Comments (27)
Thank you. It's very helpful to have your comments on the outputs of different methods. I'll use the current version and fractions.
from immunedeconv.
@federicomarini, can this be closed?
from immunedeconv.
thanks for reporting this -- it seems nobody ever tried the totalcells
option in immunedeconv before.
It certainly isn't implemented correctly in immunedeconv. Also this functionality requires an additional input file as shown in the original quantiseq documentation.
If you really need that feature I have to refer you to the original quantiseq pipeline for now.
@federicomarini is working on an improved R version of quanTIseq. @federicomarini: will it support the totalcells feature?
from immunedeconv.
this is definitely a bug that @federicomarini and I are fixing in the new quanTIseq code.
We will post an update soon.
Sorry for the inconvenience,
Francesca
from immunedeconv.
Thanks. Practically, it's extremely helpful to have different methods available within 'immunedeconv'.
'btotalcells' is described in help docs for deconvolute_quantiseq_default - "btotalcells = compute cell densities instead of fractions Default: FALSE"
I'm uncertain whether I need 'btotalcells'. I may not need it. But I'm uncertain about the 'units' of the results that different methods give -- so thought I'd try 'btotalcells = TRUE', and would compare its outputs across a set of expression subtypes, where I have purity values across the subtypes from a different team. I've made this comparison for other deconvolution methods within immunedeconv.
from immunedeconv.
If it seems practical to you to fix 'btotalcells', please fix it. An less costly alternative would be to NOT fix it... I do NOT know that I need it.
from immunedeconv.
In the current implementation here in immunedeconv
it is looking for a file which is not there - but it is also containing information regarding the original images where you could compute the cell densities.
@FFinotello and I can chip in here once we have that running for quantiseqr
😉
from immunedeconv.
Hello @AGordonRobertson and apologies for my late reply.
We are working on an improved version of the quanTIseq R code that should be ready soon and will, of course, also fix this bug.
Regarding your doubts about the "totalcells" argument, it was designed to pass to quanTIseq information on total cell densities estimated from images of tumor tissue-slides (e.g. H&E). This information is needed only if you want to scale the cell fractions (i.e. referred to total cells in a sample) to cell densities (i.e. cell counts per area as in pathology images), as explained in Fig. 1a from the original quanTIseq paper.
If you want to quantify the cellular composition of a sample using different methods applied only to transcriptomics data, you do not need (and have) cell densities. So, I would suggest keeping the default settings, regardless of the current bug.
In this way, you will get cell fractions that you can compare with the cell fractions or scores from the other methods.
Nevertheless, please remember that the outputs from the various methods can be by definition very different, and only EPIC and quanTIseq provide cell fractions referred to the overall cellular content of a sample (see also Table 1 from immunedeconv paper).
I hope this helps and please feel free to reach out again if you have any doubts.
Best,
Francesca
from immunedeconv.
@federicomarini, is quantiseqR ready and should we switch to it in immunedeconv?
from immunedeconv.
I think so, quantiseqr
is pretty much ready for prime time.
- it's on Bioconductor
- has already a couple of speedup optimizations
- can handle matrix, eSets and also SummarizedExperiment objects
Especially this third point is something that can be worth having immunedeconv
-wise, and so for the next projects. I think there's a nice advantage in handling the metadata in integrated containers that even take care of the matches, subsettings, and so on.
@FFinotello - (y)our call 😉
from immunedeconv.
@federicomarini, expressionSets are already supported:
https://github.com/icbi-lab/immunedeconv/blob/c70539f2b08901687561dca755337fc6a5130440/R/immune_deconvolution_methods.R#L331-L333
I've never worked with SummarizedExperiments, but I guess it should be trivial to support them.
from immunedeconv.
from immunedeconv.
from immunedeconv.
from immunedeconv.
I can not see the question in your message, @AGordonRobertson - looks like to me you are just reporting the part from the Q&A in the vignette?
from immunedeconv.
from immunedeconv.
- Makes sense, but I'd argue it is more an aspect to curate in
immundeconv
- I am "only" a contributor of the quanTIseq porting 😉 - I agree this should be documented somewhere - in the end, we want the users to properly use the package!
Wild guess: if you use otherwise normalized data, I don't think you'd be too much far away from the expected results, but you'd be using something in a sub-optimal way. And in full honesty, if one can, one should avoid it 👍
from immunedeconv.
I agree this should be documented somewhere - in the end, we want the users to properly use the package!
Wild guess: if you use otherwise normalized data, I don't think you'd be too much far away from the expected results, but you'd be using something in a sub-optimal way. And in full honesty, if one can, one should avoid it +1
This is documented here but it keeps coming up. Where do you think we should add it to make it more visible? We were thinking about an FAQ section at some point: #61
from immunedeconv.
from immunedeconv.
I agree this should be documented somewhere - in the end, we want the users to properly use the package!
Wild guess: if you use otherwise normalized data, I don't think you'd be too much far away from the expected results, but you'd be using something in a sub-optimal way. And in full honesty, if one can, one should avoid it +1This is documented here but it keeps coming up. Where do you think we should add it to make it more visible? We were thinking about an FAQ section at some point: #61
I could argue it is well visible already, but maybe we could
- point out somewhere in the vignette as well (Q&A sounds good)
- in the function documentation of the wrapper, we can have a dedicated
Details
section for that
I mean, in the end it is up to the user to read the fantastic manual 😬
from immunedeconv.
from immunedeconv.
from immunedeconv.
I am guessing, but the FLD is the fragment length distribution? And that would be something you'd subtract from the length to "make it the effective length".
Apart from that: it can be that in some samples the length of the transcript might not really be the same. If using TPM quantifications from salmon or kallisto (as recommended, basically through the original concept of quanTIseq as a whole pipeline), then this aspect is already taken care of.
from immunedeconv.
from immunedeconv.
I'd suggest you can have a look at this very informative post by @rob-p for an explanation on the effective length 😉
http://robpatro.com/blog/?p=235#efflen
from immunedeconv.
from immunedeconv.
Guess so - ended up spacing across a couple of topics in the end, but seems done to me
from immunedeconv.
Related Issues (20)
- Modernize CI HOT 4
- Support for mouse deconvolution HOT 3
- patched mcp counter dependencies
- quantiseqr / immundeconv differences HOT 2
- quanTIseq update HOT 2
- Error in Installation of R Package HOT 2
- Error in the mapping of cell types HOT 1
- TPM-normalized HOT 1
- genes not being identical between reference and my gene expression data HOT 15
- GLIBCXX_3.4.30 not found when trying to use timer HOT 1
- Issue with ESTIMATE inner_join() HOT 3
- Wrong names of ESTIMATE scores HOT 7
- deconvolute_consensus_tme won't work with "Unfiltered" indications HOT 1
- Error message when running CIBERSORT HOT 16
- Error in deconvolute_cibersort HOT 7
- Hi what is the original article to BASE method for deconvolute_mouse()? HOT 1
- Immunedeconv terminates for (non-)duplicate gene symbols HOT 7
- Error by using mouse_genes_to_human function HOT 1
- Unable to load immunedeconv HOT 12
- Error running cibersort HOT 3
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from immunedeconv.