cannin/biostars_qa huggingface dataset:
Question: hi,
Im newbie to linux and NGS. I have genome.fasta file and transcripts.gff3 file. Both files have different chromosome naming pattern due to which im unable to use them in cufflinks as its giving following error:

Warning: couldn't find fasta record for 'CA_chr13_BGI-A2_v1.0'!
This contig will not be bias corrected.

can someone kindly help me how to equate chromosome naming in both files???

i mean the chromosome name for fasta file should exactly be the same as to that in gff3 file or vice versa.
the chromosome names in the fasta file are as follows:

gnl|BGIA2|CA_chr1

gnl|BGIA2|CA_chr2

while in gff3 file these are as follows:

CA_chr4_BGI-A2_v1.0

CA_chr6_BGI-A2_v1.0

it would be more convenient if they are retained to simply as chr1, chr2,chr3 and so on rather than this long naming. further chr1 in fasta file should exactly be the chr1 in gff3 file.

kind help would be highly appreciated.

thanks

Answer: Try this and let me know (assuming that file has # before headers):

$ awk '!/#/ {split($1, a, "_"); $1=a[2]}1' OFS='\t' test.gtf

input gtf:

$ cat test.gtf
##gff-version	null
CA_chr4_BGI-A2_v1.0	GLEAN	mRNA	123284514	123288477	0.999991-	.	ID=Cotton_A_18927_BGI-A2_v1.0;Name=Cotton_A_18927;source_id=CottonA_GLEAN_10022228;identical_support_id=CUFF67.1103.1;evid_id=Cot030308.1
CA_chr4_BGI-A2_v1.0	GLEAN	CDS	123288376	123288477	.	-0	Parent=Cotton_A_18927_BGI-A2_v1.0
CA_chr4_BGI-A2_v1.0	GLEAN	CDS	123287662	123287826	.	-0	Parent=Cotton_A_18927_BGI-A2_v1.0
CA_chr4_BGI-A2_v1.0	GLEAN	CDS	123287427	123287536	.	-0	Parent=Cotton_A_18927_BGI-A2_v1.0
CA_chr4_BGI-A2_v1.0	GLEAN	CDS	123287129	123287237	.	-1	Parent=Cotton_A_18927_BGI-A2_v1.0
CA_chr4_BGI-A2_v1.0	TEMP	CDS	123286939	123287051	.	-0	Parent=Cotton_A_18927_BGI-A2_v1.0
CA_chr4_BGI-A2_v1.0	OUT	CDS	123286180	123286330	.	-1	Parent=Cotton_A_18927_BGI-A2_v1.0
CA_chr4_BGI-A2_v1.0	NYCTY	CDS	123284514	123285671	.	-0	Parent=Cotton_A_18927_BGI-A2_v1.0
CA_chr9_BGI-A2_v1.0	GLEAN	mRNA	17802711	17803334	1	+.	ID=Cotton_A_16149_BGI-A2_v1.0;Name=Cotton_A_16149;source_id=CottonA_GLEAN_10030787;evid_id=Cot023903.1

output gtf:

$ awk '!/#/ {split($1, a, "_"); $1=a[2]}1' OFS='\t' test.gtf
##gff-version	null
chr4	GLEAN	mRNA	123284514	123288477	0.999991-	.	ID=Cotton_A_18927_BGI-A2_v1.0;Name=Cotton_A_18927;source_id=CottonA_GLEAN_10022228;identical_support_id=CUFF67.1103.1;evid_id=Cot030308.1
chr4	GLEAN	CDS	123288376	123288477	.	-0	Parent=Cotton_A_18927_BGI-A2_v1.0
chr4	GLEAN	CDS	123287662	123287826	.	-0	Parent=Cotton_A_18927_BGI-A2_v1.0
chr4	GLEAN	CDS	123287427	123287536	.	-0	Parent=Cotton_A_18927_BGI-A2_v1.0
chr4	GLEAN	CDS	123287129	123287237	.	-1	Parent=Cotton_A_18927_BGI-A2_v1.0
chr4	TEMP	CDS	123286939	123287051	.	-0	Parent=Cotton_A_18927_BGI-A2_v1.0
chr4	OUT	CDS	123286180	123286330	.	-1	Parent=Cotton_A_18927_BGI-A2_v1.0
chr4	NYCTY	CDS	123284514	123285671	.	-0	Parent=Cotton_A_18927_BGI-A2_v1.0
chr9	GLEAN	mRNA	17802711	17803334	1	+.	ID=Cotton_A_16149_BGI-A2_v1.0;Name=Cotton_A_16149;source_id=CottonA_GLEAN_10030787;evid_id=Cot023903.1
Question: Hello,

I've watched a video from YouTube about OMIM. Then I get that OMIM already had more than 3000 disease-associated genes till last year. Now I want to get all of those genes from OMIM to do some experiments.

I think maybe I should use "Gene Map" of OMIM, but I still don't know how to search to get all disease-associated genes.

Do you have any good advice or guide?

Thank you in advance,

naulty

Answer: First of all, let's clarify that in OMIM there are two types of IDs:

- the MIM Gene ID refers to genes (.e.g [600276][1])
- the MIM MORBID ID refers to diseases/phenotypes ( e.g. [125310][2])

It is easy to make confusion, because both types of IDs are integer and there is no easy way to tell whether an ID refers to a gene or to a disease. If you look for example at the MORBID ID http://www.omim.org/entry/125310, you will see that it is associated to at least one gene.

To download all the genes, you can use the page posted by Santhilal Subhash, parsing the files morbidmap and genemap in order to get the ids. An alternative is to download them from [Biomart][3], selecting no filters, and the MIM Ids in the Output tab. This will give you a list of all the genes and their association to disease, with the advantage that you can select other output columns to get other information about the genes.

[1]: http://www.omim.org/entry/600276?search=600276&highlight=600276
[2]: http://www.omim.org/entry/125310
[3]: http://central.biomart.org/martwizard/#!/Search_by_database_name?mart=Ensembl%2077%20Genes%20%28WTSI,%20UK%29
Question: Hi,

I have been struggling to extract the set of genes related to each pathway from KEGG. I read multiple topics on biostar on how to do this but apparently there is something wrong. I downloaded the bret hierarchy file in order to get genes on the pathway level that I want. However, there seem to be multiple methods which some of them are working and the others are not.

In this topic, (https://www.biostars.org/p/69336/) a nice method has been mentioned by Pierre on how to do this using the following url: http://togows.dbcls.jp/entry/pathway/hsa05200/genes.json. However, when I try this for other pathways, the json list is empty (e.g., http://togows.dbcls.jp/entry/pathway/hsa01200/genes.json).

Another method which I found in this answer (https://www.biostars.org/p/71737/), was using the REST API. However, also for some of the pathways, there are no genes (e.g., http://rest.kegg.jp/get/hsa01200).

Surprisingly, if I take the same pathway, and I go to: http://www.genome.jp/dbget-bin/get_linkdb?-t+genes+path:hsa01200 I'm able to find the list of genes that I'm looking for.

Can someone tell me what is the difference between these 3 methods and what is the recommended method to do this.

Answer: I'm able to get a list of genes by using the kg tool as shown below. Perhaps this is enough to meet your needs. I would also like to be able to specify the type of gene identifier, such as Entrez ID or Ensembl ID (link to issue), but this has not been implemented yet as far as I can tell.

**Command:**

kg --mergecol=0 --noheader --genes --definition --species=hsa <(echo 01200)

**Output:** (trimmed)

```
Cache path is: /Users/slowikow/.kegg/ (Time: Thu, 29 Oct 2015 10:10:24 )
Get KEGG gene to external gene map. (Time: Thu, 29 Oct 2015 10:10:24 )
Get KEGG path to gene map. (Time: Thu, 29 Oct 2015 10:10:25 )
Get KEGG pathway to definition map. (Time: Thu, 29 Oct 2015 10:10:25 )
Connect KEGG gene map and KEGG pathway map. (Time: Thu, 29 Oct 2015 10:10:26 )
/Users/slowikow/anaconda/lib/python2.7/site-packages/ebs/merge_cols.py:74: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)
in_df = in_df.sort(pre_merge_sort_order_col)
Attaching pathway definitions. (Time: Thu, 29 Oct 2015 10:10:26 )
01200 ME3 Carbon metabolism
01200 PGAMA Carbon metabolism
01200 PGAM-B Carbon metabolism
01200 HEL-S-35 Carbon metabolism
01200 PGAM1 Carbon metabolism
01200 PFKF Carbon metabolism
```
Question: Hi,

I'm in the process of adding (optional) support for using alignments to [our tool for RNA-seq quantification][1], and I'm currently trying to model the fragment size distribution of aligned fragments in a set of reads. I'm a bit confused about exactly where this information is stored in a bam/sam file. According to the spec, the SAM file has a TLEN field that gives the "template length" of an alignment, which seems to me like it would be equivalent to the fragment length (except in the case of e.g. a chimeric alignment). However, the API doesn't expose this field directly, and instead in SAM and BAM files parsed via samtools, you have access to an isize field, which, from various online sources seems to be the "insert size" of the aligned fragment.

My question is how do these two fields relate? Are they different? Is the insert size the fragment size, the mate inner distance (frag. size - read lengths), or something else entirely? Unfortunately, the documentation on this isn't too clear, so I'm reaching out to someone whose dealt with this in practice before.

Thanks!

Rob

[1]: https://github.com/kingsfordgroup/sailfish

Answer: The TLEN value itself is actually isize. Yes, this can be a bit confusing.

Edit: I removed an incorrect part about TLEN.
Question: Hi, I am new to Music when find significant mutated genes in my data.

There three test to calculate significant p value of gene, and I am confused which test should be chosen or all three test should be considered together?

Another question: The q value threshold is limited at 0.01 as paper (Kandoth, Cyriac, et al. "Mutational landscape and significance across 12 major cancer types." *Nature* 502.7471 (2013): 333-339.) said? Can I set the q value threshold at 0.1?

Thank you for help!

Answer: Per the [documentation of the MuSiC `smg` module][1]:

> P-values and false discovery rates (FDRs) for each gene in gene_mr_file is calculated using three tests: Fisher's Combined P-value test (FCPT), Likelihood Ratio test (LRT), and the Convolution test (CT). For a gene, if its FDR for at least 2 of these tests is <= max_fdr, it will be output as an SMG. Another output file with prefix "_detailed" will have p-values and FDRs for all genes.

To report in a paper, use p-values/q-values from either CT, LRT, or both. CT performs slightly better than LRT overall, but LRT sometimes skips false-positive SMGs that CT picks. Hence the use of the `max_fdr` cutoff as described above.

It is perfectly fine to modify the q-value (FDR) cutoffs however you like, because background mutation models differ wildly between tumor types. Do a literature search on the highest ranked genes, and work your way down the list. When it seems like there are a lot of irrelevant genes, then that's where you draw the cutoff.

[1]: http://gmt.genome.wustl.edu/genome-shipit/genome-music/current/doc/genome-music-smg.html
Question: Hi!

I need to plot some genomic locations in context of their distances from transcription start site, CpG islands and Dnase hypersensitive sites.

Question1) Can anyone please tell me how I can get ucsc/refseq transcription start site with 1kb up- and downstream flank from ucsc table browser? In the output, I see the following options, I am not clear which options to choose to get what I want. If I just retrieve base pairs upstream to genes, I will miss the alternate TSS.

```
Whole Gene
Upstream by bases
Exons plus bases at each end
Introns plus bases at each end
5' UTR Exons
Coding Exons
3' UTR Exons
Downstream by bases
```

Question 2) How can I get lists of CpG islands and DNase hypersensitive sites of human genome?

Thanks a lot for your time and help!

Answer: For DNaseI hypersensitive sites in hg19, there are tracks for various cell types on UCSC's site, with the prefix `wgEncodeAwgDnaseUw*`:

http://hgdownload.cse.ucsc.edu/goldenpath/hg19/database

For example, for the SkMC cell line:

http://hgdownload.cse.ucsc.edu/goldenpath/hg19/database/wgEncodeAwgDnaseUwSkmcUniPk.txt.gz

The description of columns in this file is available here:

http://hgdownload.cse.ucsc.edu/goldenpath/hg19/database/wgEncodeAwgDnaseUwSkmcUniPk.sql

From this description, you could build a BED file via the following call:

```
$ wget -qO- http://hgdownload.cse.ucsc.edu/goldenpath/hg19/database/wgEncodeAwgDnaseUwSkmcUniPk.txt.gz \
| gunzip -c - \
| cut -f2- - \
> wgEncodeAwgDnaseUwSkmcUniPk.bed
```

Once you have a BED file for your DNaseI dataset of interest, you can do BEDOPS set operations with this and other BED files that share the same chromosome naming scheme and genome build.
Question: I have mapped my RNA-seq reads (paired-end) onto the reference genome. Now I hope to measure the expression level by genes using featurecounts. Do I count the number of reads mapped to the whole 'gene' region, which includes introns? Or I should count reads mapped to all exons belonging to the same gene? Someone suggested using exons, may I know the reason behand this?

Answer: Exons because that is what the final spliced transcript is composed of. There is a reason that exon is the default setting in featureCounts.
Question: I am trying to find the optimal local alignment score of a short sequence (50bp) to a reference bacterial genome of several Mbp. When I use Biopython Align module, I always get the score equal to the length of the shorter (query) sequence. For example, with alignxx function:

('--G------------------A-----------TC-G-G-G------AC-G---C--C----G--G-------T---TG-C-GA----AT---A--CA-C----AG-G--------T----------TT---AT-G----TG-C-T-----G------G--------AC--G--A-A----A-A----A----C-A----------------A---T--C-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------', 'CGGTGCCCTCCTCCGCGGCGTACACCCCGCGCGTCCGCGCGGGCGCGACTGATACAGCTTACGCCGGCGCGACTGCCTGTCCGAGTCGATCGCATACATCTGTTAGCGCGGCAGGGTGAACGCGGCGTTCGCATCGCCGATGGCGTACTACGCCTCCCGGGGGCCGCACACGTGACATCGGACAGGCGAGGTTCCACCGGGCCCCGCTCCGGAGCCTGGCGCGAGTGAATGCCATACCTTATATAAACTCAGTCCGCCCAGAACTATGGGTATGGAACCCGGCGGTATAGGGCACTCGCCCACCCATTGTCCTGAACTGCTGCTGAACATTGCGCGATTATGGTTGATACCGCGGATACCTCAGCGCGCTCTGCGACGCGCCCTGAGGACCCGACCGCCACTTAAGCATAGGCCGCCAGAAGAGCGCCGCTCCTAGGTGCGGCCGCGGAGACTAGGGCGGCCTTGAGACCGGAAATGCAAGGACCTGGGCAACCAACTTACCCCTTCGCTAGTGCTGAGCTACGGCGGATAAGAACGTCAATAGGCGAATAGAGTCTCCGCACCCATCGCCCTCTGAGGTTTGGAGGGCGGCCAAAGATACTGGGCAGGACCCCCGCATCCCAAGGTAGTCTGGTTCGGGACCAGCCTATCGAACAGTTCGAAATACGGAGCGGGACAAACGAAGCATCGCGCGTACCGTGTCCCGGACGCGGTGCGGACCTCGCCCGCACTTGCTTCCCGTGAACACGGAGCGGTCCCGCCCGTAGGGATCATATCGTCCCAAGGGACAGCCATAGCCCGCCTCCTCCTATGGTGCTGAGAAGCCCGGGGGCCCCCAGCAGCCTCTCCGCGGACCGAGGATGAGAGGAACACGCTCTACCGAGGCACAGTACCCTGGTTTTGTTCCAGCAATCCCCTTCCACGGCGCTATCTTGCTTAGGTGCGGTGAGAATAATGTCCGACTCCCGTTCCCGGGGATTTGGCGGTGCGGCCCAGTAT', 54.0, 0, 999)

The local alignment seems to give correct values (similar to editor distance) if the two sequences are comparable in size.

1. Why are the gaps in one of the sequence not counted? Can I set a parameter to alleviate that?
2. Is the editor distance a good approximation to the alignment score (taking into account it doesn't handle reverse complements)?

Thanks!

Answer: You need to show us the alignment code you used.

If its not counting gaps. thats probably because you used one of the `AlignIO` methods which doesn't count them. You will need to be explicit about the alignment and scoring methods.

I would say no, in general, edit distance is not a good proxy for alignment score. Unless your sequences are identical in length and include no INDELs.
Question: Hi,

I have a somewhat high content of mitochondrial RNA in my RNA-seq experiment. Is there a way to use samtools to remove alignments to the 'MT' chromosome and keep all the rest?

I'm considering using samtools view in combination with awk but perhaps there's a better/cleaner solution?

Thanks!

Answer: You can use samtools to do it by selecting particular chromosomes and never leaving the binary format, for example:

samtools view -b input.bam chr1 chr2 chr3 chr4 > output.bam

That's a bit unwieldy to list all non-mitochondrial chromosomes manually, so you could try an ugly bash one-liner like:

samtools idxstats input.bam | cut -f 1 | grep -v MT | xargs samtools view -b input.bam > output.bam
Question: Hi everyone,

I have seen a similar question [(here)][1] but I can't figure out how the issue was solved.

I have a list (> 500k) of SNPs rs numbers and would like to get their corresponding stable snp names. The code that I am running to test for one snp before I have a go with the whole list is:

library(biomaRt)
ensembl = useDataset("btaurus_gene_ensembl", mart = ensembl)
getBM(attributes=c("ensembl_gene_id","variation_name"),filters = c("snp_filter"),
values="rs17870417", mart=ensembl, uniqueRows=TRUE)

But I get this error:

Error in getBM(attributes = c("ensembl_gene_id", "variation_name"), filters = c("snp_filter"), :
Invalid filters(s): snp_filter
Please use the function 'listFilters' to get valid filter names

How can I solve this error or is there another filter option for "snp_filter"?
I can find the snp name on the ensembl [website][1].
Thanks in advance.

[1]: http://asia.ensembl.org/Bos_taurus/Variation/Explore?r=1:78737231-78738231;v=rs17870417;vdb=variation;vf=27409771

Answer: Hi,

You are using the incorrect dataset, which is why that filter cannot be found; In addition, `ensembl_gene_id` is not an attribute in the SNP *biomaRt*.

Here is a reproducible solution:

require(biomaRt)

ensembl <- useMart('ENSEMBL_MART_SNP', dataset = 'btaurus_snp')

getBM(
attributes = c('refsnp_id',
'chr_name', 'chrom_start','chrom_end',
'allele', 'mapweight', 'validated', 'allele_1', 'minor_allele',
'minor_allele_freq', 'minor_allele_count',
'synonym_name', 'ensembl_gene_stable_id'),
filters = c('snp_filter'),
values = 'rs17870417',
mart = ensembl,
uniqueRows = TRUE)

refsnp_id chr_name chrom_start chrom_end allele mapweight
1 rs17870417 1 78737731 78737731 A/G 1
validated allele_1 minor_allele minor_allele_freq
1 Frequency,Multiple_observations NA NA NA
minor_allele_count synonym_name ensembl_gene_stable_id
1 NA NA ENSBTAG00000044001

Example for Homo sapeins SNP biomaRt here: https://www.biostars.org/p/336724/

Kevin
Question: Hi, I have been trying to manually filter a vcf file by QD score, but I have not been able to do it properly.
I thought of using awk and indicate that a specific field has a value greater than 8 (that is the value I want to filter it), but I have records where the QD variable does not occupy the same position.

1 2422614 . G A 3711.77 . AF=1;DP=120;ExcessHet=3.0103;FS=0;MLEAC=2;MLEAF=1;MQ=60;QD=30.93;SOR=1.773 GT:AD:ADO:DP:GQ:PL 1/1:0,120:0:120:99:0

1 2430617 . C T 2052.76 . AF=0.5;BaseQRankSum=-1.791;ClippingRankSum=0;DP=292;ExcessHet=3.0103;FS=6.978;MLEAC=1;MLEAF=0.5;MQ=60;MQRankSum=0;QD=7.03;ReadPosRankSum=-1.813;SOR=0.652 GT:AD:ADO:DP:GQ:PL 0/1:144,148:0:292:99:3740

Thank you very much

Answer: Thank you for your answers, I found the way of doing it with:

grep -Po 'QD=.*' $FILE | cut -d";" -f1 | sed -r 's/[QD\=]//g' | paste -d'\t' $FILE - | awk 'BEGIN{OFS="\t"}{if ($11>8) {$11=""; print}}'
Question: There is some problem with my samtools_dup rule.

It says **"SyntaxError in line 201 of /data/mypipeline.smk: No rule keywords allowed after run/shell/script/wrapper/cwl in rule samtools_dup. (mypipeline.smk, line 201)"**.

If I google the error, I found that a person says that, in their code, might be that he placed the "log:" after the "shell:" (and that the shell should be the last thing in each rule), but in my code that is not the case. In many other forums I saw people posting it but no answer was recorded. I am not sure where else this mistake can be... any thoughts? Thank you !

Here I post the code for you to take a look.

dup_fun = "rmdup"
# Mark or remove duplicates with Samtools
if ( mrDup == "mark" or mrDup == "rm" ):
rule samtools_dup:
input: f'{bamDir}' + '/{sample}_sort.bam')
params: fun = "rmdup"
output: protected(f'{dupDir}' + "/" + f'{mrDup}dup.bam')
shell: "samtools {params.fun} -s {input} {output}"

Edited: when I delete this rule from the pipeline, the error goes away with it.
What comes next in the code is my bigwig rule:

# Bigwig with Deeptools bamCoverage
if ( bigWig == "yes" ):
rule bigwig:
input:
bam = f'{bamDir}' + '/{sample}.bam',
bai = f'{bamDir}' + '/{sample}.bai'
output: f'{bwDir}' + '/{sample}.bw'
params:
xtnd = bwXtnd ,
bs = binSize ,
nrm = normBW ,
effgs = effGS ,
bl = f'-bl {blBed}'
conda:
"envs/deeptools.yaml"
threads: maxThreads
shell:
"bamCoverage -b {input.bam} -bs {params.bs} --normalizeUsing {params.nrm} --effectiveGenomeSize {params.effgs} -o {output} {params.xtnd} {params.bl}"

Answer: Dmitry Kuzminov solved it in StackOverflow. I share the answer here, in case someone has the same doubt!
It seems there is an extra parenthesis at the end of the "input" line in the samtools_dup rule. I honestly didn't spot it, and also the error message made me think the problem wasn't there but around the "shell" statement.

https://stackoverflow.com/questions/60914204/snakemake-syntaxerror-no-rule-keywords-allowed-after-run-shell-script-wrapper-c/60924399#60924399
Question: I am trying to use MEGAN6. I have assembled transcriptome data that I want to align with nr database. What options do I have for long read alignments (beside LAST) that I can then import to MEGAN6?

Answer: You can use DIAMOND (https://github.com/bbuchfink/diamond) for long read alignment. It now also supports frameshift alignments. Use the options -F 15 --range-culling.
Question: Hello all!

I am current analyzing a recently completed reference genome of a non-model organism. This is a stupid question, but I can't seem to find a good answer online: can I identify miRNAs using just genome sequence (the same way that one does ab initio gene prediction)? The whole-genome analysis papers I have found all just said that non-coding RNA were identified by BLAST alignments with databases like Rfam or mirbase, without much more details. A simple alignment seems to not be the answer for so many reasons....

I am super new to genomic analyses... Any help would be greatly appreciated!

Nancy

Answer: Mostly, miRNA's are identified by short-RNA sequencing and then running pipelines like miRDeep. I guess it is possible in principle to blastN all miRbase precursors against your genome, then further check the hits using cmscan against all Rfam. However, this is not a good approach because it will both suffer from large numbers of false positives and low sensitivity. The question is what you want to do with the predicted miRNAs. If you run target prediction on hypothetical data the result will be totally unreliable, for differential expression analysis, you would need small-RNA seq anyway.

If you want to try how well this works, you could try this pipeline on a well characterized organism:

- download genome sequence
- download all miRBase pre-miRNA
- remove all pre-miRNA that are from this species and keep as test set
- BlastN all precursors against the genome
- extract hit sequences
- annotate hits using cmscan and keep seqs annotated mi-RNA
- compare with test set and evaluate the performance
Question: Hello all my cytometry friends,

I have some fcs files and I can run them through the CATALYST pipeline no errors. But I dont want to run all the cells. Need to clean up just single cells and only live cells, and only cells with X marker. The tutorial does show how to use gating through opencyto , and that works fine. I get no errors making my gates through opencyto and ggctyo. Everything looks great.

BUT

How do I pass the cells from gate into CATALYST? there doesnt seem to be a way to export those cells (or really conserve the sample and groups).

Thanks for any help you can offer.

https://www.bioconductor.org/packages/release/bioc/vignettes/CATALYST/inst/doc/preprocessing.html <- 7.4

https://www.bioconductor.org/packages/release/bioc/vignettes/CATALYST/inst/doc/differential.html <- pipeline

Answer: Solved it.

tl/dr: gh_pop_get_data() will let you export the cells from a gateset from flowWorkspace.

this is not in either the openCyto, ggcyto, or CATAYLST tutorials even though I believe this is a pretty obvious next step function to all these packages. I think I am going to submit a push to the repos to add this in.