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broadinstitute / infercnv

Inferring CNV from Single-Cell RNA-Seq
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Errors when ran BayesNet with diagnostics = TRUE #252

Closed liu-xingliang closed 4 years ago

liu-xingliang commented 4 years ago

Error messages:

        STEP 18: Run Bayesian Network Model on HMM predicted CNV's

INFO [2020-08-24 12:04:12] Creating the following Directory:  TTK/BayesNetOutput.HMMi6.hmm_mode-samples
INFO [2020-08-24 12:04:12] Initializing new MCM InferCNV Object.
INFO [2020-08-24 12:04:12] validating infercnv_obj
INFO [2020-08-24 12:04:13] Total CNV's:  1360
INFO [2020-08-24 12:04:13] Loading BUGS Model.
INFO [2020-08-24 12:04:15] Running Sampling Using Parallel with  8 Cores

INFO [2020-08-24 13:51:15] Obtaining probabilities post-sampling
INFO [2020-08-24 14:04:05] Gibbs sampling time:  119.831170745691  Minutes
INFO [2020-08-24 14:05:17] Creating Diagnostic Plots.
Error in lapply(seq_along(mcmc), function(i) { :
  argument "mcmc" is missing, with no default

Session info:

> sessionInfo()
R version 4.0.1 (2020-06-06)
Platform: x86_64-conda_cos6-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)

Matrix products: default
BLAS/LAPACK: /home/anaconda3/envs/R401/lib/libmkl_rt.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8
 [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

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base

other attached packages:
[1] infercnv_1.4.0

loaded via a namespace (and not attached):
 [1] Biobase_2.48.0              tidyr_1.1.0
 [3] edgeR_3.30.3                jsonlite_1.6.1
 [5] splines_4.0.1               foreach_1.5.0
 [7] gtools_3.8.2                argparse_2.0.1
 [9] stats4_4.0.1                HiddenMarkov_1.8-11
[11] coin_1.3-1                  GenomeInfoDbData_1.2.3
[13] globals_0.12.5              pillar_1.4.4
[15] lattice_0.20-41             glue_1.4.1
[17] limma_3.44.2                digest_0.6.25
[19] GenomicRanges_1.40.0        RColorBrewer_1.1-2
[21] XVector_0.28.0              colorspace_1.4-1
[23] sandwich_2.5-1              plyr_1.8.6
[25] Matrix_1.2-18               pkgconfig_2.0.3
[27] listenv_0.8.0               zlibbioc_1.34.0
[29] purrr_0.3.4                 mvtnorm_1.1-1
[31] scales_1.1.1                gdata_2.18.0
[33] rjags_4-10                  tibble_3.0.1
[35] generics_0.0.2              IRanges_2.22.2
[37] ggplot2_3.3.1               ellipsis_0.3.1
[39] TH.data_1.0-10              SummarizedExperiment_1.18.1
[41] fastcluster_1.1.25          BiocGenerics_0.34.0
[43] survival_3.1-12             magrittr_1.5
[45] crayon_1.3.4                future_1.17.0
[47] doParallel_1.0.15           nlme_3.1-148
[49] MASS_7.3-51.6               gplots_3.0.3
[51] tools_4.0.1                 fitdistrplus_1.1-1
[53] formatR_1.7                 lifecycle_0.2.0
[55] matrixStats_0.56.0          multcomp_1.4-13
[57] S4Vectors_0.26.1            findpython_1.0.5
[59] munsell_0.5.0               locfit_1.5-9.4
[61] DelayedArray_0.14.0         lambda.r_1.2.4
[63] compiler_4.0.1              GenomeInfoDb_1.24.0
[65] caTools_1.18.0              rlang_0.4.6
[67] futile.logger_1.4.3         grid_4.0.1
[69] RCurl_1.98-1.2              iterators_1.0.12
[71] SingleCellExperiment_1.10.1 bitops_1.0-6
[73] gtable_0.3.0                codetools_0.2-16
[75] reshape_0.8.8               R6_2.4.1
[77] gridExtra_2.3               zoo_1.8-8
[79] dplyr_1.0.0                 libcoin_1.0-5
[81] futile.options_1.0.1        KernSmooth_2.23-17
[83] ape_5.4                     modeltools_0.2-23
[85] parallel_4.0.1              Rcpp_1.0.4.6
[87] vctrs_0.3.1                 tidyselect_1.1.0
[89] coda_0.19-3

Full message:

INFO [2020-08-24 11:12:54] Parsing matrix: ../../rerun.v140.meganormal/TTK.matrix
INFO [2020-08-24 11:13:52] Parsing gene order file: ../../../refdata-cellranger-GRCh38-3.0.0.gene_pos.chr_prefix.txt
INFO [2020-08-24 11:13:52] Parsing cell annotations file: ../TTK.anno
INFO [2020-08-24 11:13:54] ::order_reduce:Start.
INFO [2020-08-24 11:13:54] .order_reduce(): expr and order match.
INFO [2020-08-24 11:13:55] ::process_data:order_reduce:Reduction from positional data, new dimensions (r,c) = 19838,8315 Total=113531359 Min=0 Max=14337.
INFO [2020-08-24 11:13:55] num genes removed taking into account provided gene ordering list: 695 = 3.50337735658837% removed.
INFO [2020-08-24 11:13:59] validating infercnv_obj
INFO [2020-08-24 11:13:59] ::process_data:Start
INFO [2020-08-24 11:13:59] Creating output path TTK
INFO [2020-08-24 11:13:59]

        STEP 1: incoming data

INFO [2020-08-24 11:14:32]

        STEP 02: Removing lowly expressed genes

INFO [2020-08-24 11:14:32] ::above_min_mean_expr_cutoff:Start
INFO [2020-08-24 11:14:33] Removing 10875 genes from matrix as below mean expr threshold: 0.1
INFO [2020-08-24 11:14:33] validating infercnv_obj
INFO [2020-08-24 11:14:33] There are 8268 genes and 8315 cells remaining in the expr matrix.
INFO [2020-08-24 11:14:37] no genes removed due to min cells/gene filter
INFO [2020-08-24 11:15:00]

        STEP 03: normalization by sequencing depth

INFO [2020-08-24 11:15:00] normalizing counts matrix by depth
INFO [2020-08-24 11:15:03] Computed total sum normalization factor as median libsize: 11414.000000
INFO [2020-08-24 11:15:04] Adding h-spike
INFO [2020-08-24 11:15:04] -hspike modeling of N_L
(Error in nls(y ~ .logistic_midpt_slope(x, midpt = x0, slope = k), data = df, : step factor 0.000488281 reduced below 'minFactor' of 0.000976562
), couldn't fit logistic, but no worries, going to use a spline
(Error in nls(y ~ .logistic_midpt_slope(x, midpt = x0, slope = k), data = df, : number of iterations exceeded maximum of 50
), couldn't fit logistic, but no worries, going to use a spline
INFO [2020-08-24 11:18:08] -hspike modeling of N_RB
(Error in nls(y ~ .logistic_midpt_slope(x, midpt = x0, slope = k), data = df, : number of iterations exceeded maximum of 50
), couldn't fit logistic, but no worries, going to use a spline
(Error in nls(y ~ .logistic_midpt_slope(x, midpt = x0, slope = k), data = df, : number of iterations exceeded maximum of 50
), couldn't fit logistic, but no worries, going to use a spline
INFO [2020-08-24 11:21:02] validating infercnv_obj
INFO [2020-08-24 11:21:02] normalizing counts matrix by depth
INFO [2020-08-24 11:21:02] Using specified normalization factor: 11414.000000
INFO [2020-08-24 11:21:28]

        STEP 04: log transformation of data

INFO [2020-08-24 11:21:28] transforming log2xplus1()
INFO [2020-08-24 11:21:31] -mirroring for hspike
INFO [2020-08-24 11:21:31] transforming log2xplus1()
INFO [2020-08-24 11:21:58]

        STEP 08: removing average of reference data (before smoothing)

INFO [2020-08-24 11:21:58] ::subtract_ref_expr_from_obs:Start inv_log=FALSE, use_bounds=TRUE
INFO [2020-08-24 11:21:58] subtracting mean(normal) per gene per cell across all data
INFO [2020-08-24 11:22:07] -subtracting expr per gene, use_bounds=TRUE
INFO [2020-08-24 11:22:16] -mirroring for hspike
INFO [2020-08-24 11:22:16] ::subtract_ref_expr_from_obs:Start inv_log=FALSE, use_bounds=TRUE
INFO [2020-08-24 11:22:16] subtracting mean(normal) per gene per cell across all data
INFO [2020-08-24 11:22:22] -subtracting expr per gene, use_bounds=TRUE
INFO [2020-08-24 11:23:08]

        STEP 09: apply max centered expression threshold: 3

INFO [2020-08-24 11:23:08] ::process_data:setting max centered expr, threshold set to: +/-:  3
INFO [2020-08-24 11:23:12] -mirroring for hspike
INFO [2020-08-24 11:23:12] ::process_data:setting max centered expr, threshold set to: +/-:  3
INFO [2020-08-24 11:23:58]

        STEP 10: Smoothing data per cell by chromosome

INFO [2020-08-24 11:23:58] smooth_by_chromosome: chr: chr1
INFO [2020-08-24 11:24:17] smooth_by_chromosome: chr: chr2
INFO [2020-08-24 11:24:31] smooth_by_chromosome: chr: chr3
INFO [2020-08-24 11:24:44] smooth_by_chromosome: chr: chr4
INFO [2020-08-24 11:24:56] smooth_by_chromosome: chr: chr5
INFO [2020-08-24 11:25:08] smooth_by_chromosome: chr: chr6
INFO [2020-08-24 11:25:23] smooth_by_chromosome: chr: chr7
INFO [2020-08-24 11:25:36] smooth_by_chromosome: chr: chr8
INFO [2020-08-24 11:25:49] smooth_by_chromosome: chr: chr9
INFO [2020-08-24 11:26:02] smooth_by_chromosome: chr: chr11
INFO [2020-08-24 11:26:16] smooth_by_chromosome: chr: chr10
INFO [2020-08-24 11:26:27] smooth_by_chromosome: chr: chr12
INFO [2020-08-24 11:26:39] smooth_by_chromosome: chr: chr13
INFO [2020-08-24 11:26:49] smooth_by_chromosome: chr: chr14
INFO [2020-08-24 11:27:01] smooth_by_chromosome: chr: chr15
INFO [2020-08-24 11:27:11] smooth_by_chromosome: chr: chr16
INFO [2020-08-24 11:27:22] smooth_by_chromosome: chr: chr17
INFO [2020-08-24 11:27:34] smooth_by_chromosome: chr: chr18
INFO [2020-08-24 11:27:45] smooth_by_chromosome: chr: chr20
INFO [2020-08-24 11:27:55] smooth_by_chromosome: chr: chr19
INFO [2020-08-24 11:28:10] smooth_by_chromosome: chr: chr22
INFO [2020-08-24 11:28:21] smooth_by_chromosome: chr: chr21
INFO [2020-08-24 11:28:28] -mirroring for hspike
INFO [2020-08-24 11:28:28] smooth_by_chromosome: chr: chrA
INFO [2020-08-24 11:28:29] smooth_by_chromosome: chr: chr_0
INFO [2020-08-24 11:28:30] smooth_by_chromosome: chr: chr_B
INFO [2020-08-24 11:28:30] smooth_by_chromosome: chr: chr_0pt5
INFO [2020-08-24 11:28:31] smooth_by_chromosome: chr: chr_C
INFO [2020-08-24 11:28:31] smooth_by_chromosome: chr: chr_1pt5
INFO [2020-08-24 11:28:32] smooth_by_chromosome: chr: chr_D
INFO [2020-08-24 11:28:32] smooth_by_chromosome: chr: chr_2pt0
INFO [2020-08-24 11:28:33] smooth_by_chromosome: chr: chr_E
INFO [2020-08-24 11:28:33] smooth_by_chromosome: chr: chr_3pt0
INFO [2020-08-24 11:28:34] smooth_by_chromosome: chr: chr_F
INFO [2020-08-24 11:29:21]

        STEP 11: re-centering data across chromosome after smoothing

INFO [2020-08-24 11:29:21] ::center_smooth across chromosomes per cell
INFO [2020-08-24 11:29:42] -mirroring for hspike
INFO [2020-08-24 11:29:42] ::center_smooth across chromosomes per cell
INFO [2020-08-24 11:30:31]

        STEP 12: removing average of reference data (after smoothing)

INFO [2020-08-24 11:30:31] ::subtract_ref_expr_from_obs:Start inv_log=FALSE, use_bounds=TRUE
INFO [2020-08-24 11:30:31] subtracting mean(normal) per gene per cell across all data
INFO [2020-08-24 11:30:38] -subtracting expr per gene, use_bounds=TRUE
INFO [2020-08-24 11:30:46] -mirroring for hspike
INFO [2020-08-24 11:30:46] ::subtract_ref_expr_from_obs:Start inv_log=FALSE, use_bounds=TRUE
INFO [2020-08-24 11:30:46] subtracting mean(normal) per gene per cell across all data
INFO [2020-08-24 11:30:51] -subtracting expr per gene, use_bounds=TRUE
INFO [2020-08-24 11:31:36]

        STEP 14: invert log2(FC) to FC

INFO [2020-08-24 11:31:36] invert_log2(), computing 2^x
INFO [2020-08-24 11:31:41] -mirroring for hspike
INFO [2020-08-24 11:31:41] invert_log2(), computing 2^x
INFO [2020-08-24 11:32:37]

        STEP 15: Clustering samples (not defining tumor subclusters)

INFO [2020-08-24 11:32:37] define_signif_tumor_subclusters(p_val=0.1
INFO [2020-08-24 11:32:37] define_signif_tumor_subclusters(), tumor: 0
INFO [2020-08-24 11:32:37] cut tree into: 1 groups
INFO [2020-08-24 11:32:37] -processing 0,0_s1
INFO [2020-08-24 11:32:37] define_signif_tumor_subclusters(), tumor: 2
INFO [2020-08-24 11:32:37] cut tree into: 1 groups
INFO [2020-08-24 11:32:37] -processing 2,2_s1
INFO [2020-08-24 11:32:37] define_signif_tumor_subclusters(), tumor: 3
INFO [2020-08-24 11:32:37] cut tree into: 1 groups
INFO [2020-08-24 11:32:37] -processing 3,3_s1
INFO [2020-08-24 11:32:37] define_signif_tumor_subclusters(), tumor: 4
INFO [2020-08-24 11:32:38] cut tree into: 1 groups
INFO [2020-08-24 11:32:38] -processing 4,4_s1
INFO [2020-08-24 11:32:38] define_signif_tumor_subclusters(), tumor: 5
INFO [2020-08-24 11:32:38] cut tree into: 1 groups
INFO [2020-08-24 11:32:38] -processing 5,5_s1
INFO [2020-08-24 11:32:38] define_signif_tumor_subclusters(), tumor: 7
INFO [2020-08-24 11:32:38] cut tree into: 1 groups
INFO [2020-08-24 11:32:38] -processing 7,7_s1
INFO [2020-08-24 11:32:38] define_signif_tumor_subclusters(), tumor: 8
INFO [2020-08-24 11:32:40] cut tree into: 1 groups
INFO [2020-08-24 11:32:40] -processing 8,8_s1
INFO [2020-08-24 11:32:40] define_signif_tumor_subclusters(), tumor: 9
INFO [2020-08-24 11:33:15] cut tree into: 1 groups
INFO [2020-08-24 11:33:15] -processing 9,9_s1
INFO [2020-08-24 11:33:15] define_signif_tumor_subclusters(), tumor: 10
INFO [2020-08-24 11:33:16] cut tree into: 1 groups
INFO [2020-08-24 11:33:16] -processing 10,10_s1
INFO [2020-08-24 11:33:16] define_signif_tumor_subclusters(), tumor: 11
INFO [2020-08-24 11:33:17] cut tree into: 1 groups
INFO [2020-08-24 11:33:17] -processing 11,11_s1
INFO [2020-08-24 11:33:17] define_signif_tumor_subclusters(), tumor: 15
INFO [2020-08-24 11:33:17] cut tree into: 1 groups
INFO [2020-08-24 11:33:17] -processing 15,15_s1
INFO [2020-08-24 11:33:17] define_signif_tumor_subclusters(), tumor: 16
INFO [2020-08-24 11:33:17] cut tree into: 1 groups
INFO [2020-08-24 11:33:17] -processing 16,16_s1
INFO [2020-08-24 11:33:17] define_signif_tumor_subclusters(), tumor: 17
INFO [2020-08-24 11:34:08] cut tree into: 1 groups
INFO [2020-08-24 11:34:08] -processing 17,17_s1
INFO [2020-08-24 11:34:08] define_signif_tumor_subclusters(), tumor: 18
INFO [2020-08-24 11:34:09] cut tree into: 1 groups
INFO [2020-08-24 11:34:09] -processing 18,18_s1
INFO [2020-08-24 11:34:09] define_signif_tumor_subclusters(), tumor: 19
INFO [2020-08-24 11:34:10] cut tree into: 1 groups
INFO [2020-08-24 11:34:10] -processing 19,19_s1
INFO [2020-08-24 11:34:10] define_signif_tumor_subclusters(), tumor: 21
INFO [2020-08-24 11:34:10] cut tree into: 1 groups
INFO [2020-08-24 11:34:10] -processing 21,21_s1
INFO [2020-08-24 11:34:10] define_signif_tumor_subclusters(), tumor: 22
INFO [2020-08-24 11:34:10] cut tree into: 1 groups
INFO [2020-08-24 11:34:10] -processing 22,22_s1
INFO [2020-08-24 11:34:10] define_signif_tumor_subclusters(), tumor: 23
INFO [2020-08-24 11:34:11] cut tree into: 1 groups
INFO [2020-08-24 11:34:11] -processing 23,23_s1
INFO [2020-08-24 11:34:11] define_signif_tumor_subclusters(), tumor: 24
INFO [2020-08-24 11:36:42] cut tree into: 1 groups
INFO [2020-08-24 11:36:42] -processing 24,24_s1
INFO [2020-08-24 11:36:42] define_signif_tumor_subclusters(), tumor: 29
INFO [2020-08-24 11:36:43] cut tree into: 1 groups
INFO [2020-08-24 11:36:43] -processing 29,29_s1
INFO [2020-08-24 11:36:43] define_signif_tumor_subclusters(), tumor: 31
INFO [2020-08-24 11:36:43] cut tree into: 1 groups
INFO [2020-08-24 11:36:43] -processing 31,31_s1
INFO [2020-08-24 11:36:43] define_signif_tumor_subclusters(), tumor: 32
INFO [2020-08-24 11:36:43] cut tree into: 1 groups
INFO [2020-08-24 11:36:43] -processing 32,32_s1
INFO [2020-08-24 11:36:43] define_signif_tumor_subclusters(), tumor: 33
INFO [2020-08-24 11:36:43] cut tree into: 1 groups
INFO [2020-08-24 11:36:43] -processing 33,33_s1
INFO [2020-08-24 11:36:43] define_signif_tumor_subclusters(), tumor: 38
INFO [2020-08-24 11:36:43] cut tree into: 1 groups
INFO [2020-08-24 11:36:43] -processing 38,38_s1
INFO [2020-08-24 11:36:43] define_signif_tumor_subclusters(), tumor: 40
INFO [2020-08-24 11:36:44] cut tree into: 1 groups
INFO [2020-08-24 11:36:44] -processing 40,40_s1
INFO [2020-08-24 11:36:44] define_signif_tumor_subclusters(), tumor: 41
INFO [2020-08-24 11:36:44] cut tree into: 1 groups
INFO [2020-08-24 11:36:44] -processing 41,41_s1
INFO [2020-08-24 11:36:44] define_signif_tumor_subclusters(), tumor: 42
INFO [2020-08-24 11:36:47] cut tree into: 1 groups
INFO [2020-08-24 11:36:47] -processing 42,42_s1
INFO [2020-08-24 11:36:47] define_signif_tumor_subclusters(), tumor: 43
INFO [2020-08-24 11:36:48] cut tree into: 1 groups
INFO [2020-08-24 11:36:48] -processing 43,43_s1
INFO [2020-08-24 11:36:48] define_signif_tumor_subclusters(), tumor: 44
INFO [2020-08-24 11:36:49] cut tree into: 1 groups
INFO [2020-08-24 11:36:49] -processing 44,44_s1
INFO [2020-08-24 11:36:49] define_signif_tumor_subclusters(), tumor: 46
INFO [2020-08-24 11:36:49] cut tree into: 1 groups
INFO [2020-08-24 11:36:49] -processing 46,46_s1
INFO [2020-08-24 11:36:49] define_signif_tumor_subclusters(), tumor: 47
INFO [2020-08-24 11:36:59] cut tree into: 1 groups
INFO [2020-08-24 11:36:59] -processing 47,47_s1
INFO [2020-08-24 11:36:59] define_signif_tumor_subclusters(), tumor: 50
INFO [2020-08-24 11:36:59] cut tree into: 1 groups
INFO [2020-08-24 11:36:59] -processing 50,50_s1
INFO [2020-08-24 11:36:59] define_signif_tumor_subclusters(), tumor: 52
INFO [2020-08-24 11:37:00] cut tree into: 1 groups
INFO [2020-08-24 11:37:00] -processing 52,52_s1
INFO [2020-08-24 11:37:00] define_signif_tumor_subclusters(), tumor: 53
INFO [2020-08-24 11:37:00] cut tree into: 1 groups
INFO [2020-08-24 11:37:00] -processing 53,53_s1
INFO [2020-08-24 11:37:00] define_signif_tumor_subclusters(), tumor: 55
INFO [2020-08-24 11:37:00] cut tree into: 1 groups
INFO [2020-08-24 11:37:00] -processing 55,55_s1
INFO [2020-08-24 11:37:00] define_signif_tumor_subclusters(), tumor: 56
INFO [2020-08-24 11:37:02] cut tree into: 1 groups
INFO [2020-08-24 11:37:02] -processing 56,56_s1
INFO [2020-08-24 11:37:02] define_signif_tumor_subclusters(), tumor: 57
INFO [2020-08-24 11:37:06] cut tree into: 1 groups
INFO [2020-08-24 11:37:06] -processing 57,57_s1
INFO [2020-08-24 11:37:06] define_signif_tumor_subclusters(), tumor: 58
INFO [2020-08-24 11:37:07] cut tree into: 1 groups
INFO [2020-08-24 11:37:07] -processing 58,58_s1
INFO [2020-08-24 11:37:07] define_signif_tumor_subclusters(), tumor: 59
INFO [2020-08-24 11:37:07] cut tree into: 1 groups
INFO [2020-08-24 11:37:07] -processing 59,59_s1
INFO [2020-08-24 11:37:07] define_signif_tumor_subclusters(), tumor: 60
INFO [2020-08-24 11:37:07] cut tree into: 1 groups
INFO [2020-08-24 11:37:07] -processing 60,60_s1
INFO [2020-08-24 11:37:07] define_signif_tumor_subclusters(), tumor: 62
INFO [2020-08-24 11:37:07] cut tree into: 1 groups
INFO [2020-08-24 11:37:07] -processing 62,62_s1
INFO [2020-08-24 11:37:07] define_signif_tumor_subclusters(), tumor: 63
INFO [2020-08-24 11:37:07] cut tree into: 1 groups
INFO [2020-08-24 11:37:07] -processing 63,63_s1
INFO [2020-08-24 11:37:07] define_signif_tumor_subclusters(), tumor: 65
INFO [2020-08-24 11:37:08] cut tree into: 1 groups
INFO [2020-08-24 11:37:08] -processing 65,65_s1
INFO [2020-08-24 11:37:08] define_signif_tumor_subclusters(), tumor: 66
INFO [2020-08-24 11:37:08] cut tree into: 1 groups
INFO [2020-08-24 11:37:08] -processing 66,66_s1
INFO [2020-08-24 11:37:08] define_signif_tumor_subclusters(), tumor: 67
INFO [2020-08-24 11:37:08] cut tree into: 1 groups
INFO [2020-08-24 11:37:08] -processing 67,67_s1
INFO [2020-08-24 11:37:08] define_signif_tumor_subclusters(), tumor: 69
INFO [2020-08-24 11:37:08] cut tree into: 1 groups
INFO [2020-08-24 11:37:08] -processing 69,69_s1
INFO [2020-08-24 11:37:08] define_signif_tumor_subclusters(), tumor: 71
INFO [2020-08-24 11:37:08] cut tree into: 1 groups
INFO [2020-08-24 11:37:08] -processing 71,71_s1
INFO [2020-08-24 11:37:08] define_signif_tumor_subclusters(), tumor: 72
INFO [2020-08-24 11:37:08] cut tree into: 1 groups
INFO [2020-08-24 11:37:08] -processing 72,72_s1
INFO [2020-08-24 11:37:08] define_signif_tumor_subclusters(), tumor: 73
INFO [2020-08-24 11:37:08] cut tree into: 1 groups
INFO [2020-08-24 11:37:08] -processing 73,73_s1
INFO [2020-08-24 11:37:08] define_signif_tumor_subclusters(), tumor: 74
INFO [2020-08-24 11:37:08] cut tree into: 1 groups
INFO [2020-08-24 11:37:08] -processing 74,74_s1
INFO [2020-08-24 11:37:08] define_signif_tumor_subclusters(), tumor: 75
INFO [2020-08-24 11:37:08] cut tree into: 1 groups
INFO [2020-08-24 11:37:08] -processing 75,75_s1
INFO [2020-08-24 11:37:08] define_signif_tumor_subclusters(), tumor: 76
INFO [2020-08-24 11:37:08] cut tree into: 1 groups
INFO [2020-08-24 11:37:08] -processing 76,76_s1
INFO [2020-08-24 11:37:08] define_signif_tumor_subclusters(), tumor: N_L
INFO [2020-08-24 11:38:15] cut tree into: 1 groups
INFO [2020-08-24 11:38:15] -processing N_L,N_L_s1
INFO [2020-08-24 11:38:15] define_signif_tumor_subclusters(), tumor: N_RB
INFO [2020-08-24 11:39:41] cut tree into: 1 groups
INFO [2020-08-24 11:39:41] -processing N_RB,N_RB_s1
INFO [2020-08-24 11:39:41] -mirroring for hspike
INFO [2020-08-24 11:39:41] define_signif_tumor_subclusters(p_val=0.1
INFO [2020-08-24 11:39:41] define_signif_tumor_subclusters(), tumor: spike_tumor_cell_N_L
INFO [2020-08-24 11:39:41] cut tree into: 1 groups
INFO [2020-08-24 11:39:41] -processing spike_tumor_cell_N_L,spike_tumor_cell_N_L_s1
INFO [2020-08-24 11:39:41] define_signif_tumor_subclusters(), tumor: spike_tumor_cell_N_RB
INFO [2020-08-24 11:39:42] cut tree into: 1 groups
INFO [2020-08-24 11:39:42] -processing spike_tumor_cell_N_RB,spike_tumor_cell_N_RB_s1
INFO [2020-08-24 11:39:42] define_signif_tumor_subclusters(), tumor: simnorm_cell_N_L
INFO [2020-08-24 11:39:42] cut tree into: 1 groups
INFO [2020-08-24 11:39:42] -processing simnorm_cell_N_L,simnorm_cell_N_L_s1
INFO [2020-08-24 11:39:42] define_signif_tumor_subclusters(), tumor: simnorm_cell_N_RB
INFO [2020-08-24 11:39:43] cut tree into: 1 groups
INFO [2020-08-24 11:39:43] -processing simnorm_cell_N_RB,simnorm_cell_N_RB_s1
INFO [2020-08-24 11:41:29] ::plot_cnv:Start
INFO [2020-08-24 11:41:29] ::plot_cnv:Current data dimensions (r,c)=8268,8315 Total=68884500.4049847 Min=0.589471657548998 Max=2.85348079620815.
INFO [2020-08-24 11:41:30] ::plot_cnv:Depending on the size of the matrix this may take a moment.
INFO [2020-08-24 11:42:41] plot_cnv(): auto thresholding at: (0.750448 , 1.253511)
INFO [2020-08-24 11:42:47] plot_cnv_observation:Start
INFO [2020-08-24 11:42:47] Observation data size: Cells= 6422 Genes= 8268
INFO [2020-08-24 11:42:49] plot_cnv_observation:Writing observation groupings/color.
INFO [2020-08-24 11:42:49] plot_cnv_observation:Done writing observation groupings/color.
INFO [2020-08-24 11:42:50] plot_cnv_observation:Writing observation heatmap thresholds.
INFO [2020-08-24 11:42:50] plot_cnv_observation:Done writing observation heatmap thresholds.
INFO [2020-08-24 11:43:10] Colors for breaks:  #00008B,#24249B,#4848AB,#6D6DBC,#9191CC,#B6B6DD,#DADAEE,#FFFFFF,#EEDADA,#DDB6B6,#CC9191,#BC6D6D,#AB4848,#9B2424,#8B0000
INFO [2020-08-24 11:43:10] Quantiles of plotted data range: 0.750447689456217,0.958769212272914,1,1.03667777012508,1.25351110421452
INFO [2020-08-24 11:43:26] plot_cnv_observations:Writing observation data to TTK/infercnv.preliminary.observations.txt
INFO [2020-08-24 11:44:24] plot_cnv_references:Start
INFO [2020-08-24 11:44:24] Reference data size: Cells= 1893 Genes= 8268
INFO [2020-08-24 11:47:04] plot_cnv_references:Number reference groups= 2
INFO [2020-08-24 11:47:05] plot_cnv_references:Plotting heatmap.
INFO [2020-08-24 11:47:09] Colors for breaks:  #00008B,#24249B,#4848AB,#6D6DBC,#9191CC,#B6B6DD,#DADAEE,#FFFFFF,#EEDADA,#DDB6B6,#CC9191,#BC6D6D,#AB4848,#9B2424,#8B0000
INFO [2020-08-24 11:47:09] Quantiles of plotted data range: 0.750447689456217,0.967543385360157,1,1.0303629876366,1.25351110421452
INFO [2020-08-24 11:47:11] plot_cnv_references:Writing reference data to TTK/infercnv.preliminary.references.txt
INFO [2020-08-24 11:47:26]

        STEP 17: HMM-based CNV prediction

INFO [2020-08-24 11:47:26] predict_CNV_via_HMM_on_whole_tumor_samples
INFO [2020-08-24 11:48:02] -done predicting CNV based on initial tumor subclusters
INFO [2020-08-24 11:48:22] get_predicted_CNV_regions(subcluster)
INFO [2020-08-24 11:48:22] -processing cell_group_name: 0.0_s1, size: 46
INFO [2020-08-24 11:48:33] -processing cell_group_name: 2.2_s1, size: 14
INFO [2020-08-24 11:48:44] -processing cell_group_name: 3.3_s1, size: 82
INFO [2020-08-24 11:48:55] -processing cell_group_name: 4.4_s1, size: 61
INFO [2020-08-24 11:49:06] -processing cell_group_name: 5.5_s1, size: 119
INFO [2020-08-24 11:49:17] -processing cell_group_name: 7.7_s1, size: 21
INFO [2020-08-24 11:49:28] -processing cell_group_name: 8.8_s1, size: 178
INFO [2020-08-24 11:49:41] -processing cell_group_name: 9.9_s1, size: 675
INFO [2020-08-24 11:49:55] -processing cell_group_name: 10.10_s1, size: 157
INFO [2020-08-24 11:50:07] -processing cell_group_name: 11.11_s1, size: 107
INFO [2020-08-24 11:50:18] -processing cell_group_name: 15.15_s1, size: 122
INFO [2020-08-24 11:50:29] -processing cell_group_name: 16.16_s1, size: 8
INFO [2020-08-24 11:50:39] -processing cell_group_name: 17.17_s1, size: 757
INFO [2020-08-24 11:50:54] -processing cell_group_name: 18.18_s1, size: 100
INFO [2020-08-24 11:51:05] -processing cell_group_name: 19.19_s1, size: 157
INFO [2020-08-24 11:51:17] -processing cell_group_name: 21.21_s1, size: 8
INFO [2020-08-24 11:51:28] -processing cell_group_name: 22.22_s1, size: 14
INFO [2020-08-24 11:51:39] -processing cell_group_name: 23.23_s1, size: 79
INFO [2020-08-24 11:51:50] -processing cell_group_name: 24.24_s1, size: 1225
INFO [2020-08-24 11:52:08] -processing cell_group_name: 29.29_s1, size: 75
INFO [2020-08-24 11:52:19] -processing cell_group_name: 31.31_s1, size: 6
INFO [2020-08-24 11:52:29] -processing cell_group_name: 32.32_s1, size: 29
INFO [2020-08-24 11:52:39] -processing cell_group_name: 33.33_s1, size: 36
INFO [2020-08-24 11:52:49] -processing cell_group_name: 38.38_s1, size: 36
INFO [2020-08-24 11:53:00] -processing cell_group_name: 40.40_s1, size: 117
INFO [2020-08-24 11:53:11] -processing cell_group_name: 41.41_s1, size: 13
INFO [2020-08-24 11:53:21] -processing cell_group_name: 42.42_s1, size: 262
INFO [2020-08-24 11:53:33] -processing cell_group_name: 43.43_s1, size: 138
INFO [2020-08-24 11:53:45] -processing cell_group_name: 44.44_s1, size: 104
INFO [2020-08-24 11:53:56] -processing cell_group_name: 46.46_s1, size: 35
INFO [2020-08-24 11:54:07] -processing cell_group_name: 47.47_s1, size: 402
INFO [2020-08-24 11:54:19] -processing cell_group_name: 50.50_s1, size: 62
INFO [2020-08-24 11:54:29] -processing cell_group_name: 52.52_s1, size: 126
INFO [2020-08-24 11:54:40] -processing cell_group_name: 53.53_s1, size: 25
INFO [2020-08-24 11:54:49] -processing cell_group_name: 55.55_s1, size: 90
INFO [2020-08-24 11:55:00] -processing cell_group_name: 56.56_s1, size: 165
INFO [2020-08-24 11:55:10] -processing cell_group_name: 57.57_s1, size: 284
INFO [2020-08-24 11:55:21] -processing cell_group_name: 58.58_s1, size: 90
INFO [2020-08-24 11:55:32] -processing cell_group_name: 59.59_s1, size: 22
INFO [2020-08-24 11:55:42] -processing cell_group_name: 60.60_s1, size: 23
INFO [2020-08-24 11:55:53] -processing cell_group_name: 62.62_s1, size: 58
INFO [2020-08-24 11:56:05] -processing cell_group_name: 63.63_s1, size: 21
INFO [2020-08-24 11:56:15] -processing cell_group_name: 65.65_s1, size: 142
INFO [2020-08-24 11:56:27] -processing cell_group_name: 66.66_s1, size: 14
INFO [2020-08-24 11:56:37] -processing cell_group_name: 67.67_s1, size: 7
INFO [2020-08-24 11:56:48] -processing cell_group_name: 69.69_s1, size: 32
INFO [2020-08-24 11:56:59] -processing cell_group_name: 71.71_s1, size: 34
INFO [2020-08-24 11:57:09] -processing cell_group_name: 72.72_s1, size: 6
INFO [2020-08-24 11:57:20] -processing cell_group_name: 73.73_s1, size: 11
INFO [2020-08-24 11:57:30] -processing cell_group_name: 74.74_s1, size: 12
INFO [2020-08-24 11:57:40] -processing cell_group_name: 75.75_s1, size: 8
INFO [2020-08-24 11:57:51] -processing cell_group_name: 76.76_s1, size: 7
INFO [2020-08-24 11:58:01] -processing cell_group_name: N_L.N_L_s1, size: 870
INFO [2020-08-24 11:58:17] -processing cell_group_name: N_RB.N_RB_s1, size: 1023
INFO [2020-08-24 11:58:34] -writing cell clusters file: TTK/17_HMM_predHMMi6.hmm_mode-samples.cell_groupings
INFO [2020-08-24 11:58:34] -writing cnv regions file: TTK/17_HMM_predHMMi6.hmm_mode-samples.pred_cnv_regions.dat
INFO [2020-08-24 11:58:36] -writing per-gene cnv report: TTK/17_HMM_predHMMi6.hmm_mode-samples.pred_cnv_genes.dat
INFO [2020-08-24 11:58:36] -writing gene ordering info: TTK/17_HMM_predHMMi6.hmm_mode-samples.genes_used.dat
INFO [2020-08-24 11:58:37] ::plot_cnv:Start
INFO [2020-08-24 11:58:37] ::plot_cnv:Current data dimensions (r,c)=8268,8315 Total=208397310 Min=1 Max=6.
INFO [2020-08-24 11:58:38] ::plot_cnv:Depending on the size of the matrix this may take a moment.
INFO [2020-08-24 11:59:40] plot_cnv_observation:Start
INFO [2020-08-24 11:59:40] Observation data size: Cells= 6422 Genes= 8268
INFO [2020-08-24 11:59:42] plot_cnv_observation:Writing observation groupings/color.
INFO [2020-08-24 11:59:42] plot_cnv_observation:Done writing observation groupings/color.
INFO [2020-08-24 11:59:43] plot_cnv_observation:Writing observation heatmap thresholds.
INFO [2020-08-24 11:59:43] plot_cnv_observation:Done writing observation heatmap thresholds.
INFO [2020-08-24 12:00:02] Colors for breaks:  #00008B,#24249B,#4848AB,#6D6DBC,#9191CC,#B6B6DD,#DADAEE,#FFFFFF,#EEDADA,#DDB6B6,#CC9191,#BC6D6D,#AB4848,#9B2424,#8B0000
INFO [2020-08-24 12:00:02] Quantiles of plotted data range: 1,3,3,3,6
INFO [2020-08-24 12:00:16] plot_cnv_observations:Writing observation data to TTK/infercnv.17_HMM_predHMMi6.hmm_mode-samples.observations.txt
INFO [2020-08-24 12:01:02] plot_cnv_references:Start
INFO [2020-08-24 12:01:02] Reference data size: Cells= 1893 Genes= 8268
INFO [2020-08-24 12:03:47] plot_cnv_references:Number reference groups= 2
INFO [2020-08-24 12:03:47] plot_cnv_references:Plotting heatmap.
INFO [2020-08-24 12:03:51] Colors for breaks:  #00008B,#24249B,#4848AB,#6D6DBC,#9191CC,#B6B6DD,#DADAEE,#FFFFFF,#EEDADA,#DDB6B6,#CC9191,#BC6D6D,#AB4848,#9B2424,#8B0000
INFO [2020-08-24 12:03:51] Quantiles of plotted data range: 3,3,3,3,3
INFO [2020-08-24 12:03:54] plot_cnv_references:Writing reference data to TTK/infercnv.17_HMM_predHMMi6.hmm_mode-samples.references.txt
INFO [2020-08-24 12:04:12]

        STEP 18: Run Bayesian Network Model on HMM predicted CNV's

INFO [2020-08-24 12:04:12] Creating the following Directory:  TTK/BayesNetOutput.HMMi6.hmm_mode-samples
INFO [2020-08-24 12:04:12] Initializing new MCM InferCNV Object.
INFO [2020-08-24 12:04:12] validating infercnv_obj
INFO [2020-08-24 12:04:13] Total CNV's:  1360
INFO [2020-08-24 12:04:13] Loading BUGS Model.
INFO [2020-08-24 12:04:15] Running Sampling Using Parallel with  8 Cores

INFO [2020-08-24 13:51:15] Obtaining probabilities post-sampling
INFO [2020-08-24 14:04:05] Gibbs sampling time:  119.831170745691  Minutes
INFO [2020-08-24 14:05:17] Creating Diagnostic Plots.
Error in lapply(seq_along(mcmc), function(i) { :
  argument "mcmc" is missing, with no default
liu-xingliang commented 4 years ago

Running command:

infercnv_obj = CreateInfercnvObject(raw_counts_matrix=paste0('../../rerun.v140.meganormal/', patient, '.', tp, ".matrix"), annotations_file=paste0('../', patient, '.', tp, ".anno"), delim="\t", gene_order_file="../../../refdata-cellranger-GRCh38-3.0.0.gene_pos.chr_prefix.txt", ref_group_names = c('N_L', 'N_RB'))
infercnv_obj = infercnv::run(
            infercnv_obj, cutoff=0.1, out_dir=paste0(patient, '.', tp), cluster_by_groups=T, denoise=T, resume_mode=F, no_prelim_plot = FALSE, HMM=TRUE, BayesMaxPNormal=0.5, diagnostics = TRUE, num_threads=8
        )
GeorgescuC commented 4 years ago

Hi @liuxl18-hku ,

Could you try updating to the github version of the code by running:

library("devtools")   
devtools::install_github("broadinstitute/infercnv", ref="RELEASE_3_10")

And then try to run the second command again?

Regards, Christophe.

liu-xingliang commented 4 years ago

@GeorgescuC ,

Thank you. After installing using your command, sessionInfo() shows infercnv_1.2.2, it's even lower than my previous version infercnv_1.4.0 which I got from Bioconductor.

I am re-running my commands using re-installed version.

bless~

GeorgescuC commented 4 years ago

Hi @liuxl18-hku

My bad, I copied the commands from the wiki but forgot to change the target branch. If you run the following, the version should show 1.4.0 again, but it has additional commits compared to the version on BioConductor.

library("devtools")   
devtools::install_github("broadinstitute/infercnv", ref="master")

Regards, Christophe.

liu-xingliang commented 4 years ago

@GeorgescuC ,

Nevermind, I just updated my installation ending with infercnv_1.5.0. Now I am now testing it on a 19838 gene x 8315 cells dataset with four cores for running Bayesian Network Model on HMM predicted CNV's with 250Gb memory allocated.

It seems to take quite a while to run. I didn't use more cores as it seems to consume more memory and made my last few test running crashes.

Thank you.

GeorgescuC commented 4 years ago

Hi @liuxl18-hku ,

For 8315 cells and 19838 genes, 250GB of memory should be more than enough. Are you running on a cluster grid, or using a tool that could limit the memory allowed for a job (such as running in docker)?

Parallelization is enabled for the random trees subclustering if that option is enabled, and the Bayesian filtering only at this time.

Regards, Christophe.

liu-xingliang commented 4 years ago

@GeorgescuC

Yes, thank u, thd job done successfully after a few hrs.