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View Code? Open in Web Editor NEWBEER: Batch EffEct Remover for single-cell data
License: MIT License
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Error in `contrasts<-`(`*tmp*`, value = contr.funs[1 + isOF[nn]]) : contrasts can be applied only to factors with 2 or more levels
Hello,
Thank you for developing so nice software!
Thank you in advance for your great help!
Best,
Yue
> D1 <- read.table(file='donor.csv', sep=',', row.names=1, header=TRUE,as.is=T)
> D2 <- read.table(file='patients.csv', sep=',', row.names=1, header=TRUE,as.is=T)
> colnames(D1)=paste0('D1_', colnames(D1))
> colnames(D2)=paste0('D2_', colnames(D2))
> source('https://raw.githubusercontent.com/jumphone/BEER/master/BEER.R')
> DATA=.simple_combine(D1,D2)$combine
> BATCH=rep('D2',ncol(DATA))
> BATCH[c(1:ncol(D1))]='D1'
> PosN=apply(DATA,2,.getPos)
> USED=which(PosN>500 & PosN<4000)
> DATA=DATA[,USED]; BATCH=BATCH[USED]
> mybeer=BEER(DATA, BATCH, GNUM=30, PCNUM=50, ROUND=1, GN=2000, SEED=1, COMBAT=TRUE, RMG=NULL)
[1] "BEER start!"
[1] "2020-05-26 10:31:19 PDT"
Loading required package: stringi
[1] "Group number (GNUM) is:"
[1] 30
[1] "Varible gene number (GN) of each batch is:"
[1] 2000
[1] "ROUND is:"
[1] 1
[1] 1
[1] "D2"
Performing log-normalization
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
[1] "Total varible gene number (GN) is:"
[1] 2000
Performing log-normalization
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Error in `contrasts<-`(`*tmp*`, value = contr.funs[1 + isOF[nn]]) :
contrasts can be applied only to factors with 2 or more levels
In addition: Warning messages:
1: In simpleLoess(y, x, w, span, degree = degree, parametric = parametric, :
at -0.78561
2: In simpleLoess(y, x, w, span, degree = degree, parametric = parametric, :
radius 5.5604e-05
3: In simpleLoess(y, x, w, span, degree = degree, parametric = parametric, :
all data on boundary of neighborhood. make span bigger
4: In simpleLoess(y, x, w, span, degree = degree, parametric = parametric, :
pseudoinverse used at -0.78561
5: In simpleLoess(y, x, w, span, degree = degree, parametric = parametric, :
neighborhood radius 0.0074568
6: In simpleLoess(y, x, w, span, degree = degree, parametric = parametric, :
reciprocal condition number 1
7: In simpleLoess(y, x, w, span, degree = degree, parametric = parametric, :
zero-width neighborhood. make span bigger
8: In simpleLoess(y, x, w, span, degree = degree, parametric = parametric, :
There are other near singularities as well. 0.090619
Input data
It is unclear in the vignette which data is used as input.
From the demo data, I can see that the input is a data frame ( https://github.com/jumphone/BEER/raw/master/DATA/demodata.zip )
Using a Seurat object, I can use the .simple_combine function to merge assays using the [["RNA"]] slot:
DATA=.simple_combine( Seurat_1 [["RNA"]], Seurat_2 [["RNA"]] )
The result is a sparse Matrix of class "dgCMatrix".
But impossible to use the FILL=T, I have the following error:
Error in rbind2(argl[[i]], r) :
no method for coercing this S4 class to a vector
Called from: rbind2(argl[[i]], r)
- Do I use the .simple_combine function properly?
- What can I do concerning the FILL=T option (I actually should have complete gene record)
Merge multiple scATAC Seq objects
Hello,
Thanks for the great package! I would like to merge multiple scATAC Seq objects with BEER and BBKNN that I am currently analyzing with Signac, so they are Seurat objects. How would one do this?
unused argument (reduction.use = "umap")
Hello,
Thank you for developing so nice software "BEER".
I followed the: https://github.com/jumphone/BEER
Thanks in advance for any great help!
Best,
Yue
> DimPlot(pbmc_batch, reduction.use='umap', group.by='batch', pt.size=0.1) Error in DimPlot(pbmc_batch, reduction.use = "umap", group.by = "batch", : unused argument (reduction.use = "umap")
Error in BEER(DATA, BATCH, GNUM = 30, PCNUM = 50, ROUND = 1, GN = 2000, : no slot of name "data" for this object of class "Assay5"
Thany you for the very nice package, but I've met some trouble, could you please offer some help?
Here I downloaded the demodata, and run the code as the README file says,
> mybeer=BEER(DATA, BATCH, GNUM=30, PCNUM=50, ROUND=1, GN=2000, SEED=1, COMBAT=TRUE, RMG=NULL)
And I receive the error as
[1] "BEER start!"
[1] "2024-01-23 22:34:55 CST"
[1] "Group number (GNUM) is:"
[1] 30
[1] "Varible gene number (GN) of each batch is:"
[1] 2000
[1] "ROUND is:"
[1] 1
[1] 1
[1] "D1"
Warning: Data is of class data.frame. Coercing to dgCMatrix.
Normalizing layer: counts
Performing log-normalization
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Finding variable features for layer counts
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
[1] 2
[1] "D2"
Warning: Data is of class data.frame. Coercing to dgCMatrix.
Normalizing layer: counts
Performing log-normalization
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Finding variable features for layer counts
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
[1] "Total varible gene number (GN) is:"
[1] 2551
Warning: Data is of class data.frame. Coercing to dgCMatrix.
Normalizing layer: counts
Performing log-normalization
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Error in BEER(DATA, BATCH, GNUM = 30, PCNUM = 50, ROUND = 1, GN = 2000, :
no slot of name "data" for this object of class "Assay5"
I'm sure I followed the pipeline as the README file says. I may make a stupid mistake, but I finally failed to correct it. Here is the session info:
> sessionInfo()
R version 4.2.2 (2022-10-31 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19044)
Matrix products: default
locale:
[1] LC_COLLATE=Chinese (Simplified)_China.utf8 LC_CTYPE=Chinese (Simplified)_China.utf8
[3] LC_MONETARY=Chinese (Simplified)_China.utf8 LC_NUMERIC=C
[5] LC_TIME=Chinese (Simplified)_China.utf8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] stringi_1.8.3 reticulate_1.34.0 Seurat_5.0.1 SeuratObject_5.0.1
[5] sp_2.1-2 limma_3.54.2 sva_3.46.0 BiocParallel_1.32.6
[9] genefilter_1.80.3 mgcv_1.9-1 nlme_3.1-164 dbplyr_2.4.0.9000
loaded via a namespace (and not attached):
[1] spam_2.10-0 plyr_1.8.9 igraph_1.6.0 lazyeval_0.2.2
[5] splines_4.2.2 RcppHNSW_0.5.0 listenv_0.9.0 scattermore_1.2
[9] GenomeInfoDb_1.34.9 ggplot2_3.4.4 digest_0.6.34 htmltools_0.5.7
[13] fansi_1.0.5 magrittr_2.0.3 memoise_2.0.1 tensor_1.5
[17] cluster_2.1.6 ROCR_1.0-11 globals_0.16.2 Biostrings_2.66.0
[21] annotate_1.76.0 matrixStats_1.2.0 spatstat.sparse_3.0-3 colorspace_2.1-0
[25] blob_1.2.4 ggrepel_0.9.5 dplyr_1.1.3 crayon_1.5.2
[29] RCurl_1.98-1.14 jsonlite_1.8.8 progressr_0.14.0 spatstat.data_3.0-4
[33] survival_3.5-7 zoo_1.8-12 glue_1.6.2 polyclip_1.10-6
[37] gtable_0.3.4 zlibbioc_1.44.0 XVector_0.38.0 leiden_0.4.3.1
[41] future.apply_1.11.1 BiocGenerics_0.44.0 abind_1.4-5 scales_1.3.0
[45] DBI_1.2.1 edgeR_3.40.2 spatstat.random_3.2-2 miniUI_0.1.1.1
[49] Rcpp_1.0.11 viridisLite_0.4.2 xtable_1.8-4 bit_4.0.5
[53] dotCall64_1.1-1 stats4_4.2.2 htmlwidgets_1.6.4 httr_1.4.7
[57] RColorBrewer_1.1-3 ellipsis_0.3.2 ica_1.0-3 pkgconfig_2.0.3
[61] XML_3.99-0.16 uwot_0.1.16 deldir_2.0-2 locfit_1.5-9.8
[65] utf8_1.2.4 tidyselect_1.2.0 rlang_1.1.2 reshape2_1.4.4
[69] later_1.3.2 AnnotationDbi_1.60.2 munsell_0.5.0 tools_4.2.2
[73] cachem_1.0.8 cli_3.6.1 generics_0.1.3 RSQLite_2.3.4
[77] ggridges_0.5.5 stringr_1.5.1 fastmap_1.1.1 goftest_1.2-3
[81] bit64_4.0.5 fitdistrplus_1.1-11 purrr_1.0.2 RANN_2.6.1
[85] KEGGREST_1.38.0 pbapply_1.7-2 future_1.33.1 mime_0.12
[89] compiler_4.2.2 rstudioapi_0.15.0 plotly_4.10.4 png_0.1-8
[93] spatstat.utils_3.0-4 tibble_3.2.1 RSpectra_0.16-1 lattice_0.21-9
[97] Matrix_1.6-3 vctrs_0.6.4 pillar_1.9.0 lifecycle_1.0.4
[101] BiocManager_1.30.22 spatstat.geom_3.2-7 lmtest_0.9-40 RcppAnnoy_0.0.21
[105] data.table_1.14.10 cowplot_1.1.2 bitops_1.0-7 irlba_2.3.5.1
[109] httpuv_1.6.13 patchwork_1.2.0 R6_2.5.1 promises_1.2.1
[113] KernSmooth_2.23-22 gridExtra_2.3 IRanges_2.32.0 parallelly_1.36.0
[117] codetools_0.2-19 fastDummies_1.7.3 MASS_7.3-60.0.1 sctransform_0.4.1
[121] S4Vectors_0.36.2 GenomeInfoDbData_1.2.9 parallel_4.2.2 grid_4.2.2
[125] tidyr_1.3.0 Rtsne_0.17 spatstat.explore_3.2-5 Biobase_2.58.0
[129] shiny_1.8.0
Thank you very much for your help.
Error in names(x) <- value : 'names' attribute [1] must be the same length as the vector [0]
Hello,
Thank you for developing so nice software!
I can go through the demodata of DATA2_MAT.txt.
I have problem when upload my data: donor_82_01.csv .
Thank you in advance for great help!
Best,
Yue
> D1<-read.csv(file='donor_82_01.csv', sep='\t',row.names=1, header=T)
> D2<-read.csv(file='donor_85_02.csv', sep='\t',row.names=1, header=T)
> colnames(D1)=paste0('D1_', colnames(D1))
Error in names(x) <- value :
'names' attribute [1] must be the same length as the vector [0]
Error in asMethod(object) : Cholmod error 'problem too large' at file ../Core/cholmod_dense.c, line 105
Hello,
Thank you so much for developing so great software!
If the size of the file is not too big, BEER works well.
If too big, BEER does not work.
Thank you in advance for your great help!
Best,
Yue
> library(Seurat)
Collecting package metadata (current_repodata.json): ...working... failed
UnavailableInvalidChannel: The channel is not accessible or is invalid.
channel name: anaconda/cloud/pytorch
channel url: https://mirrors.cloud.tencent.com/anaconda/cloud/pytorch
error code: 404
You will need to adjust your conda configuration to proceed.
Use `conda config --show channels` to view your configuration's current state,
and use `conda config --show-sources` to view config file locations.
> library(limma)
> library(sva)
Loading required package: mgcv
Loading required package: nlme
This is mgcv 1.8-31. For overview type 'help("mgcv-package")'.
Loading required package: genefilter
Loading required package: BiocParallel
> source('https://raw.githubusercontent.com/jumphone/BEER/master/BEER.R')
[1] "Welcome to BEER (v0.1.7)!"
> library(data.table)
data.table 1.12.8 using 6 threads (see ?getDTthreads). Latest news: r-datatable.com
> getwd()
[1] "/home/li"
> setwd("/home/li/Transcriptomic_patients/donor")
> a<-"donor_82_01.csv"
> b<-"donor_85_02.csv"
> c<-"donor_87_03.csv"
> d<-"donor_89_04.csv"
> e<-"donor_91_05.csv"
> f<-"donor_93_06.csv"
> g<-"donor_95_07.csv"
> h<-"donor_97_08.csv"
> a1<-data.frame(fread(a),check.names=FALSE, row.names=1,header=TRUE)
|--------------------------------------------------|
|==================================================|
> b1<-data.frame(fread(b),check.names=FALSE, row.names=1,header=TRUE)
> c1<-data.frame(fread(c),check.names=FALSE, row.names=1,header=TRUE)
|--------------------------------------------------|
|==================================================|
> d1<-data.frame(fread(d),check.names=FALSE, row.names=1,header=TRUE)
> e1<-data.frame(fread(e),check.names=FALSE, row.names=1,header=TRUE)
> f1<-data.frame(fread(f),check.names=FALSE, row.names=1,header=TRUE)
> g1<-data.frame(fread(g),check.names=FALSE, row.names=1,header=TRUE)
|--------------------------------------------------|
|==================================================|
> h1<-data.frame(fread(h),check.names=FALSE, row.names=1,header=TRUE)
> setwd("/home/li/Transcriptomic_patients/patients")
> i<-"mild_69_141.csv"
> j<-"mild_70_142.csv"
> k<-"severe_71_143.csv"
> l<-"mild_72_144.csv"
> m<-"severe_73_145.csv"
> n<-"severe_74_146.csv"
> o<-"severe_51_148.csv"
> p<-"severe_52_149.csv"
> q<-"severe_53_152.csv"
> i1<-data.frame(fread(i),check.names=FALSE, row.names=1,header=TRUE)
|--------------------------------------------------|
|==================================================|
> j1<-data.frame(fread(j),check.names=FALSE, row.names=1,header=TRUE)
|--------------------------------------------------|
|==================================================|
> k1<-data.frame(fread(k),check.names=FALSE, row.names=1,header=TRUE)
|--------------------------------------------------|
|==================================================|
> l1<-data.frame(fread(l),check.names=FALSE, row.names=1,header=TRUE)
> m1<-data.frame(fread(m),check.names=FALSE, row.names=1,header=TRUE)
|--------------------------------------------------|
|==================================================|
> n1<-data.frame(fread(n),check.names=FALSE, row.names=1,header=TRUE)
> o1<-data.frame(fread(o),check.names=FALSE, row.names=1,header=TRUE)
> p1<-data.frame(fread(p),check.names=FALSE, row.names=1,header=TRUE)
> q1<-data.frame(fread(q),check.names=FALSE, row.names=1,header=TRUE)
|--------------------------------------------------|
|==================================================|
> D1<-cbind(a1,b1)
> D2<-cbind(i1,j1)
> colnames(D1)=paste0('D1_', colnames(D1))
> colnames(D2)=paste0('D2_', colnames(D2))
> DATA=.simple_combine(D1,D2)$combine
> BATCH=rep('D2',ncol(DATA))
> BATCH[c(1:ncol(D1))]='D1'
> PosN=apply(DATA,2,.getPos)
> USED=which(PosN>500 & PosN<4000)
> DATA=DATA[,USED]; BATCH=BATCH[USED]
> mybeer=BEER(DATA, BATCH, GNUM=30, PCNUM=50, ROUND=1, GN=2000, SEED=1, COMBAT=TRUE, RMG=NULL)
[1] "BEER start!"
[1] "2020-05-27 20:15:01 PDT"
[1] "Group number (GNUM) is:"
[1] 30
[1] "Varible gene number (GN) of each batch is:"
[1] 2000
[1] "ROUND is:"
[1] 1
[1] 1
[1] "D1"
Performing log-normalization
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
[1] 2
[1] "D2"
Performing log-normalization
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
[1] "Total varible gene number (GN) is:"
[1] 3101
Performing log-normalization
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Found2batches
Adjusting for0covariate(s) or covariate level(s)
Standardizing Data across genes
Fitting L/S model and finding priors
Finding parametric adjustments
Adjusting the Data
Centering and scaling data matrix
|======================================================================| 100%
[1] "Calculating PCs ..."
PC_ 1
Positive: ENSG00000011600
Negative: ENSG00000196260
Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session
20:16:09 UMAP embedding parameters a = 0.9922 b = 1.112
20:16:09 Read 13570 rows and found 50 numeric columns
20:16:09 Using Annoy for neighbor search, n_neighbors = 30
20:16:09 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:16:12 Writing NN index file to temp file /tmp/RtmpDd8OkR/fileabb1332a96ba
20:16:12 Searching Annoy index using 1 thread, search_k = 3000
20:16:17 Annoy recall = 100%
20:16:18 Commencing smooth kNN distance calibration using 1 thread
20:16:19 Initializing from normalized Laplacian + noise
20:16:21 Commencing optimization for 200 epochs, with 596834 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:16:29 Optimization finished
[1] "Get group for:"
[1] "D1"
[1] "Group Number:"
[1] 30
[1] "Get group for:"
[1] "D2"
[1] "Group Number:"
[1] 30
[1] "Finding MN pairs..."
[1] "1 / 60"
[1] "ROUND:"
[1] 1
[1] "Number of MN pairs:"
[1] 4
[1] "Evaluating PCs ..."
[1] "Start"
[1] 1
[1] 2
[1] 3
[1] 4
[1] 5
[1] 6
[1] 7
[1] 8
[1] 9
[1] 10
[1] 11
[1] 12
[1] 13
[1] 14
[1] 15
[1] 16
[1] 17
[1] 18
[1] 19
[1] 20
[1] 21
[1] 22
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[1] 24
[1] 25
[1] 26
[1] 27
[1] 28
[1] 29
[1] 30
[1] 31
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[1] 42
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[1] 44
[1] 45
[1] 46
[1] 47
[1] 48
[1] 49
[1] 50
[1] "Finished!!!"
[1] "############################################################################"
[1] "BEER cheers !!! All main steps finished."
[1] "############################################################################"
[1] "2020-05-27 20:16:52 PDT"
> D1<-cbind(a1,b1,c1,d1,e1,f1,g1,h1)
> D2<-cbind(i1,j1,k1,l1,m1,n1,o1,p1,q1)
> colnames(D1)=paste0('D1_', colnames(D1))
> colnames(D2)=paste0('D2_', colnames(D2))
> DATA=.simple_combine(D1,D2)$combine
> BATCH=rep('D2',ncol(DATA))
> BATCH[c(1:ncol(D1))]='D1'
> PosN=apply(DATA,2,.getPos)
> USED=which(PosN>500 & PosN<4000)
> DATA=DATA[,USED]; BATCH=BATCH[USED]
> mybeer=BEER(DATA, BATCH, GNUM=30, PCNUM=50, ROUND=1, GN=2000, SEED=1, COMBAT=TRUE, RMG=NULL)
[1] "BEER start!"
[1] "2020-05-27 20:34:03 PDT"
[1] "Group number (GNUM) is:"
[1] 30
[1] "Varible gene number (GN) of each batch is:"
[1] 2000
[1] "ROUND is:"
[1] 1
[1] 1
[1] "D1"
Performing log-normalization
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
[1] 2
[1] "D2"
Performing log-normalization
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
[1] "Total varible gene number (GN) is:"
[1] 3060
Performing log-normalization
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Error in asMethod(object) :
Cholmod error 'problem too large' at file ../Core/cholmod_dense.c, line 105
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