Closed beginner984 closed 2 years ago
alexyermanos
commented
2 years ago can you please post the first few lines of gene_expression_cluster[[5]] ?
can you also try setting the Mt.Rb.filter to F?
also if you have another findmarkers output can you test if this is working ? (e.g. gene_expression_cluster[[1]])
beginner984
commented
2 years ago Thank you very much
I get this
> head(gene_expression_cluster[[5]])
p_val avg_logFC pct.1 pct.2 p_val_adj SYMBOL cluster
CD8A 0 1.630774 0.465 0.063 0 CD8A 4
GZMK 0 2.792085 0.734 0.034 0 GZMK 4
GZMA 0 1.461000 0.649 0.103 0 GZMA 4
CTSW 0 1.350435 0.752 0.185 0 CTSW 4
KLRB1 0 2.361302 0.686 0.144 0 KLRB1 4
CCL5 0 2.178712 0.939 0.175 0 CCL5 4
>
> gsea_EAE <- GEX_GSEA(GEX.cluster.genes.output = gene_expression_cluster[[5]], MT.Rb.filter = FALSE, path.to.pathways = "/user/data/data1/")
Error in path.to.pathways[[2]] : subscript out of bounds
> gsea_EAE <- GEX_GSEA(GEX.cluster.genes.output = gene_expression_cluster[[0]], MT.Rb.filter = FALSE, path.to.pathways ="/user/data/data1/")
Error in gene_expression_cluster[[0]] :
attempt to select less than one element in get1index <real>
> gsea_EAE <- GEX_GSEA(GEX.cluster.genes.output = gene_expression_cluster[[1]], MT.Rb.filter = FALSE, path.to.pathways ="/user/data/data1/")
Error in path.to.pathways[[2]] : subscript out of bounds
> gsea_EAE <- GEX_GSEA(GEX.cluster.genes.output = gene_expression_cluster[[2]], MT.Rb.filter = FALSE, path.to.pathways = "/user/data/data1/")
Error in path.to.pathways[[2]] : subscript out of bounds
>
vickreiner
commented
2 years ago Hi!
I was able to replicate this issue on my machine.
For reading the .gmt file, we are using fgsea::gmtPathways function. This one needs a direct path to the .gmt object. In your case this would probably look like
path.to.pathways = "/user/data/data1/c1.all.v7.4.symbols.gmt"
I hope this solves the issue! Thanks!
beginner984
commented
2 years ago Thank you so much Another error :(
> gsea_EAE <- GEX_GSEA(GEX.cluster.genes.output = gene_expression_cluster[[2]], MT.Rb.filter = F, path.to.pathways = "/user/data/data1/c1.all.v7.4.symbols.gmt")
Error in `dplyr::arrange()`:
! Problem with the implicit `transmute()` step.
x Problem while computing `..1 = IRanges::desc(NES)`.
Caused by error:
! unable to find an inherited method for function ‘desc’ for signature ‘"numeric"’
Run `rlang::last_error()` to see where the error occurred.Hi!
I assume this is a version incompatibility, but to make sure we can still check the raw Fgsea output.
If you run the function with verbose = TRUE you should get raw results printed to the console.
Could you share theses? Thanks a lot!
raphkuhn
commented
2 years ago Thank you for reporting this second error! A wrong package was assigned to the dplyr::desc() function. We fixed the function now and it can be downloaded from our github:
https://github.com/alexyermanos/Platypus/blob/master/R/GEX_GSEA.R
beginner984
commented
2 years ago Thank you very much
Sorry, might be irrelevant to the main post, but, I only have TCR + GEX
But I am wondering how I get B cells here
clonal_out <- VDJ_clonal_expansion(VDJ = vgm[[1]],celltype = "Bcells",clones = "30", group.by = "sample_id", color.by = "isotype", isotypes.to.plot = "all", treat.incomplete.clones = "exclude", treat.incomplete.cells = "proportional")
#group by specifies how many separate plots should be generated. If vgm contains global clonotype information this can be set to "none"
print(clonal_out[[1]])
And when I try for T cells I get nothing
clonal_out <- VDJ_clonal_expansion(VDJ = vgm[[1]],celltype = "Tcells",clones = "30", group.by = "sample_id", color.by = "isotype", isotypes.to.plot = "all", treat.incomplete.clones = "exclude", treat.incomplete.cells = "proportional")
#group by specifies how many separate plots should be generated. If vgm contains global clonotype information this can be set to "none"
print(clonal_out[[1]])
alexyermanos
commented
2 years ago Thanks for posting this - we will look into this but given that the distribution is identical for both arguments and there is no isotype information, the clonal distribution is most likely only describing the T cell expansion in both plots. this argument was more designed in the case that B and T cells are present in the same VGM
beginner984
commented
2 years ago Sorry maybe this is a non related question
But what does the color scale bar here means?
Red means we have more fold of a given gene or more cells express this given gene?
alexyermanos
commented
2 years ago We would need to know the parameters used to call this function - it would either be the fraction of clones or fraction of cells using a given TRBV-TRVA pairing in this sample's repertoire
beginner984
commented
1 year ago Excuse me,
What does this error mean please?
Integrating VDJ and GEX
Error in VDJ_GEX_matrix(VDJ.out.directory.list = c(VDJ.out.directory.list_T, :
object 'batches' not foundHere is the full thing
> vgm <- VDJ_GEX_matrix(VDJ.out.directory.list = c(VDJ.out.directory.list_T, VDJ.out.directory.list_B),
+ GEX.out.directory.list = GEX.out.directory.list,
+ Seurat.in = pbmc,
+ GEX.integrate = T,
+ VDJ.combine = T,
+ integrate.GEX.to.VDJ = T,
+ integrate.VDJ.to.GEX = T, #This will adjunct the VDJ information as metadata to the GEX object
+ exclude.GEX.not.in.VDJ = F,
+ filter.overlapping.barcodes.GEX = T,
+ filter.overlapping.barcodes.VDJ = T,
+ #exclude.on.cell.state.markers = c("CD3E"), #Exclude T cells from this analysis
+ get.VDJ.stats = T,
+ parallel.processing = "mclapply", #see note at the end of this chunk
+ trim.and.align = T, #Do not align BCR sequences to reference
+ group.id = c(1,1,2,2),
+ n.feature.rna=2000,
+ n.count.rna.min=200,
+ n.count.rna.max=2500,
+ mito.filter = 5,
integration.method = "harmony")
17:25:03
Loading in data
17:25:45 Loaded VDJ data
Getting VDJ GEX stats
Starting with 1 of 12
Starting with 2 of 12
Starting with 3 of 12
Starting with 4 of 12
Starting with 5 of 12
Starting with 6 of 12
Starting with 7 of 12
Starting with 8 of 12
Starting with 9 of 12
Starting with 10 of 12
Starting with 11 of 12
Starting with 12 of 12
Getting 10x stats
17:25:53 Got VDJ GEX stats
For input Seurat object: 14261 cells assigned barcodes in GEX
For input Seurat object GEX and VDJ barcode overlap is: 9141
For sample 1: 2293 cells assigned with high confidence barcodes in VDJ
For sample 2: 2074 cells assigned with high confidence barcodes in VDJ
For sample 3: 2393 cells assigned with high confidence barcodes in VDJ
For sample 4: 100 cells assigned with high confidence barcodes in VDJ
For sample 5: 2077 cells assigned with high confidence barcodes in VDJ
For sample 6: 606 cells assigned with high confidence barcodes in VDJ
For sample 7: 1038 cells assigned with high confidence barcodes in VDJ
For sample 8: 341 cells assigned with high confidence barcodes in VDJ
For sample 9: 314 cells assigned with high confidence barcodes in VDJ
For sample 10: 28 cells assigned with high confidence barcodes in VDJ
For sample 11: 678 cells assigned with high confidence barcodes in VDJ
For sample 12: 106 cells assigned with high confidence barcodes in VDJ
Removed a total of 184 cells with non unique barcodes in VDJ
17:25:53 Starting VDJ barcode iteration 1 of 12...
Started mcapply cluster with 48 cores
17:30:19 Done with 1 of 12
17:30:19 Starting VDJ barcode iteration 2 of 12...
Started mcapply cluster with 48 cores
17:30:50 Done with 2 of 12
17:30:50 Starting VDJ barcode iteration 3 of 12...
Started mcapply cluster with 48 cores
17:35:59 Done with 3 of 12
17:35:59 Starting VDJ barcode iteration 4 of 12...
Started mcapply cluster with 48 cores
17:36:12 Done with 4 of 12
17:36:12 Starting VDJ barcode iteration 5 of 12...
Started mcapply cluster with 48 cores
17:40:18 Done with 5 of 12
17:40:18 Starting VDJ barcode iteration 6 of 12...
Started mcapply cluster with 48 cores
17:41:37 Done with 6 of 12
17:41:37 Starting VDJ barcode iteration 7 of 12...
Started mcapply cluster with 48 cores
17:43:48 Done with 7 of 12
17:43:48 Starting VDJ barcode iteration 8 of 12...
Started mcapply cluster with 48 cores
17:44:31 Done with 8 of 12
17:44:31 Starting VDJ barcode iteration 9 of 12...
Started mcapply cluster with 48 cores
17:45:10 Done with 9 of 12
17:45:10 Starting VDJ barcode iteration 10 of 12...
Started mcapply cluster with 48 cores
17:45:15 Done with 10 of 12
17:45:15 Starting VDJ barcode iteration 11 of 12...
Started mcapply cluster with 48 cores
17:46:42 Done with 11 of 12
17:46:42 Starting VDJ barcode iteration 12 of 12...
Started mcapply cluster with 48 cores
17:46:43 Done with 12 of 12
Preparing Seurat.in object
17:46:48 Done with Seurat.in
Integrating VDJ and GEX
Error in VDJ_GEX_matrix(VDJ.out.directory.list = c(VDJ.out.directory.list_T, :
object 'batches' not found
In addition: Warning messages:
1: In data.frame(..., check.names = FALSE) :
row names were found from a short variable and have been discarded
2: In VDJ_GEX_matrix(VDJ.out.directory.list = c(VDJ.out.directory.list_T, :
Filtering of overlapping barcodes in GEX is not performed for Seurat.in input
3: In SeuratObject::FetchData(GEX.proc, vars = c("orig.ident", "seurat_clusters", :
The following requested variables were not found: tSNE_1, tSNE_2
Hello
I am getting a permanent error in two of the functions
I am sure
c1.all.v7.4.symbols.gmtis in the path