Error getting MMs by the end of the correction

[juanbot]

This is my code. I detect all astrocyte related signals, and put them to correct a TAU based samples network. Everything seems fine but see below

############# secNetwork = function(type,enrichment="Astro"){

expr.data = selectData(type)

if(type == "TAU") net = readRDS("~/Dropbox/collab/dervis/bootstrap/netBootTAU_boot.19.it.30.b.30.rds") cts = rownames(net$ct) print(cts) detected = cts[grepl(enrichment,cts)] detected = c(detected,cts[grepl(toupper(enrichment),cts)]) detected = c(detected,cts[grepl(tolower(enrichment),cts)]) detected = unique(detected) removePrimaryEffect(expr.data = selectData(type), markers.path="~/Dropbox/KCL/workspace/CoExpNets/inst/", target.enrichment = detected, net = net ) } ############

This is the output from console

secondary = secNetwork("TAU") [1] "Astrocyte" "Bcells"
[3] "CA1_Pyramidal" "CA1.Pyramidal"
[5] "CD8Tcells" "Cerebellum_Basket_Neuroexpresso"
[7] "Cerebellum_Bergmann_Neuroexpresso" "Cerebellum_Golgi_Neuroexpresso"
[9] "Cerebellum_Granule_Neuroexpresso" "Cerebellum_Microglia_Neuroexpresso"
[11] "Cerebellum_MicrogliaActivation_Neuroexpresso" "Cerebellum_MicrogliaDeactivation_Neuroexpresso" [13] "Cerebellum_Oligo_Neuroexpresso" "Cerebellum_Purkinje_Neuroexpresso"
[15] "Cortex_Astrocyte_Neuroexpresso" "Cortex_Endothelial_Neuroexpresso"
[17] "Cortex_GabaPV_Neuroexpresso" "Cortex_GabaRelnCalb_Neuroexpresso"
[19] "Cortex_GabaVIPReln_Neuroexpresso" "Cortex_Microglia_Neuroexpresso"
[21] "Cortex_MicrogliaActivation_Neuroexpresso" "Cortex_MicrogliaDeactivation_Neuroexpresso"
[23] "Cortex_Oligo_Neuroexpresso" "Cortex_OligoPrecursors_Neuroexpresso"
[25] "Cortex_Pyramidal_Neuroexpresso" "Cytotoxiccells"
[27] "D_NEuronCom1" "D_NEuronCom2"
[29] "D_NEuronCom3" "D_NEuronCom4"
[31] "D_NEuronCom5" "D_NEuronCom6"
[33] "D_NEuronCom7" "DAMANIS_MICROGLIA"
[35] "DARMANIS_ASTROCYES" "DARMANIS_ENDOTHELIAL"
[37] "DARMANIS_FETAL1" "DARMANIS_FETAL2"
[39] "DARMANIS_MIXTURENEUASTRO" "DARMANIS_MIXTURENEUOLIGOPC"
[41] "DARMANIS_NEUROND" "DARMANIS_OLIGO"
[43] "DARMANIS_OPCS" "Dopaminergic_Neuron_SN"
[45] "Dopaminergic_Neuron_VTA" "Dopaminergic_Neuron"
[47] "Dopaminergic" "DopaminergicNeuron"
[49] "Endothelial" "Eosinophils"
[51] "Ependymal" "Ex1"
[53] "Ex2" "Ex3"
[55] "Ex4" "Ex5"
[57] "Ex6" "Ex7"
[59] "Ex8" "GABASST_HIPPD_Neuroexpresso"
[61] "Hippocampus_Astrocyte_Neuroexpresso" "Hippocampus_DentateGranule_Neuroexpresso"
[63] "Hippocampus_GabaSSTRein_Neuroexpresso" "Hippocampus_MicrogliaActivation_Neuroexpresso"
[65] "Hippocampus_MicrogliaDeactivation_Neuroexpresso" "Hippocampus_Oligo_Neuroexpresso"
[67] "Hippocampus_Pyramidal_Neuroexpresso" "Hyppocampus_Microglia_Neuroexpresso"
[69] "iDC" "In1"
[71] "In2" "In3"
[73] "In4" "In5"
[75] "In6" "In7"
[77] "In8" "Interneuron"
[79] "Macrophages" "Mastcells"
[81] "Microglia" "Mural"
[83] "NaturalKillers" "Neuroexpress0_Cortex_Layer.2.3.Pyra"
[85] "Neuroexpresso_Cortex_Laye.6aPyra" "Neuroexpresso_Cortex_Layer4Pyra"
[87] "Neuroexpresso_CortexD_Endothelial" "Neuroexpresso_CortexD_GabaPV"
[89] "Neuroexpresso_CortexD_GabaRelnCalb" "Neuroexpresso_CortexD_GabaVIPReln"
[91] "Neuroexpresso_CortexD_Layer6bPyra" "Neuroexpresso_CortexD_Pyramidal_Glt_25d2"
[93] "Neuroexpresso_CortexD_Pyramidal_S100a10" "Neuroexpresso_CortexD_PyramidalCorticoThalam"
[95] "Neuron_Dopaminergic_SNigra" "Neuron_Dopaminergic_VTA"
[97] "Neuron_Dopaminergic" "Neuron_Interneuron"
[99] "Neuron_Interneuton" "Neuron_Pyramidal_CA1"
[101] "Neuron_Pyramidal_S1" "Neuron.Ex1"
[103] "Neuron.Ex2" "Neuron.Ex3"
[105] "Neuron.Ex4" "Neuron.Ex5"
[107] "Neuron.Ex6" "Neuron.Ex7"
[109] "Neuron.Ex8" "Neuron.In1"
[111] "Neuron.In2" "Neuron.In3"
[113] "Neuron.In4" "Neuron.In5"
[115] "Neuron.In6" "Neuron.In7"
[117] "Neuron.In8" "Neutrophils"
[119] "Oligodendrocyte" "Pyramidal_CA1"
[121] "Pyramidal_S1" "PyramidalThy_HIPPD_Neuroexpresso"
[123] "S1_Pyramidal" "S1.Pyramidal"
[125] "SN_Dopaminergic" "SN_DopaminergicNeuron"
[127] "Tcells" "Tcm"
[129] "Tem" "Tfh"
[131] "Th1cells" "Th2cells"
[133] "Thelpercells" "VTA_Dopaminergic"
[135] "VTA_DopaminergicNeuron"
[1] 14 19328 Read 20 items Read 20 items Read 240 items Read 40 items Read 409 items Read 409 items Read 40 items Read 21 items Read 150 items Read 148 items Read 150 items Read 150 items Read 150 items Read 353 items Read 40 items Read 484 items Read 85 items Read 119 items Read 21 items Read 25 items Read 42 items Read 61 items Read 79 items Read 91 items Read 32 items Read 45 items Read 58 items Read 47 items Read 18 items Read 40 items Read 24 items Read 20 items Read 5 items Read 365 items Read 40 items Read 40 items Read 436 items Read 155 items Read 41 items Read 150 items Read 148 items Read 150 items Read 365 items Read 365 items Read 409 items Read 294 items Read 85 items Read 119 items Read 21 items Read 25 items Read 42 items Read 61 items Read 79 items Read 91 items Read 45 items Read 58 items Read 47 items Read 18 items Read 40 items Read 24 items Read 20 items Read 5 items Read 40 items Read 453 items Read 409 items Read 294 items Read 294 items Read 294 items Read 150 items Read 150 items Read 24 items Read 40 items Read 16 items Read 40 items Read 29 items Read 32 items Read 24 items Read 148 items Read 148 items Read 13 items Read 14 items Read 14 items Read 63194 items Read 17629 items Read 7 items Read 18 items Read 18 items Read 20 items Read 20 items Read 9 items Read 12297 items 912 comparisons were successfully performed. Power SFT.R.sq slope truncated.R.sq mean.k. median.k. max.k. Density Centralization Heterogeneity 1 1 0.216000 8.0100 0.895 9820.0 9830.0 10800 0.50800 0.0514 0.0465 2 2 0.145000 3.1200 0.940 5690.0 5670.0 6990 0.29500 0.0669 0.0913 3 3 0.046500 1.0000 0.951 3620.0 3580.0 5050 0.18700 0.0741 0.1490 4 4 0.000359 -0.0567 0.956 2460.0 2410.0 3900 0.12700 0.0747 0.2150 5 5 0.064600 -0.5630 0.947 1760.0 1710.0 3140 0.09090 0.0714 0.2830 6 6 0.162000 -0.7180 0.928 1310.0 1260.0 2590 0.06760 0.0667 0.3530 7 7 0.276000 -0.8240 0.884 1000.0 945.0 2190 0.05190 0.0615 0.4210 8 8 0.431000 -0.8240 0.935 789.0 726.0 1880 0.04080 0.0564 0.4870 9 9 0.603000 -0.8490 0.971 635.0 567.0 1630 0.03290 0.0517 0.5520 10 10 0.726000 -0.9790 0.980 521.0 449.0 1480 0.02690 0.0498 0.6160 11 11 0.790000 -1.1000 0.987 433.0 361.0 1370 0.02240 0.0486 0.6780 12 12 0.839000 -1.1800 0.991 365.0 294.0 1280 0.01890 0.0474 0.7390 13 13 0.871000 -1.2600 0.989 312.0 241.0 1200 0.01610 0.0460 0.7990 14 14 0.892000 -1.3100 0.986 269.0 199.0 1130 0.01390 0.0446 0.8580 15 15 0.912000 -1.3500 0.985 234.0 166.0 1070 0.01210 0.0433 0.9150 16 16 0.921000 -1.3800 0.979 205.0 140.0 1020 0.01060 0.0420 0.9720 17 17 0.931000 -1.4100 0.976 181.0 118.0 969 0.00938 0.0407 1.0300 18 18 0.941000 -1.4200 0.975 161.0 100.0 925 0.00834 0.0395 1.0800 19 19 0.946000 -1.4400 0.971 144.0 85.8 886 0.00746 0.0384 1.1400 20 20 0.951000 -1.4500 0.969 130.0 73.5 850 0.00671 0.0373 1.1900 21 21 0.954000 -1.4600 0.966 117.0 63.2 817 0.00606 0.0362 1.2500 22 22 0.957000 -1.4600 0.964 106.0 54.5 786 0.00550 0.0352 1.3000 23 23 0.961000 -1.4700 0.965 97.0 47.4 758 0.00502 0.0342 1.3500 24 24 0.964000 -1.4700 0.965 88.8 41.3 732 0.00459 0.0333 1.4000 25 25 0.966000 -1.4700 0.965 81.5 36.1 708 0.00422 0.0324 1.4600 26 26 0.968000 -1.4700 0.965 75.1 31.6 685 0.00389 0.0316 1.5100 27 27 0.970000 -1.4600 0.967 69.5 27.8 664 0.00359 0.0308 1.5600 28 28 0.971000 -1.4600 0.966 64.4 24.5 644 0.00333 0.0300 1.6100 29 29 0.972000 -1.4500 0.967 59.8 21.7 626 0.00310 0.0293 1.6600 30 30 0.973000 -1.4500 0.966 55.8 19.2 608 0.00289 0.0286 1.7100 The final beta value to use is: 12 [1] "Creating adjacency matrix" [1] "Created" [1] "Creating TOM" ..connectivity.. ..matrix multiplication (system BLAS).. ..normalization.. ..done. [1] "Created" [1] "Deleting adjacency matrix" ..cutHeight not given, setting it to 0.988 ===> 99% of the (truncated) height range in dendro. ..done. [1] "Deleting TOM and dissTOM" [1] "Computing Module Membership" Error in mm[[gene]] <- cor(outnet$MEs[paste0("ME", module)], expr.data[, : more elements supplied than there are to replace In addition: Warning messages: 1: In read.table(file = file, header = header, sep = sep, quote = quote, : incomplete final line found by readTableHeader on '~/dervis/dervisSampleInfo.txt' Show Traceback

Rerun with Debug Error in mm[[gene]] <- cor(outnet$MEs[paste0("ME", module)], expr.data[, : more elements supplied than there are to replace

[juanbot]

I know what happens, it doesn´t work when the gene names in the network are duplicated... And they are here. Should we address that?

length(names(net$moduleColors)) [1] 19328 length(unique(names(net$moduleColors))) [1] 19327

[juanbot]

I just verified that was the reason.