_k-mean2_c1_Z1_overDispersed_genes.txt ../scripts/extract_gene_set_ENSG.pl 2014_1st_Quater_18_1N_70pct_k-mean2_c1_Z1_overDispersed_genes.txt 2014_1st_Quater_18_1N_70pct_expr_matrix_k-mean2_c1.txt > 2014_1st_Quater_18_1N_70pct_k-mean2_c1_Z1_overDispersed_genes_expr_matrix.txt x=read.table("2014_1st_Quater_18_1N_70pct_k-mean2_c1_Z1_overDispersed_genes_expr_matrix.txt",header=TRUE,row.names=1); x.cor = cor(log10(x+1),use="pairwise.complete.obs",method="pearson") hm<-heatmap.2(as.matrix(x.cor), col=bluered(128), scale="none", cexCol=0.1, cexRow=0.1 ,key=T, symkey=F,density.info="histogram",trace="none",dendrogram="both",Rowv=TRUE,Colv=TRUE) #For some reason that I couldn't figure out, K-mean clustering didn't work on this data set. All samples collapsed on two straight lines or 3-5 define spots. So I further divide C1 into four sub-clusters based on tree cutting. ct<- cutree(hc.rows, h=3.8) # rect.hclust(hc.rows, h=3.8) # draw red rectangles to mark the subgroups write.table(ct, file="2014_1st_Quater_18_1N_70pct_c1_Z1_overDispersed_cutree_4_clusters.txt", sep = "\t", row.names=TRUE, col.names=TRUE) [[Image:2014_1st_Quater_18_70pct_over-dispersed-Z1_c1_cutree_4_clusters.png|600px]] *Then I did the SAM analysis on four sub-clusters, and hand-picked the most up-regulated genes for each cluster. {| {{table}} | align="center" style="background:#f0f0f0;"|'''Hugo''' | align="center" style="background:#f0f0f0;"|'''contrast-1''' | align="center" style="background:#f0f0f0;"|'''contrast-2''' | align="center" style="background:#f0f0f0;"|'''contrast-3''' | align="center" style="background:#f0f0f0;"|'''contrast-4''' |- | GULP1||8.425237569||-8.046609482||-3.600322514||-0.557093767 |- | GLIS3||8.343548625||-5.563345218||-1.192143799||-4.861766435 |- | PDZD2||8.24768625||-8.659361328||-9.103894161||5.443518451 |- | GPC6||7.454229827||-5.095298333||-4.376936986||-1.077364549 |- | PTPRK||7.444544177||-6.183656599||2.248062283||-6.460351566 |- | CUX2||7.410971043||-8.776620023||-7.187091414||4.790494964 |- | ST6GALNAC5||7.386061028||-4.663803517||-2.542527965||-3.106939335 |- | DGKB||7.370751451||-8.571181072||-8.594561568||6.010402456 |- | HS6ST3||7.27057172||-8.107257569||-7.610239863||4.805509098 |- | ADAMTS3||7.076801907||-8.42261044||-4.098819873||1.972586834 |- | |} {| {{table}} | align="center" style="background:#f0f0f0;"|'''Hugo''' | align="center" style="background:#f0f0f0;"|'''contrast-1''' | align="center" style="background:#f0f0f0;"|'''contrast-2''' | align="center" style="background:#f0f0f0;"|'''contrast-3''' | align="center" style="background:#f0f0f0;"|'''contrast-4''' |- | SLC35F1||-6.472144655||8.272579443||6.147406638||-4.577265042 |- | PTCHD4||-5.502784325||7.796328744||0.786530817||-0.303972278 |- | FOXP2||-3.860427522||6.951464815||6.996421917||-7.622723144 |- | FSTL5||-6.125676731||6.820074944||5.252446902||-2.93360098 |- | VWC2L||-4.266060283||6.501114113||4.461321639||-4.299193058 |- | GRIP1||-7.256540308||6.344769187||4.881292263||-0.720788007 |- | FNBP1L||-5.068946724||6.250833375||4.557613564||-3.145588857 |- | MYRIP||-1.97257783||6.110724268||5.336333823||-7.751749713 |- | SLC44A5||-2.444135501||6.071122614||5.030114017||-6.819412197 |- | KLHL5||-4.988223503||5.894461437||2.629056693||-1.106223833 |- | |} {| {{table}} | align="center" style="background:#f0f0f0;"|'''Hugo''' | align="center" style="background:#f0f0f0;"|'''contrast-1''' | align="center" style="background:#f0f0f0;"|'''contrast-2''' | align="center" style="background:#f0f0f0;"|'''contrast-3''' | align="center" style="background:#f0f0f0;"|'''contrast-4''' |- | RXFP1||-2.585159705||4.851245621||8.81861737||-9.215244171 |- | CLSTN2||-2.882489285||3.539838122||8.210887065||-7.137631463 |- | NRG1||0.581508264||1.277217783||8.141319499||-9.602031634 |- | DCC||-1.338380764||2.699355287||7.937058101||-8.147450676 |- | IL1RAPL2||-5.207244176||3.833199619||7.741449575||-3.949301642 |- | CADPS2||2.1536228||0.248433806||7.30501552||-9.951046312 |- | CPNE4||-0.205159683||3.640818046||7.221434895||-9.722838787 |- | POU6F2||0.98733985||3.616163654||7.220286284||-11.23448076 |- | RORB||0.243334211||1.93511208||7.055460544||-8.6905897 |- | FOXP2||-3.860427522||6.951464815||6.996421917||-7.622723144 |- | |} {| {{table}} | align="center" style="background:#f0f0f0;"|'''Hugo''' | align="center" style="background:#f0f0f0;"|'''contrast-1''' | align="center" style="background:#f0f0f0;"|'''contrast-2''' | align="center" style="background:#f0f0f0;"|'''contrast-3''' | align="center" style="background:#f0f0f0;"|'''contrast-4''' |- | GNAL||2.152713882||-4.204862434||-8.66612006||9.076171648 |- | GRIA4||3.222367558||-4.333159768||-9.315812969||8.429467979 |- | MRC1L1||-2.557892185||-3.230970989||-1.86243514||7.814728707 |- | RGS12||-2.103405117||-2.204978885||-3.28629215||7.716132243 |- | RPS6KA2||-3.299256284||1.582562861||-4.970998816||7.640257898 |- | THSD7A||0.418925677||-2.269664451||-6.47728784||7.571726232 |- | HPCAL1||1.004808105||-2.915489262||-6.670063124||7.551415141 |- | SLCO2A1||2.230426314||-5.854172552||-5.600667599||7.454133784 |- | ANKRD30BL||-2.887431523||-1.875881406||-2.240940892||7.447192944 |- | GRIK4||1.879214978||-5.679658971||-5.193369988||7.368797978 |- | |} *Take the top up-regulated genes for all groups, and generate a expression matrix for all samples. Hierarchical clustering based on these genes showed a very clear separate of the five groups. A small subset of genes can be selected to "label" these groups. Next we need to validate the in situ expression patterns of these gens by RNA FISH. [[Image:2014_1st_Quater_18_1N_70pct_C1_cluster_specific_genes_expr_heatmap.png|800px]]
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