"No CNV remains after filtering" using default settings on 10x lymphoma data

[juliashay]

I wanted to use Numbat to investigate the copy number variations in a publicly available 10x lymphoma dataset. My attempts ended with Numbat finding no copy number variations in this dataset, which presumably should have some.

Steps to reproduce:

1. Run a docker with Numbat: docker run -v /home/jupyter/docker_test:/mnt/mydata -it pkharchenkolab/numbat-rbase:latest /bin/bash

2. Download and extract the following files from 10x link: save the feature matrix along with barcodes and features from lymph_node_lymphoma_14k_filtered_feature_bc_matrix.tar.gz as lymphoma_matrix.mtx, lymphoma_barcodes.tsv and lymphoma_features.tsv; save the BAM file and its index from lymph_node_lymphoma_14k_gex_possorted_bam.bam as lymphoma.bam and lymphoma.bam.bai.

3. Inside the docker, run

Rscript /numbat/inst/bin/pileup_and_phase.R --label lymphoma --samples lymphoma --bams /mnt/mydata/lymphoma.bam --barcodes /mnt/mydata/lymphoma_barcodes.tsv --outdir /mnt/mydata/lymphoma --gmap /Eagle_v2.4.1/tables/genetic_map_hg38_withX.txt.gz --snpvcf /data/genome1K.phase3.SNP_AF5e2.chr1toX.hg38.vcf --paneldir /data/1000G_hg38 --ncores 16

to generate an allele count file.

4. Prepare the matrix and allele count file to work with Numbat

mtx <- as(Matrix::readMM("/mnt/mydata/lymphoma_matrix.mtx"), "dgCMatrix")
df_allele_cnt <- read.csv("/mnt/mydata/lymphoma/lymphoma_allele_counts.tsv", sep = "\t")
features <- read.csv("/mnt/mydata/lymphoma_features.tsv", sep = "\t", header = F)
barcodes <- read.csv("/mnt/mydata/lymphoma_barcodes.tsv", sep = "\t", header = F)
dimnames(mtx) = list(features$V2, barcodes$V1)

duplicate_genes = duplicated(features$V2)
features_new = features[!duplicate_genes, ]
mtx_new = mtx[!duplicate_genes, ]

5. Run Numbat:

run_numbat(mtx_new, ref_hca, df_allele_cnt, genome = "hg38", t = 1e-5, ncores = 4, plot = TRUE, out_dir = "/mnt/mydata/lymphoma_results")

Expected result:
I expected Numbat to find some copy number variations in this dataset.

Actual result:
Numbat runs for 1 iteration, then terminates with "No CNV after filtering - terminating" message (see attached image).

Please help me understand the results I am getting: are there settings that should be different? Is the result expected for this dataset?

[teng-gao]

Thanks for the detailed report. We previously analyzed this sample before and it should find some CNVs. Could you attach the outputs (bulk_subtrees_1.png, exp_roll_clust.png, and joint_post_1.tsv) in the results folder?

[juliashay]

Thanks for the detailed report. We previously analyzed this sample before and it should find some CNVs.

That's good to know.

Could you attach the outputs (bulk_subtrees_1.png, exp_roll_clust.png, and joint_post_1.tsv) in the results folder?

In my run, Numbat terminated before it generated the results folder and any of these files.

[teng-gao]

That would be surprising, because the output folder is created before any of the analysis steps are executed:

https://github.com/kharchenkolab/numbat/blob/main/R/main.R#L109-L112

Please double-check - I'm almost sure that the result folder shouldn't be empty. The output files should also be generated at each step, not all at once at the last step.

[teng-gao]

Hi @juliashay ,

I was able to reproduce your results. The CNVs are filtered out because of the max_entropy threshold. You can check by

fread('~/lymphoma/lymphoma_results/joint_post_1.tsv') %>% distinct(seg, avg_entropy) %>% count(avg_entropy < 0.5)

This is also documented in the FAQ section.

The uncertainty in single-cell posterior is high because of the high noise in expression logFC (ref_hca is not a great match for this dataset; better use a custom reference). Alternatively, change max_entropy=0.8 and the workflow should succeed.