Visualizing Large-scale Copy Number Variation in Single-Cell RNA-Seq Expression Data
Installation
Required dependencies
inferCNV uses the R packages ape, BiocGenerics, binhf, caTools, coda, coin, dplyr, doparallel, edgeR, fastcluster, fitdistrplus, foreach, futile.logger, future, gplots, ggplot2, HiddenMarkov, leiden, phyclust, RANN, reshape, rjags, RColorBrewer, SingleCellExperiment, SummarizedExperiment, tidyr and imports functions from the archived GMD.
Installing
Optional extension
If you want to use the interactive heatmap visualization, please check the add-on packge R inferCNV_NGCHM after installing the packages tibble, tsvio and NGCHMR. To install optional packages, type the following in an R command window:
install.packages("tibble")
install.packages("devtools")
devtools::install_github("bmbroom/tsvio")
devtools::install_github("bmbroom/NGCHMR", ref="stable")
devtools::install_github("broadinstitute/inferCNV_NGCHM")And download the NGCHM java application by typing the following in a regular shell:
Running InferCNV
Create the InferCNV Object
Reading in the raw counts matrix and meta data, populating the infercnv object
infercnv_obj = CreateInfercnvObject(
raw_counts_matrix="../inst/extdata/oligodendroglioma_expression_downsampled.counts.matrix.gz",
annotations_file="../inst/extdata/oligodendroglioma_annotations_downsampled.txt",
delim="\t",
gene_order_file="../inst/extdata/gencode_downsampled.EXAMPLE_ONLY_DONT_REUSE.txt",
ref_group_names=c("Microglia/Macrophage","Oligodendrocytes (non-malignant)"))## INFO [2026-01-04 16:55:48] Parsing matrix: ../inst/extdata/oligodendroglioma_expression_downsampled.counts.matrix.gz## INFO [2026-01-04 16:55:50] Parsing gene order file: ../inst/extdata/gencode_downsampled.EXAMPLE_ONLY_DONT_REUSE.txt## INFO [2026-01-04 16:55:50] Parsing cell annotations file: ../inst/extdata/oligodendroglioma_annotations_downsampled.txt## INFO [2026-01-04 16:55:50] ::order_reduce:Start.## INFO [2026-01-04 16:55:50] .order_reduce(): expr and order match.## INFO [2026-01-04 16:55:50] ::process_data:order_reduce:Reduction from positional data, new dimensions (r,c) = 10338,184 Total=18322440.6799817 Min=0 Max=34215.## INFO [2026-01-04 16:55:50] num genes removed taking into account provided gene ordering list: 399 = 3.8595473012188% removed.## INFO [2026-01-04 16:55:50] -filtering out cells < 100 or > Inf, removing 0 % of cells## WARN [2026-01-04 16:55:51] Please use "options(scipen = 100)" before running infercnv if you are using the analysis_mode="subclusters" option or you may encounter an error while the hclust is being generated.## INFO [2026-01-04 16:55:51] validating infercnv_objRunning the full default analysis
out_dir = tempfile()
infercnv_obj_default = infercnv::run(
infercnv_obj,
cutoff=1, # cutoff=1 works well for Smart-seq2, and cutoff=0.1 works well for 10x Genomics
out_dir=out_dir,
cluster_by_groups=TRUE,
plot_steps=FALSE,
denoise=TRUE,
HMM=FALSE,
no_prelim_plot=TRUE,
png_res=60
)## INFO [2026-01-04 16:55:51] ::process_data:Start## INFO [2026-01-04 16:55:51] Creating output path /tmp/RtmpYpWqlu/file1f914141196b## INFO [2026-01-04 16:55:51] Checking for saved results.## INFO [2026-01-04 16:55:51]
##
## STEP 1: incoming data## INFO [2026-01-04 16:55:51]
##
## STEP 02: Removing lowly expressed genes## INFO [2026-01-04 16:55:51] ::above_min_mean_expr_cutoff:Start## INFO [2026-01-04 16:55:51] Removing 1431 genes from matrix as below mean expr threshold: 1## INFO [2026-01-04 16:55:51] validating infercnv_obj## INFO [2026-01-04 16:55:51] There are 8508 genes and 184 cells remaining in the expr matrix.## INFO [2026-01-04 16:55:52] no genes removed due to min cells/gene filter## INFO [2026-01-04 16:55:53]
##
## STEP 03: normalization by sequencing depth## INFO [2026-01-04 16:55:53] normalizing counts matrix by depth## INFO [2026-01-04 16:55:53] Computed total sum normalization factor as median libsize: 93909.929740## INFO [2026-01-04 16:55:53]
##
## STEP 04: log transformation of data## INFO [2026-01-04 16:55:53] transforming log2xplus1()## INFO [2026-01-04 16:55:55]
##
## STEP 08: removing average of reference data (before smoothing)## INFO [2026-01-04 16:55:55] ::subtract_ref_expr_from_obs:Start inv_log=FALSE, use_bounds=TRUE## INFO [2026-01-04 16:55:55] subtracting mean(normal) per gene per cell across all data## INFO [2026-01-04 16:55:56] -subtracting expr per gene, use_bounds=TRUE## INFO [2026-01-04 16:55:58]
##
## STEP 09: apply max centered expression threshold: 3## INFO [2026-01-04 16:55:58] ::process_data:setting max centered expr, threshold set to: +/-: 3## INFO [2026-01-04 16:55:59]
##
## STEP 10: Smoothing data per cell by chromosome## INFO [2026-01-04 16:55:59] smooth_by_chromosome: chr: chr1## INFO [2026-01-04 16:56:00] smooth_by_chromosome: chr: chr2## INFO [2026-01-04 16:56:00] smooth_by_chromosome: chr: chr3## INFO [2026-01-04 16:56:00] smooth_by_chromosome: chr: chr4## INFO [2026-01-04 16:56:01] smooth_by_chromosome: chr: chr5## INFO [2026-01-04 16:56:01] smooth_by_chromosome: chr: chr6## INFO [2026-01-04 16:56:01] smooth_by_chromosome: chr: chr7## INFO [2026-01-04 16:56:01] smooth_by_chromosome: chr: chr8## INFO [2026-01-04 16:56:02] smooth_by_chromosome: chr: chr9## INFO [2026-01-04 16:56:02] smooth_by_chromosome: chr: chr10## INFO [2026-01-04 16:56:02] smooth_by_chromosome: chr: chr11## INFO [2026-01-04 16:56:02] smooth_by_chromosome: chr: chr12## INFO [2026-01-04 16:56:03] smooth_by_chromosome: chr: chr13## INFO [2026-01-04 16:56:03] smooth_by_chromosome: chr: chr14## INFO [2026-01-04 16:56:03] smooth_by_chromosome: chr: chr15## INFO [2026-01-04 16:56:03] smooth_by_chromosome: chr: chr16## INFO [2026-01-04 16:56:04] smooth_by_chromosome: chr: chr17## INFO [2026-01-04 16:56:04] smooth_by_chromosome: chr: chr18## INFO [2026-01-04 16:56:04] smooth_by_chromosome: chr: chr19## INFO [2026-01-04 16:56:04] smooth_by_chromosome: chr: chr20## INFO [2026-01-04 16:56:04] smooth_by_chromosome: chr: chr21## INFO [2026-01-04 16:56:04] smooth_by_chromosome: chr: chr22## INFO [2026-01-04 16:56:06]
##
## STEP 11: re-centering data across chromosome after smoothing## INFO [2026-01-04 16:56:06] ::center_smooth across chromosomes per cell## INFO [2026-01-04 16:56:07]
##
## STEP 12: removing average of reference data (after smoothing)## INFO [2026-01-04 16:56:07] ::subtract_ref_expr_from_obs:Start inv_log=FALSE, use_bounds=TRUE## INFO [2026-01-04 16:56:07] subtracting mean(normal) per gene per cell across all data## INFO [2026-01-04 16:56:09] -subtracting expr per gene, use_bounds=TRUE## INFO [2026-01-04 16:56:10]
##
## STEP 14: invert log2(FC) to FC## INFO [2026-01-04 16:56:10] invert_log2(), computing 2^x## INFO [2026-01-04 16:56:12]
##
## STEP 15: computing tumor subclusters via leiden## INFO [2026-01-04 16:56:12] define_signif_tumor_subclusters(p_val=0.1## INFO [2026-01-04 16:56:12] define_signif_tumor_subclusters(), tumor: malignant_93## INFO [2026-01-04 16:56:12] Setting auto leiden resolution for malignant_93 to 0.355267## Warning: Data is of class matrix. Coercing to dgCMatrix.## Finding variable features for layer counts## Centering and scaling data matrix## PC_ 1
## Positive: PSMD14, GCA, TANK, SCN2A, MARCH7, CSRNP3, TTC21B, SCN1A, SPC25, DHRS9
## BBS5, FASTKD1, PPIG, AKIRIN1, NDUFS5, RRAGC, MACF1, UTP11L, PPIEL, PHOSPHO2
## FHL3, PABPC4, SF3A3, PPIE, INPP5B, KLHL23, TRIT1, C1orf122, CAP1, MANEAL
## Negative: PPM1A, DHRS7, MNAT1, TRMT5, SLC38A6, HIF1A, SNAPC1, LINC00643, PCNXL4, DDHD1
## FERMT2, WDR89, GNPNAT1, GMFB, PPP2R5E, CGRRF1, STYX, RTN1, SOCS4, PSMC6
## MAPK1IP1L, FBXO34, ATG14, TXNDC16, JKAMP, KTN1, L3HYPDH, PELI2, C14orf166, KIAA0586
## PC_ 2
## Positive: TMEM219, KCTD13, ASPHD1, TAOK2, SEZ6L2, HIRIP3, CDIPT, ALDOA, MVP, PAGR1
## PPP4C, PRRT2, MAZ, YPEL3, KIF22, QPRT, MAPK3, SULT1A4, SLX1B, CORO1A
## BOLA2, BOLA2B, SPNS1, SLX1A, NFATC2IP, SULT1A3, TUFM, CD2BP2, ATXN2L, TBC1D10B
## Negative: SEL1L, DIO2, NRXN3, ADCK1, GALC, SNW1, SPATA7, SLIRP, ZC3H14, ALKBH1
## TTC8, SPTLC2, FOXN3, AHSA1, EFCAB11, VIPAS39, TMED8, PSMC1, GSTZ1, POMT2
## CALM1, ZDHHC22, KIAA1737, RPS6KA5, ANGEL1, VASH1, C14orf159, GPATCH2L, TGFB3, SMEK1
## PC_ 3
## Positive: CAND1, TMBIM4, DYRK2, MDM1, NUP107, RAP1B, LLPH, SLC35E3, LEMD3, MDM2
## CPM, GNS, CPSF6, LYZ, TBK1, YEATS4, XPOT, FRS2, C12orf66, SRGAP1
## CCT2, TMEM5, PPM1H, MON2, RAB3IP, USP15, CTDSP2, CNOT2, TSFM, METTL1
## Negative: RPL23A, SUPT6H, TRAF4, SDF2, FAM222B, KIAA0100, ALDOC, PIGS, ARHGEF26, ARHGEF26-AS1
## RAP2B, P2RY1, RNPC3, DPH5, MBNL1, SLC30A7, SLC44A1, ABCA1, FSD1L, EXTL2
## NIPSNAP3A, FKTN, RTCA, TMEM38B, HIAT1, P2RY12, SMC2, SLC35A3, ZNF462, KLF4
## PC_ 4
## Positive: MRPL46, NTRK3, KLHL25, AKAP13, MRPS11, DET1, AEN, MFGE8, ABHD2, POLG
## PEX11A, AP3S2, IDH2, C15orf38-AP3S2, C15orf38, CIB1, NGRN, CRTC3, MAN2A2, UNC45A
## HDDC3, VPS33B, CHD2, RGMA, IGF1R, LRRC28, MEF2A, LINS, ASB7, VIMP
## Negative: RBM12, NFS1, CPNE1, ROMO1, ERGIC3, CEP250, EIF6, RBM39, EDEM2, TRPC4AP
## PHF20, GSS, SCAND1, ACSS2, LINC00657, GGT7, EPB41L1, TP53INP2, AAR2, NCOA6
## DLGAP4, PIGU, C20orf24, NDRG3, MAP1LC3A, SOGA1, DYNLRB1, SAMHD1, ITCH, RBL1
## PC_ 5
## Positive: STX17, ERP44, NR4A3, INVS, SEC61B, TEX10, ALG2, MSANTD3, MSANTD3-TMEFF1, TGFBR1
## LPPR1, TBC1D2, MRPL50, TRIM14, ZNF189, NANS, ANP32B, TMEM246, C9orf156, RNF20
## XPA, SMC2, NCBP1, NIPSNAP3A, TSTD2, ABCA1, TMOD1, SLC44A1, ZNF510, FSD1L
## Negative: ZNF160, ZNF415, ZNF83, ZNF331, MYADM, ZNF528, NDUFA3, ZNF880, ZNF610, ZNF480
## TFPT, PRPF31, LENG1, MBOAT7, TSEN34, RPS9, LILRA4, LAIR1, TTYH1, LENG8
## LILRB4, RDH13, HSPBP1, RPL28, SHISA7, ISOC2, ZNF580, ZNF581, ZNF542, ZNF583## Computing nearest neighbor graph## Computing SNN## INFO [2026-01-04 16:56:13] define_signif_tumor_subclusters(), tumor: malignant_97## INFO [2026-01-04 16:56:13] Setting auto leiden resolution for malignant_97 to 0.398413## Warning: Data is of class matrix. Coercing to dgCMatrix.## Finding variable features for layer counts## Centering and scaling data matrix## PC_ 1
## Positive: RFC1, RPL9, KLHL5, LIAS, UGDH, TMEM156, UGDH-AS1, TLR1, KLF3, SMIM14
## PGM2, RELL1, UBE2K, ARAP2, TBC1D19, PDS5A, RBPJ, APBB2, ANAPC4, DHX15
## UCHL1, PACRGL, LCORL, DCAF16, LIMCH1, MED28, LAP3, TMEM33, QDPR, TAPT1-AS1
## Negative: PNPLA2, RPLP2, CD151, PIDD, SLC25A22, CEND1, POLR2L, PDDC1, TALDO1, CHID1
## TMEM80, TOLLIP, DEAF1, IRF7, MOB2, PHRF1, SNHG9, RPS2, IFITM10, TBL3
## NDUFB10, NTHL1, MSRB1, FAHD1, NUBP2, HAGH, SPSB3, MRPS34, TSC2, CRAMP1L
## PC_ 2
## Positive: APC2, C19orf25, MBD3, UQCR11, SCAMP4, BTBD2, MOB3A, AP3D1, PLEKHJ1, RNPS1
## ECI1, E4F1, ABCA3, TBC1D24, ATP6V0C, MLST8, TRAF7, AMDHD2, SF3A2, PKD1
## TSC2, NTHL1, TBL3, SNHG9, CEMP1, RPS2, NDUFB10, OAZ1, MSRB1, FAHD1
## Negative: ZNF131, NIM1, NIPBL, SLC1A3, SEPP1, HMGCS1, SKP2, C5orf42, FBXO4, LMBRD2
## C5orf28, C5orf51, NUP155, BRIX1, PAIP1, WDR70, RAD1, OXCT1, NNT, LIFR
## AMACR, RPL37, RICTOR, TARS, FYB, MRPS30, DAB2, PRKAA1, SUB1, TTC33
## PC_ 3
## Positive: PSMC5, TEX2, CCDC47, POLG2, FTSJ3, STRADA, DDX42, DDX5, TACO1, MIR3064
## MIR5047, DCAF7, CEP95, TANC2, SMURF2, PLEKHM1P, METTL2A, LRRC37A3, MED13, AMZ2P1
## INTS2, BCAS3, GNA13, PRKCA, PPM1D, CACNG4, APPBP2, HELZ, PSMD12, USP32
## Negative: PODXL2, MCM2, TPRA1, MGLL, PLXNA1, SEC61A1, CHCHD6, ZXDC, RUVBL1, SLC41A3
## CCDC14, KALRN, OSBPL11, UMPS, SNX4, ZNF148, EEFSEC, MYLK, PTPLB, RPN1
## SEC22A, RAB7A, SEMA5B, ACAD9, HSPBAP1, ISY1, DTX3L, CNBP, PARP9, COPG1
## PC_ 4
## Positive: MOSPD3, ACTL6B, GNB2, GIGYF1, TSC22D4, POP7, C7orf61, MEPCE, ZCWPW1, SLC12A9
## PILRA, PILRB, TRIP6, SRRT, ACHE, AP1S1, PLOD3, ZNHIT1, FIS1, RABL5
## CUX1, PRKRIP1, ORAI2, ALKBH4, NDN, MKRN3, SNRPN, SNURF, HERC2P7, UBE3A
## Negative: SYNJ2BP, MED6, COX16, SMOC1, SRSF5, PCNX, KIAA0247, ZFYVE1, RBM25, PSEN1
## NUMB, ACOT2, PNMA1, ELMSAN1, PTGR2, CRY1, MTERFD3, ZNF410, C12orf23, RIC8B
## POLR3B, FAM161B, TCP11L2, CKAP4, COQ6, C12orf75, ALDH6A1, APPL2, LIN52, ABCD4
## PC_ 5
## Positive: FGFR1OP, RNASET2, RPS6KA2, MLLT4, MPC1, SFT2D1, THBS2, QKI, C6orf120, CAHM
## LINC00574, AGPAT4, DLL1, FAM120B, MAP3K4, PSMB1, TBP, LPAL2, PDCD2, MRPL18
## TCP1, SRD5A1, MTRR, NSUN2, CCDC127, MED10, SDHA, FASTKD3, MRPL36, PDCD6
## Negative: SEC61B, NR4A3, ALG2, STX17, TGFBR1, TBC1D2, ERP44, TRIM14, NANS, INVS
## ANP32B, ECHDC1, RNF146, TEX10, KIAA0408, TRMT11, HINT3, NCOA7, C9orf156, MSANTD3
## PTPRK, MSANTD3-TMEFF1, EPB41L2, XPA, LPPR1, AKAP7, MRPL50, ZNF189, TMEM246, NCBP1## Computing nearest neighbor graph## Computing SNN## INFO [2026-01-04 16:56:13] define_signif_tumor_subclusters(), tumor: malignant_MGH36## INFO [2026-01-04 16:56:13] Setting auto leiden resolution for malignant_MGH36 to 0.419053## Warning: Data is of class matrix. Coercing to dgCMatrix.## Finding variable features for layer counts## Centering and scaling data matrix## PC_ 1
## Positive: NTHL1, TBL3, SNHG9, RPS2, NDUFB10, MSRB1, FAHD1, HAGH, NUBP2, SPSB3
## MRPS34, NME3, MAPK8IP3, CRAMP1L, TELO2, CLCN7, C16orf91, UNKL, GNPTG, BAIAP3
## UBE2I, SOX8, LMF1, CHTF18, RPUSD1, NARFL, HAGHL, WSCD1, MIS12, C1QBP
## Negative: NFASC, MDM4, CNTN2, PIK3C2B, RBBP5, PPP1R15B, DSTYK, TMCC2, SNRPE, ZBED6
## CDK18, ZC3H11A, NUCKS1, ATP2B4, RAB7L1, BTG2, SLC41A1, ADORA1, SRGAP2, TMEM183A
## GLMN, BTBD8, RPAP2, CDC7, ZNF644, RPL5, ZNF326, CYB5R1, LRRC8D, EIF2D
## PC_ 2
## Positive: GBA2, CREB3, TLN1, CCDC107, RUSC2, STOML2, PIGO, FANCG, VCP, DNAJB5
## IL11RA, KIAA1161, FAM219A, GALT, NUDT2, RPP25L, DCTN3, SIGMAR1, UBAP1, ATP5H
## ARMC7, DCAF12, ICT1, NT5C, HN1, UBAP2, CDR2L, SUMO2, HID1, UBE2R2
## Negative: CD27-AS1, TNFRSF1A, TAPBPL, VAMP1, NACA, MRPL51, PRIM1, SNORA48, PARP11, LRP1
## TULP3, RHNO1, SHMT2, NRIP2, R3HDM2, ITFG2, FKBP4, ARHGAP9, DCP1B, MARS
## ADIPOR2, DDIT3, ERC1, RAD52, DCTN2, WNK1, KIF5A, PIP4K2C, NINJ2, DTX3
## PC_ 3
## Positive: IFI27L2, KIAA0196, SQLE, NSMCE2, DICER1, MTSS1, DDX24, TRIB1, NDUFB9, DICER1-AS1
## FAM84B, TATDN1, SNHG10, RNF139, UNC79, GLRX5, TRMT12, MYC, FAM91A1, UBR7
## C14orf132, FAM49B, WDYHV1, ATG2B, ZHX1, ASAP1, C14orf142, C8orf76, GSKIP, EFR3A
## Negative: TSTD2, NCBP1, XPA, C9orf156, ANP32B, NANS, TRIM14, TBC1D2, TGFBR1, ALG2
## SEC61B, NR4A3, STX17, ERP44, INVS, TEX10, MSANTD3, MSANTD3-TMEFF1, LPPR1, MRPL50
## COMMD1, CCT4, B3GNT2, FAM161A, EHBP1, WDPCP, TIA1, C2orf42, XPO1, ZNF189
## PC_ 4
## Positive: ACSS2, GSS, GGT7, TRPC4AP, TP53INP2, NCOA6, EDEM2, PIGU, EIF6, CEP250
## MAP1LC3A, ERGIC3, CPNE1, DYNLRB1, RBM12, NFS1, ITCH, ROMO1, RBM39, PHF20
## AHCY, VPS29, SCAND1, FAM216A, LINC00657, EIF2S2, GPN3, EPB41L1, AAR2, ARPC3
## Negative: POLG, ABHD2, PEX11A, MFGE8, AEN, AP3S2, DET1, C15orf38-AP3S2, MRPS11, C15orf38
## MAN2A2, UNC45A, CRTC3, IDH2, NGRN, MRPL46, HDDC3, CIB1, VPS33B, NTRK3
## CHD2, RGMA, KLHL25, AKAP13, IGF1R, ZNF592, SEC11A, NMB, LRRC28, WDR73
## PC_ 5
## Positive: SGTB, NLN, TRAPPC13, TRIM23, PPWD1, CWC27, RNF180, IPO11, DIMT1, KIF2A
## ZSWIM6, SMIM15, ADAMTS1, NDUFAF2, APP, N6AMT1, LTN1, GABPA, ATP5J, RWDD2B
## ERCC8, JAM2, USP16, PLK2, CCT8, MRPL39, BACH1, TIAM1, NCAM2, GPBP1
## Negative: MFNG, CDC42EP1, GGA1, PDXP, LGALS1, H1F0, GCAT, ANKRD54, EIF3L, POLR2F
## PLA2G6, MAFF, TMEM184B, CSNK1E, DDX17, CBY1, TOMM22, JOSD1, GTPBP1, SUN2
## DNAL4, NPTXR, PDGFB, RPL3, SYNGR1, TAB1, ATF4, RPS19BP1, TNRC6B, ADSL## Computing nearest neighbor graph## Computing SNN## INFO [2026-01-04 16:56:14] define_signif_tumor_subclusters(), tumor: malignant_MGH53## INFO [2026-01-04 16:56:14] Setting auto leiden resolution for malignant_MGH53 to 0.408451## Warning: Data is of class matrix. Coercing to dgCMatrix.## Finding variable features for layer counts## Centering and scaling data matrix## PC_ 1
## Positive: BANP, KLHDC4, MAP1LC3B, IRF8, COX4I1, EMC8, CRISPLD2, USP10, COTL1, SUDS3
## TAOK3, PEBP1, VSIG10, WSB2, TAF1C, RFC5, FBXO21, JRK, FBXW8, CASP2
## GSTK1, FAM131B, MTRNR2L6, RNFT2, ZFYVE27, ARC, CRTAC1, AVPI1, ZYX, R3HCC1L
## Negative: SCFD2, FIP1L1, DANCR, SGCB, DCUN1D4, OCIAD1, CHIC2, FRYL, PDGFRA, NFXL1
## SRD5A3, COMMD8, GNPDA2, TMEM165, GUF1, CLOCK, ATP8A1, EXOC1, AASDH, PPAT
## SLC30A9, PAICS, SRP72, NOA1, POLR2B, LPHN3, UBA6, YTHDC1, TMEM33, UTP3
## PC_ 2
## Positive: PLD2, PSMB6, SLC25A11, CXCL16, RNF167, ARRB2, PELP1, MED11, PFN1, MYBBP1A
## XAF1, SPAG7, UBE2G1, C17orf100, TXNDC17, RNASEK, WSCD1, ANKFY1, CAMTA2, MIS12
## C17orf49, ZFP3, DERL2, RPAIN, ZNF232, CYB5D2, ACADVL, C1QBP, ZNF594, NUP88
## Negative: COG1, FAM104A, CD300A, SSTR2, C17orf80, BTBD17, SLC39A11, SOX9, CDC42EP4, SLC9A3R1
## KCNJ16, TTYH2, RPL38, ABCA8, PRKAR1A, NAT9, AMZ2, LINC00674, KPNA2, TMEM104
## C17orf58, BPTF, FDXR, NOL11, PSMD12, HID1, HELZ, CDR2L, CACNG4, PRKCA
## PC_ 3
## Positive: APLP2, NFRKB, ZBTB44, SNX19, NTM, IGSF9B, JAM3, NCAPD3, VPS26B, THYN1
## ACAD8, B3GAT1, SYNJ2BP, MED6, PCNX, ZFYVE1, RBM25, PSEN1, NUMB, ACOT2
## PNMA1, EML1, CYP46A1, ELMSAN1, EVL, YY1, CCNK, PTGR2, FAM161B, COQ6
## Negative: FAM21C, AGAP4, PARG, ZNF485, ALOX5, VSTM4, CXCL12, ZNF32, ZNF22, ZNF239
## NCOA4, RASSF4, ARHGAP22, BMS1P1, HNRNPF, GLUD1P7, MAPK8, FAM21B, BMS1P2, BMS1P6
## AGAP9, ZNF488, BMS1P5, CSGALNACT2, TIMM23, BMS1, ZNF33B, AGAP6, ZNF37BP, HSD17B7P2
## PC_ 4
## Positive: GEMIN2, TRAPPC6B, PNN, CTAGE5, SEC23A, KLHL28, LINC00639, FAM179B, TMX1, TRIM9
## NIN, PYGL, PRPF39, MBIP, ATL1, MAP4K5, FKBP3, ATP5S, MDGA2, RPS29
## MIS18BP1, BRMS1L, RPL36AL, L2HGDH, MGAT2, DNAAF2, ARF6, NEMF, KLHDC1, KLHDC2
## Negative: TBXAS1, SLC37A3, LUC7L2, HIPK2, C7orf55, MKRN1, UBN2, NDUFB2, ZC3HAV1, BRAF
## TRIM24, AHSA2, USP34, CREB3L2, C2orf74, XPO1, MRPS33, PEX13, FAM161A, DGKI
## REL, CCT4, AGK, TMED8, PTN, VIPAS39, AHSA1, COMMD1, GSTZ1, SPTLC2
## PC_ 5
## Positive: SYNJ2BP, MED6, PCNX, ZFYVE1, RBM25, PSEN1, NUMB, ACOT2, PNMA1, ELMSAN1
## PTGR2, ZNF410, FAM161B, COQ6, ALDH6A1, LIN52, UBIAD1, MTOR, EXOSC10, SRM
## TARDBP, DFFA, PGD, KIF1B, ABCD4, UBE4B, NMNAT1, CNTLN, PSIP1, SNAPC3
## Negative: NUP88, RPAIN, RABEP1, ZNF594, C1QBP, ZNF232, DERL2, ZFP3, CAMTA2, MIS12
## SPAG7, WSCD1, PFN1, TXNDC17, RNF167, SLC25A11, PLD2, C17orf100, PSMB6, CXCL16
## XAF1, MED11, ARRB2, RNASEK, PELP1, MYBBP1A, UBE2G1, C17orf49, MARCH5, EXOC6## Computing nearest neighbor graph## Computing SNN## INFO [2026-01-04 16:56:15] define_signif_tumor_subclusters(), tumor: Microglia/Macrophage## INFO [2026-01-04 16:56:15] Less cells in group Microglia/Macrophage than k_nn setting. Keeping as a single subcluster.## INFO [2026-01-04 16:56:15] define_signif_tumor_subclusters(), tumor: Oligodendrocytes (non-malignant)## INFO [2026-01-04 16:56:15] Setting auto leiden resolution for Oligodendrocytes (non-malignant) to 0.57128## Warning: Data is of class matrix. Coercing to dgCMatrix.## Finding variable features for layer counts## Centering and scaling data matrix## PC_ 1
## Positive: CHRNB1, NLGN2, ZBTB4, TMEM256, POLR2A, GPS2, EIF4A1, EIF5A, CTDNEP1, GABARAP
## PHF23, DVL2, ACADVL, C17orf49, RNASEK, XAF1, ZNF594, RABEP1, C17orf100, ZNF232
## NUP88, TXNDC17, RPAIN, DERL2, ZFP3, MIS12, C1QBP, WSCD1, CAMTA2, AMZ1
## Negative: MRPL42, UBE2N, CRADD, EEA1, CCDC41, ACTR6, SCYL2, BTG1, TMCC3, ANO4
## ATP2B1, NDUFA12, ARL1, NR2C1, GNPTAB, POC1B, CCDC53, NUP37, DUSP6, ASCL1
## NT5DC3, C12orf29, HSP90B1, RSU1, STAM, C12orf73, FAM188A, NSUN6, CCDC59, ARL5B
## PC_ 2
## Positive: PTPN1, ADNP, TMEM189, DPM1, UBE2V1, MOCS3, ATP9A, RNF114, ZFP64, BCAS1
## SLC9A8, ZFAS1, ARFGEF2, PFDN4, CSE1L, ZNFX1, STAU1, DDX27, DOK5, CSTF1
## RTFDC1, BMP7, RAE1, MTRNR2L3, VAPB, STX16, NPEPL1, ATP5J2, ZNF789, ZNF394
## Negative: ING2, RWDD4, CDKN2AIP, AGA, TRAPPC11, SPCS3, STOX2, GPM6A, SH3D19, PET112
## RPS3A, ARFIP1, TRIM2, FBXW7, KIAA0922, DCLK2, GLRA3, TLR2, PRMT10, ZKSCAN8
## PLRG1, IRF2, TMEM184C, CTSO, SLC10A7, CEP44, LSM6, PDGFC, ZSCAN9, FBXO8
## PC_ 3
## Positive: ATP6V1H, RB1CC1, TCEA1, PCMTD1, LYPLA1, UBE2V2, MRPL15, TMEM68, TGS1, ERLIN2
## PROSC, DUSP26, BRF2, RNF122, RPS20, EIF4EBP1, MAK16, ASH2L, TTI2, FUT10
## LSM1, CHCHD7, PPP2CB, BAG4, IMPAD1, DDHD2, ZCCHC8, UBXN8, CLIP1, VPS33A
## Negative: TBK1, GNS, XPOT, LEMD3, C12orf66, LLPH, TMBIM4, CAND1, DYRK2, SRGAP1
## MDM1, RAP1B, TMEM5, NUP107, NMI, RND3, RIF1, SLC35E3, PPM1H, MMADHC
## KIF5C, ARL5A, EPC2, CACNB4, STAM2, MDM2, MON2, PAPOLA, VRK1, GSKIP
## PC_ 4
## Positive: TRPC4AP, GSS, EDEM2, EIF6, ACSS2, CEP250, CPNE1, ERGIC3, GGT7, RBM12
## NFS1, TP53INP2, ROMO1, NCOA6, RBM39, PIGU, PHF20, MAP1LC3A, DYNLRB1, SCAND1
## ITCH, LINC00657, AHCY, EPB41L1, AAR2, EIF2S2, DLGAP4, RALY, C20orf24, PXMP4
## Negative: GCC2, ST6GAL2, LIMS1, UXS1, RANBP2, NCK2, SEPT10, C2orf49, RGPD5, GPR45
## LINC00116, MRPS9, MERTK, MAP4K4, RNF149, ZC3H8, RPL31, PDCL3, TTL, CHST10
## AFF3, REV1, EIF5B, TXNDC9, MRPL30, MITD1, MGAT4A, LIPT1, NR1H3, ACP2
## PC_ 5
## Positive: GOT1, SLC25A28, CUTC, COX15, DNMBP, HPS1, ERLIN1, CHUK, CWF19L1, BLOC1S2
## SFXN2, WBP1L, SCD, ARL3, R3HCC1L, C10orf32, TRIM8, LINC00263, AS3MT, NT5C2
## DCTPP1, LDB1, SEC31B, ACTR1A, PPRC1, NOLC1, GBF1, INA, FBXL15, TMEM180
## Negative: MED28, DCAF16, LCORL, PACRGL, LAP3, DHX15, ANAPC4, QDPR, RBPJ, TBC1D19
## TAPT1-AS1, ARAP2, RELL1, FAM200B, PGM2, FBXL5, KLF3, TLR1, CC2D2A, TMEM156
## RAB28, KLHL5, WDR1, RFC1, RPL9, ACOX3, LIAS, SH3TC1, UGDH, UGDH-AS1## Computing nearest neighbor graph## Computing SNN## INFO [2026-01-04 16:56:17] ::plot_cnv:Start## INFO [2026-01-04 16:56:17] ::plot_cnv:Current data dimensions (r,c)=8508,184 Total=1565375.78852005 Min=0.530812412297805 Max=1.98172179650671.## INFO [2026-01-04 16:56:17] ::plot_cnv:Depending on the size of the matrix this may take a moment.## INFO [2026-01-04 16:56:17] plot_cnv(): auto thresholding at: (0.649779 , 1.350098)## INFO [2026-01-04 16:56:17] plot_cnv_observation:Start## INFO [2026-01-04 16:56:17] Observation data size: Cells= 142 Genes= 8508## INFO [2026-01-04 16:56:17] clustering observations via method: ward.D## INFO [2026-01-04 16:56:17] Number of cells in group(1) is 40## INFO [2026-01-04 16:56:17] group size being clustered: 40,8508## INFO [2026-01-04 16:56:17] Number of cells in group(2) is 35## INFO [2026-01-04 16:56:17] group size being clustered: 35,8508## INFO [2026-01-04 16:56:17] Number of cells in group(3) is 33## INFO [2026-01-04 16:56:17] group size being clustered: 33,8508## INFO [2026-01-04 16:56:17] Number of cells in group(4) is 34## INFO [2026-01-04 16:56:17] group size being clustered: 34,8508## INFO [2026-01-04 16:56:17] plot_cnv_observation:Writing observation groupings/color.## INFO [2026-01-04 16:56:17] plot_cnv_observation:Done writing observation groupings/color.## INFO [2026-01-04 16:56:17] plot_cnv_observation:Writing observation heatmap thresholds.## INFO [2026-01-04 16:56:17] plot_cnv_observation:Done writing observation heatmap thresholds.## INFO [2026-01-04 16:56:17] Colors for breaks: #00008B,#24249B,#4848AB,#6D6DBC,#9191CC,#B6B6DD,#DADAEE,#FFFFFF,#EEDADA,#DDB6B6,#CC9191,#BC6D6D,#AB4848,#9B2424,#8B0000## INFO [2026-01-04 16:56:17] Quantiles of plotted data range: 0.649778667373698,0.930913786150194,1,1.06475965837873,1.35009841572973## INFO [2026-01-04 16:56:17] plot_cnv_references:Start## INFO [2026-01-04 16:56:17] Reference data size: Cells= 42 Genes= 8508## INFO [2026-01-04 16:56:17] plot_cnv_references:Number reference groups= 2## INFO [2026-01-04 16:56:17] plot_cnv_references:Plotting heatmap.## INFO [2026-01-04 16:56:17] Colors for breaks: #00008B,#24249B,#4848AB,#6D6DBC,#9191CC,#B6B6DD,#DADAEE,#FFFFFF,#EEDADA,#DDB6B6,#CC9191,#BC6D6D,#AB4848,#9B2424,#8B0000## INFO [2026-01-04 16:56:17] Quantiles of plotted data range: 0.693386142642158,0.960381368823128,1,1.04021212541359,1.35009841572973## INFO [2026-01-04 16:56:19]
##
## STEP 22: Denoising## INFO [2026-01-04 16:56:19] ::process_data:Remove noise, noise threshold defined via ref mean sd_amplifier: 1.5## INFO [2026-01-04 16:56:19] denoising using mean(normal) +- sd_amplifier * sd(normal) per gene per cell across all data## INFO [2026-01-04 16:56:19] :: **** clear_noise_via_ref_quantiles **** : removing noise between bounds: 0.886597398348459 - 1.12128464942808## INFO [2026-01-04 16:56:20]
##
## ## Making the final infercnv heatmap #### INFO [2026-01-04 16:56:21] ::plot_cnv:Start## INFO [2026-01-04 16:56:21] ::plot_cnv:Current data dimensions (r,c)=8508,184 Total=1569112.28502327 Min=0.530812412297805 Max=1.98172179650671.## INFO [2026-01-04 16:56:21] ::plot_cnv:Depending on the size of the matrix this may take a moment.## INFO [2026-01-04 16:56:21] plot_cnv(): auto thresholding at: (0.649902 , 1.350098)## INFO [2026-01-04 16:56:21] plot_cnv_observation:Start## INFO [2026-01-04 16:56:21] Observation data size: Cells= 142 Genes= 8508## INFO [2026-01-04 16:56:21] plot_cnv_observation:Writing observation groupings/color.## INFO [2026-01-04 16:56:21] plot_cnv_observation:Done writing observation groupings/color.## INFO [2026-01-04 16:56:21] plot_cnv_observation:Writing observation heatmap thresholds.## INFO [2026-01-04 16:56:21] plot_cnv_observation:Done writing observation heatmap thresholds.## INFO [2026-01-04 16:56:21] Colors for breaks: #00008B,#24249B,#4848AB,#6D6DBC,#9191CC,#B6B6DD,#DADAEE,#FFFFFF,#EEDADA,#DDB6B6,#CC9191,#BC6D6D,#AB4848,#9B2424,#8B0000## INFO [2026-01-04 16:56:21] Quantiles of plotted data range: 0.649901584270269,1.00394102388827,1.00394102388827,1.00394102388827,1.35009841572973## INFO [2026-01-04 16:56:21] plot_cnv_references:Start## INFO [2026-01-04 16:56:21] Reference data size: Cells= 42 Genes= 8508## INFO [2026-01-04 16:56:21] plot_cnv_references:Number reference groups= 2## INFO [2026-01-04 16:56:21] plot_cnv_references:Plotting heatmap.## INFO [2026-01-04 16:56:21] Colors for breaks: #00008B,#24249B,#4848AB,#6D6DBC,#9191CC,#B6B6DD,#DADAEE,#FFFFFF,#EEDADA,#DDB6B6,#CC9191,#BC6D6D,#AB4848,#9B2424,#8B0000## INFO [2026-01-04 16:56:21] Quantiles of plotted data range: 0.693386142642158,1.00394102388827,1.00394102388827,1.00394102388827,1.35009841572973Basic ouput from running inferCNV.
Additional Information
Online Documentation
For additional explanations on files, usage, and a tutorial please visit the wiki.
TrinityCTAT
This tool is a part of the TrinityCTAT toolkit focused on leveraging the use of RNA-Seq to better understand cancer transcriptomes. To find out more please visit TrinityCTAT
Applications
This methodology was used in:
Session info
## R version 4.5.2 (2025-10-31)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.3 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so; LAPACK version 3.12.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: Etc/UTC
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] future_1.68.0 infercnv_1.27.0 BiocStyle_2.39.0
##
## loaded via a namespace (and not attached):
## [1] RcppAnnoy_0.0.22 splines_4.5.2
## [3] later_1.4.4 bitops_1.0-9
## [5] tibble_3.3.0 polyclip_1.10-7
## [7] fastDummies_1.7.5 lifecycle_1.0.4
## [9] fastcluster_1.3.0 edgeR_4.9.2
## [11] doParallel_1.0.17 globals_0.18.0
## [13] lattice_0.22-7 MASS_7.3-65
## [15] magrittr_2.0.4 limma_3.67.0
## [17] plotly_4.11.0 sass_0.4.10
## [19] rmarkdown_2.30 jquerylib_0.1.4
## [21] yaml_2.3.12 httpuv_1.6.16
## [23] otel_0.2.0 Seurat_5.4.0
## [25] sctransform_0.4.2 spam_2.11-1
## [27] sp_2.2-0 spatstat.sparse_3.1-0
## [29] reticulate_1.44.1 cowplot_1.2.0
## [31] pbapply_1.7-4 buildtools_1.0.0
## [33] RColorBrewer_1.1-3 multcomp_1.4-29
## [35] abind_1.4-8 Rtsne_0.17
## [37] GenomicRanges_1.63.1 purrr_1.2.0
## [39] BiocGenerics_0.57.0 TH.data_1.1-5
## [41] sandwich_3.1-1 IRanges_2.45.0
## [43] S4Vectors_0.49.0 ggrepel_0.9.6
## [45] irlba_2.3.5.1 listenv_0.10.0
## [47] spatstat.utils_3.2-0 maketools_1.3.2
## [49] goftest_1.2-3 RSpectra_0.16-2
## [51] spatstat.random_3.4-3 fitdistrplus_1.2-4
## [53] parallelly_1.46.0 codetools_0.2-20
## [55] coin_1.4-3 DelayedArray_0.37.0
## [57] tidyselect_1.2.1 futile.logger_1.4.9
## [59] farver_2.1.2 rjags_4-17
## [61] matrixStats_1.5.0 stats4_4.5.2
## [63] spatstat.explore_3.6-0 Seqinfo_1.1.0
## [65] jsonlite_2.0.0 progressr_0.18.0
## [67] ggridges_0.5.7 survival_3.8-3
## [69] iterators_1.0.14 foreach_1.5.2
## [71] tools_4.5.2 ica_1.0-3
## [73] Rcpp_1.1.0.8.1 glue_1.8.0
## [75] gridExtra_2.3 SparseArray_1.11.10
## [77] xfun_0.55 MatrixGenerics_1.23.0
## [79] dplyr_1.1.4 formatR_1.14
## [81] BiocManager_1.30.27 fastmap_1.2.0
## [83] caTools_1.18.3 digest_0.6.39
## [85] parallelDist_0.2.7 R6_2.6.1
## [87] mime_0.13 scattermore_1.2
## [89] gtools_3.9.5 tensor_1.5.1
## [91] spatstat.data_3.1-9 tidyr_1.3.2
## [93] generics_0.1.4 data.table_1.18.0
## [95] httr_1.4.7 htmlwidgets_1.6.4
## [97] S4Arrays_1.11.1 uwot_0.2.4
## [99] pkgconfig_2.0.3 gtable_0.3.6
## [101] modeltools_0.2-24 lmtest_0.9-40
## [103] S7_0.2.1 SingleCellExperiment_1.33.0
## [105] XVector_0.51.0 sys_3.4.3
## [107] htmltools_0.5.9 dotCall64_1.2
## [109] SeuratObject_5.3.0 scales_1.4.0
## [111] Biobase_2.71.0 png_0.1-8
## [113] phyclust_0.1-34 spatstat.univar_3.1-5
## [115] knitr_1.51 lambda.r_1.2.4
## [117] reshape2_1.4.5 coda_0.19-4.1
## [119] nlme_3.1-168 cachem_1.1.0
## [121] zoo_1.8-15 stringr_1.6.0
## [123] KernSmooth_2.23-26 parallel_4.5.2
## [125] miniUI_0.1.2 libcoin_1.0-10
## [127] pillar_1.11.1 grid_4.5.2
## [129] vctrs_0.6.5 gplots_3.3.0
## [131] RANN_2.6.2 promises_1.5.0
## [133] xtable_1.8-4 cluster_2.1.8.1
## [135] evaluate_1.0.5 mvtnorm_1.3-3
## [137] cli_3.6.5 locfit_1.5-9.12
## [139] compiler_4.5.2 futile.options_1.0.1
## [141] rlang_1.1.6 future.apply_1.20.1
## [143] argparse_2.3.1 plyr_1.8.9
## [145] stringi_1.8.7 viridisLite_0.4.2
## [147] deldir_2.0-4 lazyeval_0.2.2
## [149] spatstat.geom_3.6-1 Matrix_1.7-4
## [151] RcppHNSW_0.6.0 patchwork_1.3.2
## [153] ggplot2_4.0.1 statmod_1.5.1
## [155] shiny_1.12.1 SummarizedExperiment_1.41.0
## [157] ROCR_1.0-11 igraph_2.2.1
## [159] RcppParallel_5.1.11-1 bslib_0.9.0
## [161] ape_5.8-1