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smorabit / hdWGCNA

High dimensional weighted gene co-expression network analysis
https://smorabit.github.io/hdWGCNA/
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No cell names (colnames) names present in the input matrix #138

Closed olive-yelloweyes closed 9 months ago

olive-yelloweyes commented 9 months ago

Hi, Thank you for your wonderful packages, I was using the function FindDMEs() and it occurred error

image

I checked my colnames and it looks like this [1] "BD1_AAACCTGAGAGCTATA-1" "BD1_AAACCTGAGATGTGTA-1" "BD1_AAACCTGAGCGTCTAT-1" [4] "BD1_AAACCTGAGCTATGCT-1" "BD1_AAACCTGAGGTGCTAG-1" "BD1_AAACCTGAGTGCCATT-1"

it is because mine have sample name in the front to occurred error?

olive-yelloweyes commented 9 months ago
image

also this is my matrix looks like

smorabit commented 9 months ago

Can you please share which hdWGCNA code that you have ran before this step?

olive-yelloweyes commented 9 months ago

Thanks for the quick response！ I ran the whole workflow using your tutorial and then ran the DME,（https://smorabit.github.io/hdWGCNA/articles/basic_tutorial.html） But after I ran the whole workflow I saved it as rds and then I ran DME with the rds.

olive-yelloweyes commented 9 months ago

seurat_obj <- SetupForWGCNA( seurat_obj, gene_select = "fraction", # the gene selection approach fraction = 0.05, # fraction of cells that a gene needs to be expressed in order to be included wgcna_name = "tutorial")

seurat_obj <- MetacellsByGroups( seurat_obj = seurat_obj, group.by = c("celltype", "orig.ident"), # specify the columns in seurat_obj@meta.data to group by reduction = 'harmony', # select the dimensionality reduction to perform KNN on k = 25, # nearest-neighbors parameter max_shared = 10, # maximum number of shared cells between two metacells ident.group = 'celltype' # set the Idents of the metacell seurat object )

seurat_obj <- NormalizeMetacells(seurat_obj)j) Performing log-normalization 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **| seurat_obj <- SetDatExpr( seurat_obj, group_name = "T", group.by='celltype', # the metadata column containing the cell type info. This same column should have also been used in MetacellsByGroups assay = 'RNA', # using RNA assay slot = 'data' # using normalized data ) table(seurat_obj$celltype,seurat_obj$orig.ident)

               BD1  BD2  BD3  BD4  BD5  BD6  BD7 UVQ10 UVQ14 UVQ16 UVQ17

T 5581 7583 6065 4263 4393 6217 2507 3754 7826 6341 987 monocytes 3199 4782 1717 1598 1981 2823 1836 4803 5090 2378 1377 NK 1099 1301 700 1319 1686 893 728 669 920 1049 437 B 1527 1646 2273 871 657 1923 379 712 1026 985 444 DC 51 144 88 96 69 201 43 87 211 74 19 NKT cell 24 51 16 17 22 35 40 47 47 35 24 Progenitor cell 13 37 27 17 52 20 9 75 78 45 34

              UVQ18 UVQ4 UVQ6 UVQ9

T 7110 6523 6785 8617 monocytes 2294 2390 4504 6477 NK 700 883 1243 827 B 639 927 796 1900 DC 91 183 163 188 NKT cell 22 44 47 114 Progenitor cell 50 18 89 103

seurat_obj <- TestSoftPowers( seurat_obj, setDatExpr=FALSE, powers = c(seq(1, 10, by = 1), seq(12, 30, by = 2))) pickSoftThreshold: will use block size 6681. pickSoftThreshold: calculating connectivity for given powers... ..working on genes 1 through 6681 of 6696 ..working on genes 6682 through 6696 of 6696 Power SFT.R.sq slope truncated.R.sq mean.k. median.k. max.k. 1 1 0.4460 12.00 0.622 3.45e+03 3.46e+03 3780.00 2 2 0.0656 2.18 0.788 1.80e+03 1.80e+03 2220.00 3 3 0.0368 -1.17 0.928 9.45e+02 9.36e+02 1360.00 4 4 0.2220 -2.48 0.866 5.02e+02 4.89e+02 865.00 5 5 0.5460 -3.56 0.840 2.70e+02 2.57e+02 566.00 6 6 0.8540 -4.08 0.936 1.47e+02 1.36e+02 382.00 7 7 0.9290 -3.80 0.964 8.08e+01 7.24e+01 264.00 8 8 0.9570 -3.47 0.979 4.52e+01 3.88e+01 188.00 9 9 0.9680 -3.16 0.983 2.57e+01 2.09e+01 137.00 10 10 0.9700 -2.88 0.986 1.49e+01 1.13e+01 101.00 11 12 0.9560 -2.43 0.973 5.37e+00 3.34e+00 59.80 12 14 0.9250 -2.08 0.948 2.16e+00 1.01e+00 37.80 13 16 0.9690 -1.73 0.987 9.92e-01 3.09e-01 25.10 14 18 0.9880 -1.48 0.992 5.16e-01 9.67e-02 17.40 15 20 0.9830 -1.41 0.991 3.00e-01 3.09e-02 14.40 16 22 0.9840 -1.35 0.995 1.91e-01 1.00e-02 12.20 17 24 0.9820 -1.30 0.991 1.30e-01 3.29e-03 10.40 18 26 0.9850 -1.27 0.987 9.35e-02 1.11e-03 9.00 19 28 0.9560 -1.25 0.949 6.97e-02 3.78e-04 7.82 20 30 0.9160 -1.26 0.895 5.35e-02 1.29e-04 6.85 Warning messages: 1: executing %dopar% sequentially: no parallel backend registered 2: In (function (x, y = NULL, robustX = TRUE, robustY = TRUE, use = "all.obs", : bicor: zero MAD in variable 'x'. Pearson correlation was used for individual columns with zero (or missing) MAD. 3: In (function (x, y = NULL, robustX = TRUE, robustY = TRUE, use = "all.obs", : bicor: zero MAD in variable 'y'. Pearson correlation was used for individual columns with zero (or missing) MAD. 4: In (function (x, y = NULL, robustX = TRUE, robustY = TRUE, use = "all.obs", : bicor: zero MAD in variable 'x'. Pearson correlation was used for individual columns with zero (or missing) MAD. 5: In (function (x, y = NULL, robustX = TRUE, robustY = TRUE, use = "all.obs", : bicor: zero MAD in variable 'y'. Pearson correlation was used for individual columns with zero (or missing) MAD. plot_list <- PlotSoftPowers(seurat_obj, point_size = 5, text_size = 3) Power SFT.R.sq slope truncated.R.sq mean.k. median.k. max.k. 1 1 0.44586933 11.966613 0.6221923 3450.0983 3460.8208 3776.9026 2 2 0.06556859 2.181022 0.7879130 1796.7706 1796.0154 2221.2504 3 3 0.03682151 -1.174009 0.9284033 945.2106 935.8556 1363.6287 4 4 0.22216180 -2.483997 0.8663646 502.3379 489.3100 865.4660 5 5 0.54598619 -3.559985 0.8403527 269.8637 256.8468 566.4454 6 6 0.85402290 -4.076880 0.9360449 146.6881 135.8110 381.6272 setwd("/data/huang/BD_VKH_merge)

  • setwd("/data/huang/BD_VKH_merge")
  • setwd("/data/huang/BD_VKH_merge") Error: unexpected string constant in: "setwd("/data/huang/BD_VKH_merge") setwd("" setwd("/data/huang/BD_VKH_merge") pdf("merge-plotsoftpower.pdf",width=8,height=8) wrap_plots(plot_list, ncol=2) dev.off() null device 1 seurat_obj <- ConstructNetwork( seurat_obj, soft_power=6, setDatExpr=FALSE, tom_name = 'BD_VKH' # name of the topoligical overlap matrix written to disk ) Error in ConstructNetwork(seurat_obj, soft_power = 6, setDatExpr = FALSE, : TOM TOM/BD_VKH_TOM.rda already exists. Set overwrite_tom = TRUE or change tom_name to proceed. pdf("merge-plotsoftpower.pdf",width=8,height=8) wrap_plots(plot_list, ncol=2) dev.off() null device 1 seurat_obj <- ConstructNetwork( seurat_obj, soft_power=6, setDatExpr=FALSE, tom_name = 'BD_VKH' # name of the topoligical overlap matrix written to disk ) Calculating consensus modules and module eigengenes block-wise from all genes Calculating topological overlaps block-wise from all genes Flagging genes and samples with too many missing values... ..step 1 TOM calculation: adjacency.. ..will not use multithreading. Fraction of slow calculations: 0.000000 ..connectivity.. ..matrix multiplication (system BLAS).. ..normalization.. ..done. ..Working on block 1 . ..Working on block 1 . ..merging consensus modules that are too close.. pdf("BD_VKH-T cell plot.pdf",width=20,height=8) PlotDendrogram(seurat_obj, main='hdWGCNA Dendrogram') dev.off() null device 1 TOM <- GetTOM(seurat_obj)

seurat_obj <- ScaleData(seurat_obj, features=VariableFeatures(seurat_obj)) Error in ScaleData(seurat_obj, features = VariableFeatures(seurat_obj)) : could not find function "ScaleData"

library(Seurat) Attaching SeuratObject TOM <- GetTOM(seurat_obj)

seurat_obj <- ScaleData(seurat_obj, features=VariableFeatures(seurat_obj)) Centering and scaling data matrix |======================================================================| 100%

seurat_obj <- ModuleEigengenes( seuratobj, group.by.vars="orig.ident" ) [1] "grey" Centering and scaling data matrix |======================================================================| 100% Warning: Keys should be one or more alphanumeric characters followed by an underscore, setting key from pcagrey to pcagrey Warning: All keys should be one or more alphanumeric characters followed by an underscore '', setting key to pcagrey pcagrey 1 Positive: CST3, IFI30, FCN1, LYZ, SERPINA1, S100A9, S100A8, LST1, FCER1G, VCAN CD68, CD14, CSTA, SPI1, FOS, MS4A6A, GRN, CFD, CLEC7A, FTL S100A12, NCF2, PLXDC2, TMEM176B, CYBB, CSF3R, HCK, NAMPT, MNDA, LILRB2 Negative: IFITM1, EVL, RPS29, CD69, CD6, MT-ATP8, ITM2A, DNAJB1, CD5, S1PR1 CYFIP2, STK17A, TECR, PBXIP1, THEMIS, DENND2D, LY9, PHF1, ORMDL3, RPL41 PRKACB, AC092821.3, KIF2A, NCL, TSC22D3, LINC01138, SRP9, CDKN1B, AP002387.2, KCNA3 pcagrey 2 Positive: S100A8, S100A9, LYZ, VCAN, S100A12, IFI30, CST3, NAMPT, FOS, RBP7 CXCL8, ACSL1, CSTA, FTL, IL1B, CLEC4E, DMXL2, MNDA, NLRP3, PLXDC2 ARHGAP24, FCN1, CSF3R, CDA, ATP2B1-AS1, ADAMTSL4-AS1, CREB5, IRAK3, PLAUR, LRRK2 Negative: CORO1A, SRSF5, RPS29, HNRNPA2B1, EIF1, RBM3, COX6C, EVL, HNRNPK, GSTK1 MT-ATP8, IFITM1, ARL6IP4, SAP18, SRP9, NCL, PGK1, TMA7, HNRNPC, SERBP1 PCBP1, XRCC6, CCDC85B, ARHGAP45, ATP5F1E, ERH, C11orf58, ATP5F1C, JUNB, SSBP1 pcagrey 3 Positive: MS4A1, BANK1, LINC00926, NIBAN3, FCRL1, IGHM, CD79B, FCER2, RUBCNL, BLK ADAM28, MEF2C, HVCN1, P2RX5, SWAP70, WDFY4, OSBPL10, TCF4, BCL11A, CD40 EAF2, PLPP5, CDK14, MARCH1, IRF8, IGKC, JCHAIN, SNX2, CHPT1, CD83 Negative: IFITM1, LCP2, CD6, DNAJB1, CD5, GSTK1, ITM2A, RPS26, RPL41, VSIR TIMP1, UPP1, TMA7, TMEM173, THEMIS, EVL, DENND2D, SLFN5, TECR, ATP5F1E NINJ1, FCER1G, PKM, TOB1, OST4, CORO1A, NDUFB9, TSPAN14, COX6C, ETHE1 pcagrey 4 Positive: FP236383.3, APOO, RBFOX2, AC138123.1, MT-ATP8, CXCL8, ACSL1, LINC01138, IL1B, TEX14 SLC2A3, JUN, EGR1, NAMPT, LRMDA, NSF, NLRP3, ABCA1, ADAMTSL4-AS1, CD69 BCL6, PLXDC2, CCDC200, U2AF1, ATP2B1-AS1, CD83, VCAN, WDFY3, RPS10-NUDT3, DMXL2 Negative: RPL41, MT-ATP6, CALHM6, RPS26, CORO1A, FTL, CD79B, HMOX1, CSF1R, LRRC25 CD68, PCBP1, SERPINA1, GPBAR1, MS4A7, SCIMP, SMIM25, MS4A1, HVCN1, CD40 EAF2, LST1, BTK, IFI30, LGALS9, C20orf27, LILRA1, TIMP1, COTL1, RNH1 pcagrey 5 Positive: MT-ATP6, RPL41, TBC1D8, SLC8A1, STAT1, TBXAS1, GRK3, ADGRE2, CYFIP2, TSPAN14 LCP2, FRY, CSF1R, PECAM1, AMBRA1, CPQ, SLFN5, DAPK1, MS4A7, PSEN1 ANKS1A, KLHL2, WASHC1, DOP1A, XRRA1, SLC11A1, FMN1, IRAK3, ANKFY1, ARRB1 Negative: APOO, RBFOX2, FP236383.3, AC138123.1, RPS29, ATP5F1E, RPS10-NUDT3, LINC01138, JUN, SNRPD3 PLEKHA7, EIF1, TMA7, FTL, MT-ATP8, MT1X, DYNLRB1, ATP5MPL, OOEP, S100A12 ATP5MD, RETN, JUNB, NAA10, OST4, HIGD1A, U2AF1, CSTA, CAPG, CXCL8 Harmony 1/10 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **| Harmony 2/10 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **| Harmony 3/10 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **| Harmony 4/10 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **| Harmony 5/10 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **| Harmony 6/10 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **| Harmony converged after 6 iterations Warning: Invalid name supplied, making object name syntactically valid. New object name is Seurat..ProjectDim.RNA.harmony; see ?make.names for more details on syntax validity [1] "turquoise" Centering and scaling data matrix |======================================================================| 100% Warning: Keys should be one or more alphanumeric characters followed by an underscore, setting key from pcaturquoise to pcaturquoise Warning: All keys should be one or more alphanumeric characters followed by an underscore '', setting key to pcaturquoise pcaturquoise 1 Positive: IFNG-AS1, SEL1L3, PDE4B, TPD52, CD3G, PRKCE, AC007384.1, CLEC2D, SPOCK2, PBX4 CD8B, CIITA, TRAC, VCPKMT, PIGB, GGA2, TGIF1, MED21, KIF20B, GRAP2 HDAC9, WAKMAR2, MT-CO2, PDLIM1, DDHD2, ANAPC10, SP4, SMC6, CSNK1G3, ELL2 Negative: CST7, NKG7, PRF1, GZMA, GZMB, PFN1, FGFBP2, FCGR3A, SRGN, ITGB2 CTSW, CLIC1, GZMH, SH3BGRL3, KLRD1, HOPX, CFL1, CCL5, ARPC2, EFHD2 CYBA, FLNA, HLA-C, SPON2, GNLY, IFITM2, HCST, HLA-A, S100A4, MYL6 pcaturquoise 2 Positive: IL32, IL2RG, CTSW, GZMM, CST7, PTPRCAP, CCL5, NKG7, GZMA, TRBC2 LIME1, ZAP70, PRF1, KLRK1, B2M, CD2, TRBC1, CALM1, GZMH, LITAF PRKCH, GZMB, HOPX, GNLY, FGFBP2, KLRD1, PLAAT4, SPOCK2, MYL12A, KLRB1 Negative: TYMP, CTSS, SAT1, FTH1, HLA-DRA, TYROBP, LGALS3, PSAP, FGL2, LYN S100A11, PYCARD, BLVRB, APLP2, HLA-DRB1, STX11, DUSP1, GABARAP, CPPED1, STXBP2 S100A6, CD74, CTSH, WARS, FGR, LGALS1, ZEB2, FAM49A, PTPRE, HLA-DMA pcaturquoise 3 Positive: GNLY, NKG7, GZMB, FGFBP2, KLRD1, SPON2, PRF1, KLRF1, CST7, AOAH ZEB2, GZMH, MCTP2, CCL5, GZMA, TTC38, S1PR5, CLIC3, SH2D1B, PRSS23 CTSW, CCL4, ADGRG1, FCGR3A, HOPX, C1orf21, FCRL6, MT-CO1, TYROBP, MAP3K8 Negative: CD52, PPIA, ARHGDIB, TMSB4X, ACTG1, PPDPF, LIME1, CD3G, SRP14, MYL12A PSME1, IL32, RAC2, SUMO2, CALM2, TAGLN2, MYL12B, PFN1, CHCHD2, CFL1 YWHAB, ALDOA, HLA-A, COX6A1, UBC, ACTB, ARPC1B, TRAC, ATP5MC3, ATP5F1B pcaturquoise 4 Positive: ACTB, S100A4, TYROBP, S100A6, SH3BGRL3, B2M, GAPDH, TMSB4X, S100A11, GNLY FGFBP2, GZMB, LGALS1, IFITM3, FTH1, S100A10, NKG7, GZMH, GSTP1, FCGR3A HCST, PFN1, PRF1, CTSD, MYL6, IFITM2, CST7, SERF2, SRGN, ANXA1 Negative: HLA-DQA1, HLA-DQB1, EZR, SEL1L3, CXCR4, MT-ND6, TPD52, HLA-DRA, HLA-DMA, SP140 PDLIM1, HLA-DPB1, PDE4B, SP100, MT-ND5, PRKCB, PRKCE, HLA-DRB1, CIITA, EML4 IFNG-AS1, HLA-DMB, SETBP1, STX7, P2RY8, REL, HLA-DPA1, PPP3CA, GGA2, RABGAP1L pcaturquoise 5 Positive: HLA-DQA1, HLA-DPB1, CD74, HLA-DPA1, HLA-DQB1, HLA-DRA, HLA-DRB5, HLA-DRB1, PDLIM1, HLA-DQA2 HLA-DMA, CXXC5, HLA-DMB, FCGR3A, TPD52, LAT2, SETBP1, CIITA, MTSS1, GZMB STX7, KLRF1, SH2D1B, SEL1L3, PLCG2, FGFBP2, CLIC3, SMIM14, SPON2, TLE1 Negative: TNFAIP3, IL32, GZMK, CD3G, SPOCK2, RORA, FYN, DUSP2, PIK3R1, PHACTR2 PARP8, S100A4, S100A10, ANXA1, ARHGAP26, LIME1, ATXN1, SAMSN1, PLCB1, S100A6 CD2, ERN1, SRGN, RNF149, PRKCH, GPRIN3, DYNLT1, HNRNPLL, ZFP36L2, PPP2R5C Harmony 1/10 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **| Harmony 2/10 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **| Harmony 3/10 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **| Harmony 4/10 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **| Harmony 5/10 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **| Harmony 6/10 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **| Harmony 7/10 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **| Harmony 8/10 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **| Harmony converged after 8 iterations Warning: Invalid name supplied, making object name syntactically valid. New object name is Seurat..ProjectDim.RNA.harmony; see ?make.names for more details on syntax validity [1] "red" Centering and scaling data matrix |======================================================================| 100% Warning in irlba(A = t(x = object), nv = npcs, ...) : You're computing too large a percentage of total singular values, use a standard svd instead. Warning: Keys should be one or more alphanumeric characters followed by an underscore, setting key from pcared to pcared Warning: All keys should be one or more alphanumeric characters followed by an underscore '', setting key to pcared pcared 1 Positive: LRCH4, CTDSP2, REPIN1, EXOSC6, ATPAF1, MAP7D1, MKRN1, RETREG2, SLC2A4RG, SNF8 SURF1, ATXN2L, H1FX, TMEM248, SOCS1, PNPLA2, GABPB1-IT1, PTOV1, UBA2, NENF NSMCE3, MMP24OS, FAAP20, EBPL, S1PR4, RNF187, CITED2, PDCD2 Negative: RPS2, KLF2, PNRC1, BTG1, TRIR, YBX1, CIRBP, HNRNPA0, HNRNPDL, KRT10 EIF5A, MZT2A, MZT2B, SUN2, SNHG7, FKBP8, KHDRBS1, SNX3, STUB1, CMPK1 SSU72, H2AFV, RBBP6, BLOC1S4, ODC1, SFPQ, UBE2D3, FNTA pcared 2 Positive: SNF8, SNX3, CTDSP2, MAP7D1, LRCH4, YBX1, MMP24OS, FKBP8, MKRN1, SURF1 PNPLA2, H2AFV, FAAP20, TRIR, SFPQ, NENF, RNF187, KHDRBS1, STUB1, REPIN1 ATXN2L, SSU72, RETREG2, H1FX, S1PR4, UBE2D3, TMEM248, PTOV1 Negative: BTG1, CIRBP, HNRNPDL, KLF2, HNRNPA0, NSMCE3, SUN2, SLC2A4RG, MZT2A, BLOC1S4 GABPB1-IT1, SOCS1, CMPK1, RBBP6, ODC1, MZT2B, EIF5A, SNHG7, PNRC1, RPS2 KRT10, FNTA, UBA2, EBPL, EXOSC6, CITED2, ATPAF1, PDCD2 pcared 3 Positive: SUN2, CITED2, UBE2D3, MAP7D1, HNRNPA0, HNRNPDL, FKBP8, EIF5A, RNF187, H2AFV SLC2A4RG, CIRBP, PDCD2, ATXN2L, UBA2, PNPLA2, MKRN1, CTDSP2, SURF1, STUB1 S1PR4, FAAP20, FNTA, BLOC1S4, RETREG2, TRIR, YBX1, LRCH4 Negative: SNHG7, SNX3, ODC1, NENF, RPS2, PTOV1, REPIN1, PNRC1, SFPQ, BTG1 MZT2B, EBPL, H1FX, SNF8, EXOSC6, CMPK1, SOCS1, GABPB1-IT1, KRT10, RBBP6 KHDRBS1, MZT2A, TMEM248, ATPAF1, KLF2, MMP24OS, NSMCE3, SSU72 pcared 4 Positive: SLC2A4RG, MZT2B, SUN2, ATPAF1, H1FX, CITED2, CMPK1, MZT2A, SOCS1, NENF RETREG2, NSMCE3, MAP7D1, KLF2, STUB1, MMP24OS, S1PR4, EBPL, H2AFV, SNF8 BLOC1S4, LRCH4, EXOSC6, RNF187, SFPQ, PTOV1, FNTA, SSU72 Negative: SNX3, HNRNPDL, EIF5A, YBX1, CIRBP, UBE2D3, HNRNPA0, PNPLA2, ODC1, UBA2 MKRN1, FAAP20, REPIN1, SNHG7, KRT10, BTG1, ATXN2L, PDCD2, GABPB1-IT1, SURF1 RBBP6, TRIR, RPS2, TMEM248, FKBP8, PNRC1, CTDSP2, KHDRBS1 pcared 5 Positive: SURF1, SOCS1, LRCH4, S1PR4, FAAP20, PDCD2, RNF187, EBPL, CITED2, TRIR SSU72, PTOV1, STUB1, PNPLA2, FKBP8, BLOC1S4, MZT2A, RPS2, SNHG7, SLC2A4RG SNX3, KLF2, H2AFV, GABPB1-IT1, ATPAF1, KRT10, ODC1, BTG1 Negative: SFPQ, RBBP6, EXOSC6, ATXN2L, MAP7D1, NSMCE3, H1FX, MKRN1, KHDRBS1, UBE2D3 CTDSP2, RETREG2, TMEM248, SUN2, CMPK1, REPIN1, MMP24OS, UBA2, FNTA, MZT2B PNRC1, CIRBP, EIF5A, HNRNPA0, SNF8, NENF, YBX1, HNRNPDL Harmony 1/10 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **| Harmony 2/10 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **| Harmony 3/10 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **| Harmony converged after 3 iterations Warning: Invalid name supplied, making object name syntactically valid. New object name is Seurat..ProjectDim.RNA.harmony; see ?make.names for more details on syntax validity [1] "brown" Centering and scaling data matrix |======================================================================| 100% Warning: Keys should be one or more alphanumeric characters followed by an underscore, setting key from pcabrown to pcabrown Warning: All keys should be one or more alphanumeric characters followed by an underscore '', setting key to pcabrown pcabrown 1 Positive: EEF1A1, RPS12, RPL13, RPL32, RPS18, RPS23, RPS6, RPS3, RPL10, RPS15A RPS5, RPS25, RPS4X, RPS14, RPL3, RPS3A, RPL30, RPS27A, RPL19, RPL18A RPL14, RPSA, RPL10A, RPL5, RPL29, RPL11, RPL18, RPS8, RPS27, RPS19 Negative: NCF1, CFP, HRH2, NPC2, FCGRT, TKT, LY96, GRINA, FBP1, RENBP LTA4H, C1orf162, HEBP1, TNFAIP8L2, PPT1, MID1IP1, FAM200B, FKBP1A, SPART, NUAK2 C16orf74, TMEM69, LAMTOR4, CD4, MCRIP2, EIF4EBP3, HAUS4, BTF3L4, ANP32A, TCEAL4 pcabrown 2 Positive: CD3E, IL7R, PCED1B-AS1, LEF1, CAMK4, TCF7, CD7, PRKCQ-AS1, LTB, SARAF CCR7, PIK3IP1, TLE5, GIMAP5, MAL, LDHB, NOSIP, FLT3LG, LEPROTL1, CD27 C12orf57, FCMR, GIMAP7, TRAT1, LDLRAP1, SEPTIN1, RCAN3, AQP3, TRABD2A, LBH Negative: CFP, NPC2, TKT, FCGRT, NCF1, TMSB10, GRINA, VIM, LTA4H, C1orf162 ATP5MC2, FKBP1A, RPS24, LY96, SERP1, SLC25A6, HRH2, LAMTOR4, FAU, RPS13 RPLP1, GPX4, COX4I1, PFDN5, GDI2, RPL8, HEBP1, RPL28, FBP1, NACA pcabrown 3 Positive: CFP, TKT, VIM, C1orf162, FCGRT, RGS10, FKBP1A, CD4, NPC2, ACTN1 LEF1, RNASET2, GIMAP1, GRINA, ABRACL, GMFG, ITM2B, GPX4, HRH2, SLC25A3 ATP5PD, NOSIP, GPSM3, PPT1, TCF7, CCR7, PLP2, LTA4H, SELENOH, GIMAP7 Negative: RPL39, RPS27A, RPS12, RPL30, RPL34, RPS15A, RPL10, RPS27, RPS3, RPL32 RPLP1, RPL28, RPS8, RPS28, EEF1A1, RPLP2, RPL19, RPS14, RPS23, RPL13 RPL18A, RPL11, RPS19, RPS3A, FAU, RPS4X, RPL26, RPS15, RPS7, RPL35A pcabrown 4 Positive: RPS13, LEF1, MAL, TCF7, CCR7, VIM, RPL11, CAMK4, PRKCQ-AS1, RGS10 IL7R, ACTN1, RPS8, RPL39, TRABD2A, NOSIP, RPLP1, RPL30, RPL28, RCAN3 LDHB, RPS3A, RPL32, SELL, EPHX2, RPL10, BEX3, RPS24, TRAT1, MYC Negative: RPL27A, RPL37A, RPL23A, RPS27, RPL27, RPL3, RPL35, HNRNPA1, CD37, RPS20 RPL13A, RPS11, HMGN2, HMGN1, RPL21, RPS19, RPSA, UBXN1, RPL38, CD7 RPS6, EIF3G, EIF3D, LIMD2, RPS18, RPL10A, SNHG6, EIF4A2, EEF2, EIF3F pcabrown 5 Positive: CD7, CD3E, HSPA8, GIMAP7, TMSB10, ATP5MC2, GMFG, RPL6, PCED1B-AS1, NDUFA12 GYPC, ABRACL, SSR2, FAU, RPS3, SEPTIN1, EEF1D, CALM3, EIF3K, RPS7 DAD1, SKP1, RPL7A, TLE5, GIMAP5, CUTA, RPL10, NDUFA4, SNRPD2, EIF3G Negative: LTB, NCF1, SYPL1, ZFAS1, GAS5, CD37, SPINT2, SELL, C16orf74, RPL38 CCR7, EIF3E, SNHG8, NUAK2, TMEM123, RPS20, RPS11, EIF2S3, NOP53, MYC RPS9, FCMR, RPL37A, RCAN3, RPS18, SNHG32, RPL27A, LTA4H, RPL4, RPS21 Harmony 1/10 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **| Harmony 2/10 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **| Harmony 3/10 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **| Harmony 4/10 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **| Harmony 5/10 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **| Harmony 6/10 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **| Harmony 7/10 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **| Harmony 8/10 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **| Harmony converged after 8 iterations Warning: Invalid name supplied, making object name syntactically valid. New object name is Seurat..ProjectDim.RNA.harmony; see ?make.names for more details on syntax validity [1] "yellow" Centering and scaling data matrix |======================================================================| 100% Warning: Keys should be one or more alphanumeric characters followed by an underscore, setting key from pcayellow to pcayellow Warning: All keys should be one or more alphanumeric characters followed by an underscore '', setting key to pcayellow pcayellow 1 Positive: AIF1, GCA, IFNGR2, SULT1A1, BRI3, MAML3, IRS2, DENND5A, UBE2E2, SLC25A37 GNAQ, CKAP4, MIR181A1HG, BMP2K, ST6GALNAC3, IL17RA, RIN3, MYO15B, SNN, NFKBIZ MCL1, CEP170, MAP3K1, PELI1, NRIP1, KCNQ1, DISC1, PIK3CB, MSRA, SERTAD2 Negative: BCL11B, ITK, LINC00861, OXNAD1, PCED1B, INPP4B, PDE3B, TXNIP, NDFIP1, NELL2 MALAT1, BCL2, TESPA1, SEPTIN6, TRAF3IP3, ARHGAP15, TXK, FOXP1, SERINC5, BACH2 RHOH, ABLIM1, MGAT4A, DOCK10, DGKA, SF1, ACAP1, TSHZ2, IL6ST, MAML2 pcayellow 2 Positive: LINC00861, MGAT4A, TXNIP, PRMT2, ACAP1, BCL11B, ITK, SCML4, TRAF3IP3, SEPTIN6 TXK, THEM4, CCND3, PCED1B, RBL2, RHOH, OXNAD1, DHRS3, TMC8, CENPK FBXO32, CD28, ABLIM1, LINC01550, SFXN1, ATF7IP2, INPP4B, APBA2, PSIP1, TESPA1 Negative: AIF1, IFNGR2, UBE2E2, NFKBIZ, MAML3, GNAQ, GCA, PELI1, MAP3K1, DENND5A SULT1A1, IL17RA, BRI3, ST6GALNAC3, MIR181A1HG, MCL1, IRS2, CERS6, NCOA3, DISC1 BMP2K, CEP170, FOXP1, SLC25A37, RIN3, SAMHD1, CD55, FKBP5, KCNQ1, AFF3 pcayellow 3 Positive: FYB1, SAMHD1, AIF1, MCL1, GNAQ, SULT1A1, IL17RA, FKBP5, CD44, NFKBIZ NDFIP1, BRI3, DENND5A, ITK, LINC00861, SERINC5, IL6R, BCL11B, MGAT4A, TIAM1 SVIL, INPP4B, PRKCA, OXNAD1, RBMS1, PDE3B, IPCEF1, MPP7, PAG1, IL6ST Negative: CD79A, AFF3, RALGPS2, GNG7, BACH2, STRBP, RAB30, MARCH3, IL4R, LINC02245 NCOA3, BIRC3, ZNF107, CHD7, BCAS4, RHOH, AC025164.1, SNX9, EBLN3P, TMEM243 ZBTB20, SF1, FOXP1, PNISR, SP140L, AC119396.1, TXNIP, PDE7A, RIC3, RHBDD1 pcayellow 4 Positive: RASGRP2, TRAF3IP3, CHURC1, RIN3, CCND3, UBE2B, PNISR, SEPTIN6, DDX39B, PRMT2 MCL1, TXNIP, SEC62, MPHOSPH8, SRSF1, MCUB, TRMT1, ACAP1, CHMP3, CMTM7 FYB1, ADD3, TAF1D, PSIP1, LINC02256, IFNGR2, SNRNP70, DPEP2, CYTIP, OIP5-AS1 Negative: MALAT1, ANK3, INPP4B, BCL2, TSHZ2, PRKCA, ARHGAP15, MAML2, PLCL1, CDC14A NR3C2, ZBTB20, IMMP2L, SESN3, BACH2, IGF1R, PATJ, SERINC5, FHIT, CSGALNACT1 IL6ST, PDE3B, DOCK10, FAAH2, RASGRF2, AP3M2, FKBP5, NCK2, MLLT3, FOXP1 pcayellow 5 Positive: CCND3, ARHGEF3, MALAT1, TXK, ATM, MBNL1, DDX17, PRMT2, APBA2, LINC02256 RIN3, ZBTB20, DOCK10, FKBP5, CYTIP, CDK6, ARHGAP15, RBL2, IKZF1, EPB41 NAP1L4, ELMO1, FBXO32, FTX, ZMYND8, DHRS3, TXNIP, MIR181A1HG, IFNAR2, RBMS1 Negative: MCUB, TSHZ2, ICOS, CD28, DGKA, MDS2, BIRC3, LINC01550, INPP4B, ARMH1 AIF1, CMTM7, TBC1D4, CHMP7, RIC3, TESPA1, FHIT, PVT1, FAM102A, SESN3 CHRM3-AS2, IL6R, FAAH2, IFNGR2, PLK3, CD44, NBPF15, IL6ST, CD79A, MHENCR Harmony 1/10 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **| Harmony 2/10 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **| Harmony 3/10 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **| Harmony converged after 3 iterations Warning: Invalid name supplied, making object name syntactically valid. New object name is Seurat..ProjectDim.RNA.harmony; see ?make.names for more details on syntax validity [1] "blue" Centering and scaling data matrix |======================================================================| 100% Warning: Keys should be one or more alphanumeric characters followed by an underscore, setting key from pcablue to pcablue Warning: All keys should be one or more alphanumeric characters followed by an underscore '', setting key to pcablue pcablue 1 Positive: TC2N, CD247, ETS1, CD96, ZNF831, KLHL28, KIZ, PRKCQ, ZNF484, RBL1 LANCL1, SFI1, MFSD8, KPNA5, LINC01934, NEK1, PRPF39, FOCAD, GOLGA8B, TDG ANKRD36B, PRH1, CEP120, HEATR1, CRY1, ABHD18, PCNX2, SUPV3L1, JMY, SNRPA1 Negative: NEAT1, DPYD, CELF2, MED13L, ZFAND3, LINC01619, SSH2, SIPA1L1, PAN3, ANKRD44 DOCK8, ETV6, JARID2, MEF2A, KMT2C, USP15, SMCHD1, ZSWIM6, ARID1B, GAB2 COP1, CAMK1D, FCHSD2, DIAPH2, BIRC6, SPIDR, FOXN3, MT-ND1, DENND1A, SBF2 pcablue 2 Positive: MT-ND4L, CD247, SKAP1, TC2N, CD96, ETS1, MT-CYB, SLC38A1, RASA3, KLF12 PRKCQ, PPP3CC, TNIK, DDX6, SRSF10, PITPNC1, JAK1, EPC1, STK4, RASA2 LRBA, CCSER2, TUT4, CDC42SE2, AAK1, ZEB1, TNRC6C, SIDT1, LINC01934, ARGLU1 Negative: NEAT1, GAB2, ZSWIM6, DPYD, ETV6, VMP1, DENND1A, SIPA1L1, SBF2, BACH1 NUP214, SLCO3A1, ATG7, CUX1, JARID2, MED13L, DIAPH2, HIF1A, TET2, EXT1 MAP3K3, FNDC3B, ASAP1, TRPS1, AGFG1, CSGALNACT2, MEF2A, PTEN, PRKAG2, CCNY pcablue 3 Positive: CD247, PITPNC1, SORL1, AAK1, PRKCQ, RASA3, CD96, TNIK, TC2N, FNDC3B SKAP1, NEAT1, INPP4A, DPYD, YES1, SLCO3A1, STK38, CCSER2, ESYT2, ETS1 ATP10A, FOXN3, LRRC8C, JAK1, LINC01934, GLG1, CLIP4, SNRK, RELL1, HDAC4 Negative: SIPA1L3, FCHSD2, ADK, ZCCHC7, ITPR1, MGAT5, UVRAG, SNX29, CAMK1D, CAMK2D MT-ND4L, MT-ND3, EIF2AK3, LMBRD1, RB1, MT-CYB, MT-CO3, PRDM2, ARID5B, SECISBP2L PHTF2, MEF2A, SIPA1L1, RERE, MT-ND1, SMCHD1, MT-ND2, FUT8, TBC1D22A, RABEP1 pcablue 4 Positive: MT-CO3, MT-CYB, MT-ND4, MT-ND1, MT-ND3, MT-ND2, MT-ND4L, SORL1, TC2N, CD96 SLCO3A1, PITPNC1, DPYD, SBF2, VMP1, CCNH, BTBD11, AAK1, PRKAG2, CD247 CELF2, FOXN3, FNDC3B, CSGALNACT2, EXT1, MAP3K3, STAT3, CLIP4, NEAT1, DENND1A Negative: FCHSD2, ARID5B, ZCCHC7, ITPR1, MGAT5, ADK, CAMK2D, EIF2AK3, SIPA1L3, SMCHD1 CAMK1D, UVRAG, PHTF2, TLK1, FUT8, PRDM2, ATF7IP, ZEB1, SECISBP2L, RB1 RABEP1, LRBA, SMARCC1, ZHX2, ARID1B, RFX3, RSRC2, ITSN2, DIPK1A, SNX29 pcablue 5 Positive: RUNX1, MGAT5, XIST, RFX3, KLF12, AKT3, RAD51B, CASK, ZNF407, SMYD3 LINC01619, SKAP1, CDKAL1, FBXL17, PCNX1, TNIK, BBS9, ARL15, TC2N, CELF2 SCFD2, BTBD9, CAMKMT, LRBA, MSI2, EXOC4, LINC01934, ANKRD44, STXBP5, KDM6A Negative: ARGLU1, HNRNPD, WTAP, BCLAF1, RSRC2, KPNB1, SRSF10, CCNL1, ADAR, SF3B1 RIOK3, SEPTIN2, ERAP2, ZNF207, PRRC2C, JAK1, PNN, G3BP2, CLK1, SNRPA1 HNRNPH1, PCGF5, SRSF4, MYLIP, USP16, STK17B, PPP4R3A, DCTN4, AP001011.1, PRPF38B Harmony 1/10 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **| Harmony 2/10 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **| Harmony converged after 2 iterations Warning: Invalid name supplied, making object name syntactically valid. New object name is Seurat..ProjectDim.RNA.harmony; see ?make.names for more details on syntax validity [1] "green" Centering and scaling data matrix |======================================================================| 100% Warning in irlba(A = t(x = object), nv = npcs, ...) : You're computing too large a percentage of total singular values, use a standard svd instead. Warning: Keys should be one or more alphanumeric characters followed by an underscore, setting key from pcagreen to pcagreen Warning: All keys should be one or more alphanumeric characters followed by an underscore '', setting key to pcagreen pcagreen 1 Positive: RPL36AL, UCP2, NME2, EDF1, ENO1, MIF, GIMAP4, SLC25A5, HIGD2A, SELPLG ARPC5, ATP5F1D, TUBB, NDUFA11, TUBA1B, BANF1, LCK, PRDX1, TWF2, ATP5PB SPCS1, H2AFZ, ANXA11, C4orf3, ATP5PF, SELENOW, ACTR3, LAT, ICAM2, PSMC5 Negative: CHTF8, CFAP298, VPS72, ZNF524, CISH, IRF2, PNP, ORMDL2, FGFR1OP2, RNF5 ECHS1, NDUFA8, AHSA1, GSTM1, SIT1, UBL7, C14orf119, EGLN2, TMEM35B, MRPL14 PPP1R11, ESYT1, PRMT1, LSM2, TALDO1, SYNGR2, KDELR1, TSPO, PRDX3, TMBIM4 pcagreen 2 Positive: CD3D, LCK, LAT, SIT1, HMOX2, MIF, MAT2B, RPL36AL, ESYT1, GIMAP4 CISH, SELENOW, CCT7, TMEM35B, ICAM2, PRMT1, SELPLG, ANXA11, PSMC5, SPCS1 FGFR1OP2, CHTF8, CFAP298, PHB, EDF1, UCP2, MRPL34, VPS72, TUBB, EGLN2 Negative: TSPO, TALDO1, TRAPPC5, GLRX, NOP10, PRDX3, SLC25A5, SYNGR2, PTPN6, ARPC5 HIGD2A, NME2, TMBIM4, KDELR1, ZNF524, NDUFA11, ATP5F1D, TWF2, ENO1, ATP5PB POLD4, CCT5, PRDX1, NDUFB3, H2AFZ, MRPL14, ECHS1, CCT6A, TUBA1B, MRPL51 pcagreen 3 Positive: SYNGR2, PTPN6, POLD4, PRMT1, UCP2, TUBA1B, IRF2, BANF1, LSM2, UBL7 TUBB, CCT7, ACTR3, CHTF8, AHSA1, RNF5, CFAP298, PPP2R1A, CCT5, PHB MAT2B, EGLN2, ECHS1, ICAM2, CCT6A, ECH1, EIF3I, TMBIM4, VPS72, NME2 Negative: TSPO, CD3D, GLRX, GIMAP4, TALDO1, LAT, LCK, HIGD2A, NDUFA11, SELENOW NOP10, SELPLG, ATP5F1D, H2AFZ, RPL36AL, TRAPPC5, CISH, ZNF524, PRDX3, KDELR1 TWF2, TMEM35B, C4orf3, ENO1, ARPC5, MRPS34, ATP5PF, HMOX2, ANXA11, SIT1 pcagreen 4 Positive: MIF, RPL36AL, NME2, SPCS1, EDF1, ATP5F1D, ATP5PF, SLC25A5, NDUFA11, HIGD2A EIF3I, PRMT1, MRPL34, C4orf3, PHB, SYNGR2, PDCD5, ANXA11, PSMC5, CD3D CCT7, LSM2, ENO1, SELENOW, ECHS1, MRPS34, H2AFZ, MRPL14, UBL7, ATP5PB Negative: GIMAP4, SELPLG, CISH, GSTM1, ICAM2, PNP, TMEM35B, ACTR3, C14orf119, IRF2 EGLN2, ARPC5, PPP1R11, ORMDL2, PRDX3, PTPN6, CCT5, GLRX, TUBA1B, TMBIM4 TWF2, VPS72, FGFR1OP2, HMOX2, NDUFA8, ESYT1, CFAP298, AHSA1, NOP10, RNF5 pcagreen_ 5 Positive: SIT1, CISH, PRDX1, EGLN2, SYNGR2, CHTF8, CD3D, NME2, HIGD2A, PNP PDCD5, ZNF524, TMEM35B, KDELR1, FGFR1OP2, POLD4, UBL7, PPP1R11, ICAM2, PRDX3 GSTM1, TRAPPC5, TALDO1, TSPO, MIF, TMBIM4, RPL36AL, SPCS1, CFAP298, ESYT1 Negative: BANF1, HMOX2, ARPC5, SELENOW, RNF5, SELPLG, ACTR3, ECH1, PHB, AHSA1 MAT2B, ATP5PF, TWF2, ORMDL2, PPP2R1A, NDUFA8, GLRX, C14orf119, ENO1, MRPL51 NOP10, ANXA11, LCK, LSM2, MRPL14, PSMC5, C4orf3, CCT5, VPS72, NDUFB3 Harmony 1/10 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **| Harmony 2/10 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **| Harmony converged after 2 iterations Warning: Invalid name supplied, making object name syntactically valid. New object name is Seurat..ProjectDim.RNA.harmony; see ?make.names for more details on syntax validity There were 48 warnings (use warnings() to see them) hMEs <- GetMEs(seurat_obj) MEs <- GetMEs(seurat_obj, harmonized=FALSE) seurat_obj <- ModuleConnectivity( seurat_obj, group.by = 'celltype', group_name = 'T' ) seurat_obj <- ResetModuleNames( seurat_obj, new_name = "BD_VKH-T cell-M" ) seurat_obj <- ModuleExprScore( seurat_obj, n_genes = 25, method='Seurat' ) Selecting by kME_BD_VKH-T cell-M1 Selecting by kME_BD_VKH-T cell-M2 Selecting by kME_BD_VKH-T cell-M3 Selecting by kME_BD_VKH-T cell-M4 Selecting by kME_BD_VKH-T cell-M5 Selecting by kME_BD_VKH-T cell-M6 plot_list <- ModuleFeaturePlot( seurat_obj, features='hMEs', # plot the hMEs o

  • plot_list <- ModuleFeaturePlot( seurat_obj, features='hMEs', # plot the hMEs order=TRUE # order so the points with highest hMEs are on top ) Error: unexpected symbol in: " o plot_list" plot_list <- ModuleFeaturePlot( seurat_obj, features='hMEs', # plot the hMEs order=TRUE # order so the points with highest hMEs are on top ) [1] "BD_VKH-T cell-M1" [1] "BD_VKH-T cell-M2" [1] "BD_VKH-T cell-M3" [1] "BD_VKH-T cell-M4" [1] "BD_VKH-T cell-M5" [1] "BD_VKH-T cell-M6" plot_list <- ModuleFeaturePlot( seurat_obj, features='scores', # plot the hub gene scores order='shuffle', # order so cells are shuffled ) [1] "BD_VKH-T cell-M1" [1] "BD_VKH-T cell-M2" [1] "BD_VKH-T cell-M3" [1] "BD_VKH-T cell-M4" [1] "BD_VKH-T cell-M5" [1] "BD_VKH-T cell-M6" MEs <- GetMEs(seurat_obj, harmonized=TRUE) mods <- colnames(MEs); mods <- mods[mods != 'grey'] seurat_obj@meta.data <- cbind(seurat_obj@meta.data, MEs) group1 <- seurat_obj@meta.data %>% subset(celltype == 'T' & original== "BD") %>% rownames group2 <- seurat_obj@meta.data %>% subset(celltype == 'T' & original== "VKH") %>% rownames head(group1) [1] "BD1_AAACCTGAGATGTGTA-1" "BD1_AAACCTGAGCGTCTAT-1" "BD1_AAACCTGCACCTGGTG-1" [4] "BD1_AAACCTGCAGCTGGCT-1" "BD1_AAACCTGCAGTAACGG-1" "BD1_AAACCTGCATATACCG-1" DMEs <- FindDMEs( Zhou_2020, barcodes1 = group1, barcodes2 = group2, test.use='wilcox', wgcna_name='T' ) Error in is.data.frame(x) : object 'Zhou_2020' not found DMEs <- FindDMEs( seurat_obj, barcodes1 = group1, barcodes2 = group2, test.use='wilcox', wgcna_name='T' ) NULL NULL Error in Seurat::CreateAssayObject(MEs) : No cell names (colnames) names present in the input matrix
smorabit commented 9 months ago

I was able to reproduce the exact same error if the wgcna_name parameter was set to something that did not exist within the Seurat object. Can you double check that this parameter was set correctly?

Rahul1711arora commented 9 months ago

@smorabit , I got the same error and as per your suggestion, I looked at the wgcna_name that it should have been set to seurat_obj@misc which in my case was an empty list. I set that parameter to NULL and got the error message: Error in seurat_obj@misc[[wgcna_name]] : attempt to select less than one element in get1index. So, I'm not sure what I'm doing wrong. Similar to @olive-yelloweyes , I ran the tutorial as is and it ran fine on my dataset but not the DME analysis. It would be helpful if we can get a bit more information around setting these parameters. Thanks in advance!

olive-yelloweyes commented 9 months ago

Hi, I managed to solve it by using the wgcna_name that I used in the first step "seurat_obj <- SetupForWGCNA( seurat_obj, gene_select = "fraction", fraction = 0.05, wgcna_name = "tutorial")",I think the names of the wgcna won't change the result, you can try it!good luck!

Rahul1711arora commented 9 months ago

@olive-yelloweyes It worked indeed! I also solved another problem. Let's say that you want to compare the modules between two different seurat objects, then you need to merge them first and then run the hdWGCNA on that object. Got DMEs to run and made the plots.

olive-yelloweyes commented 9 months ago

@Rahul1711arora yes I also compared the modules between two different seurat objects but I merged it first so that I didn't have the error.

smorabit commented 9 months ago

Closing the issue since it seems to be solved now. If you would like to discuss another topic then please open a new issue.