PHYLIFY

[thejmazz]

• parse OBO
• construct DAG
• use HumanSamples.sdrf.xlsx to filter out primary cells, tissues, cell lines. see this comment
• filter out edges/nodes. see heuristics
• Builds COdat
• Nicer docs in readme

[thejmazz]

Notes from discussion with @eugeniabarkova

High-level overview of how GATHER #9 will use this module:

/**
  * @param cellLine The cell line to get a nice background for. Comes as CL:xx or FF:xx id?
  * @param bgLower lower bound on bg. can be # of edges, weight
  * @param bgUpper upper bound on bg. can be # of edges, weight
  * @returns <List> of cell IDs to feed FANTOM
  */
function getBackgroundList(cellLine, bgLower, bgUpper) {}

Some questions to investigate:

• Interoperation between CL:xxx, FF:xxx, and FF:xx-yy IDs
• what is phase1, phase2 subsets?

To produce a nice tree:

• prune important nodes - keep "cells" and "cell transformations"
• prune edges: 1 edge per node
• weight edges if required

Potential prune strategies

• remove edges to common parents

if name != "FF" or name != "CL":
     dont include
     for each child:
          dont include

• "nice edges" tend to occur inline after each other in file, consider pruning with help of indexes
• only take FF:xx-yy? (some come from from infections!)

Notes

• UBERON:= "places"
• UO:= time response to infection
• find out how many FF:xx-yy

[biodim]

There are 1829 FF:X-Y IDs (see human phase 2 @ http://biomart.gsc.riken.jp/). Currently I have mapped 1596 of these samples (fantom_samples dataframe in the fantom_import module), that is 1596 samples have a "cell" - "ontology ID" connection. The ontology file isn't consistent (it uses CD+ for some cells and CD-positive for others), plus it has spelling mistakes in some samples so my scripts didn't catch all the cases. Ill probably go back and manually curate it

A potential issue is separating the mouse samples from the human samples. For example take a look at these two IDs: FF:3632-171A1 and FF:11641-122D3. They look identical, but one is a Mouse Microglial Cell and the other is a human macrophage. From what I understand, all human samples have a 5 digit "start ID" and the human start ID always begins with 1. Technically you dont have to separate the two, because the fantom module only returns the human samples, but if you give it a mouse+human ID mix, it will output something like "150 / 200 Ontology IDs matched", not necessarily because it is missing the other 50 but because that 50 were the mouse IDs

As far as I know phase 2 data is just a bigger dataset. The phase 2 dataset has phase 1 data built into it. I somewhat verified this manually (because I accidentally built the module with phase 1 data and when I upgraded it to phase 2, I saw many of the phase 1 samples). The fantom Module extracts the queries from a "phase1and2combined" file so you don't have to worry about phase selection. Also the length of the "phase1and2combined" file matches the length of phase 2 from biomart

I checked and the fantom module can take lists as an input, but only if each term is seperated by a comma (the spacing doesn't matter). If you subset the data each term won't necessarily be seperated by a comma, but you can use regex to replace all whitespace with "," and it will work. "FF:X-Y FF:X2-Y2 FF:X3-Y3" -> "FF:X-Y,FF:X2-Y2,FF:X3-Y3". Let me know if you need assistance with this data prep step

[thejmazz]

@biodim thanks for the comments. going to dedicate today to this, I'll ping you if I have some questions

[thejmazz]

Notes

list of good FF:A-B

• take all edges with those good
• filter out if parent/sibling has a bad one somewhere, i.e. UO

FF:A-B -> celltype ?

FF:001

A -> 001 ??

heuristics -> cell lineage w/ only cells (e.g. no "1h", "sample")

1. Build from OBO
2. Query: find node
3. find all siblings+children
4. find parents at specified level
5. count number of descendants
6. leaf-list, root-list, components

[burtonlm]

Hey, here are some guidelines (heuristics) that I would follow to clean up the OBO file before converting to a cell lineage graph. Not sure if this helps you... Let me know if you were wanting something else.
-Keep terms that have 'CL' in their ID
-If you don't want to include cell samples, remove any terms with 'FF' in the ID. But maybe you want to include cell samples so that GATHER can use the tree to obtain expression profiles for cells and their backgrounds? I'm not sure about this...
-Remove anatomical parts (terms which contain 'UBERON' in the ID)
-Remove NCBI Taxons, which have 'NCBITaxon' in the ID
-Remove 'mouse' terms (if any part of the term (ID, is_a, etc.) contains 'mouse')
-Remove diseases ('DOID' in the ID)
-Remove molecules ('CHEBI' in the ID)

[hyginn]

Could you post these on your Student Wiki page?
Thanks!

Boris

On Mar 23, 2016, at 5:48 PM, burtonlm notifications@github.com wrote:

Hey, here are some guidelines (heuristics) that I would follow to clean up the OBO file before converting to a cell lineage graph. Not sure if this helps you... Let me know if you were wanting something else.
-Keep terms that have 'CL' in their ID
-If you don't want to include cell samples, remove any terms with 'FF' in the ID. But maybe you want to include cell samples so that GATHER can use the tree to obtain expression profiles for cells and their backgrounds? I'm not sure about this...
-Remove anatomical parts (terms which contain 'UBERON' in the ID)
-Remove NCBI Taxons, which have 'NCBITaxon' in the ID
-Remove 'mouse' terms (if any part of the term (ID, is_a, etc.) contains 'mouse')
-Remove diseases ('DOID' in the ID)
-Remove molecules ('CHEBI' in the ID)

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http://fantom.gsc.riken.jp/5/datafiles/phase2.0/basic/HumanSamples2.0.sdrf.xlsx

Take a look at this file. We can turn this to a R-dataframe and subset out the rows that are not 'time course' or 'fractionation' and you will have a data frame with ff_ontology for only primary cells, tissues, or cell lines.

[thejmazz]

@cL9821 Bless up. Nice find. I've added this to checklist.

PS. I edited your comment to make it not just link to "url"

Does phase 2.0 include all the cell lines and data from phase 1.0 and up?

[thejmazz]

I believe so, @biodim mentioned above that "phase 1 + 2" == "length of phase 2 from biomart"

[thejmazz]

I saved the HumanSamples2.0.sdrf.xlsx as CSV and read.csv() it, and cleaned up the colnames(). Commit soon.

Anyone have any ideas why this newline is introduced? happens when subsetting too. not really a problem since I can just gsub it out, but curious as it seems to randomly come out of nowhere lol (and obscures the intent of the code):

> unique(humanSamples$Category)
[1] tissues                          cell lines                       primary cells                   
[4] time courses                     fractionations and perturbations
Levels: cell lines fractionations and perturbations primary cells time courses tissues
> gsub("\n*$", "", unique(humanSamples$Category))
[1] "tissues"                          "cell lines"                       "primary cells"                   
[4] "time courses"                     "fractionations and perturbations"

EDIT: I discovered levels():

> levels(humanSamples$Category)
[1] "cell lines"                       "fractionations and perturbations" "primary cells"                   
[4] "time courses"                     "tissues"

[thejmazz]

@cL9821 can you try to find the same thing for mouse? So we can remove all mouse IDs. This would solve what @biodim brought up:

A potential issue is separating the mouse samples from the human samples.

We can also just invert the IDs in the HumanSamples2.0 file, but getting the mouse IDs feels more safe.

Any more "meta info" that seems useful for these FF:num and FF:A-B IDs would be great too. I can combine it all into one module so we can effortlessly play with different subset strategies and heuristics.