A bioinformatics programming question. In R, I have a classic speciesA-to-speciesB gene symbol conversion, in this example from mouse to human, which I'm performing using biomaRt, and specifically the getLDS function.
x<-c("Lbp","Ndufv3","Ggt1")
require(biomaRt)
convert<-function(x){
human=useMart("ensembl",dataset="hsapiens_gene_ensembl")
mouse=useMart("ensembl",dataset="mmusculus_gene_ensembl")
newgenes=getLDS(
attributes="mgi_symbol",
filters="mgi_symbol",
values=x,
mart=mouse,
attributesL="hgnc_symbol",
martL=human,
uniqueRows=TRUE
)
humanx<-unique(newgenes)
return(humanx)
}
conversion<-convert(x)
However, I would like to obtain ALL ids present in the linked database: in other words, all mouse/human pairs (in this example). Something to tell the parameter value in the getLDS function to retrieve all ids, not just those specified in the x variable. I am talking about a full map, tens of thousands of lines long, specifying all orthologous relationships between symbols of the two databases.
Any ideas or workarounds? Thanks a lot!
I believe a workaround could be retrieving all IDs from the Biomart database itself, here: https://www.ensembl.org/biomart/martview/
Click on choose database -> Ensembl Genes
Choose dataset -> your selected species (e.g. Mouse genes)
Click on Results -> Check "Unique results only" -> Go
Profit
The list retrieved here has currently 53605 ids, which is, I believe, what you need.
Enjoy!
For context, I asked a question earlier today about matching company names with various variations against a big list with a lot of different company names by using the "stringdist" function from the stringdist package, in order to identify the companies in that big list. This is the question I asked.
Unfortunately, I have not been able to make any improvements to my code, which is why I'm starting to look away from stringdist and try something completely different.
I use Rstudio, and I've noticed that the internal search function in that program is much more effective:
As you can see by the picture, simply searching for the company name in the top right corner gives me the output that I'm looking for, such as the longer name "AMMINEX EMISSIONS..." and "AMMINEX AS".
However, in my previous attempt with the stringdist function (see the link to my previous question) I would get results like "LAMINEX" which are not at all relevant, but would appear before the more useful matches:
So it seems like using the algorithm that Rstudio uses is much more efficient in my case, however I'm not quite sure if it's possible to replicate this algorithm in code form, instead of having to manually search for each company.
Assuming I have a data frame that looks like this:
Company_list <- data.frame(Companies=c('AMMINEX', 'Microsoft', 'Apple'))
What would be a way for me to search for all 3 companies at the same time and get the same type of results in a data frame, like Rstudio does in the first image?
From your description of which results are good or bad, it sounds like you like exact matches of a substring rather than things that are close on those distance measures. In that case you can imitate Rstudio's search function with grepl
library(tidyverse)
demo.df <- data.frame(name = paste(rep(c("abc","jkl","xyz"), each=4), sample(1:100,4*3)), limbs=1:4*3)
demo.df%>%filter(grepl('abc|xyz',name))
where the pipe in the grepl pattern string means 'or', letting you search for multiple companies at the same time. So, to search for the names from the example data frame this string would be paste0(Company_list$Companies,collapse="|") Is this what you're after?
I am using the function query() of package seqinr to download myoglobin DNA sequences from Genbank. E.g.:
query("myoglobins","K=myoglobin AND SP=Turdus merula")
Unfortunately, for a lot of the species I'm looking for I don't get any sequence at all (or for this species, only a very short one), even though I find sequences when I search manually on the website. This is because of searching for "myoglobin" in the keywords only, while often there isn't any entry in there. Often the protein type is only specified in the name ("definition" on Genbank) -- but I have no idea how to search for this.
The help page on query() doesn't seem to offer any option for this in the details, a "generic search" without any "K=" doesn't work, and I haven't found anything via googling.
I'd be happy about any links, explanations and help. Thank you! :)
There is a complete manual for the seqinr package which describes the query language more in depth in chapter 5 (available at http://seqinr.r-forge.r-project.org/seqinr_2_0-1.pdf). I was trying to do a similar query and the description for many of the genes/cds is blank so they don't come up when searching using the k= option. One alternative would be to search for the organism alone, then match gene names in the individual annotations and pull out the accession numbers, which you could then use to re-query the database for your sequences.
This would pull out the annotation for the first gene:
choosebank("emblTP")
query("ACexample", "sp=Turdus merula")
getName(ACexample$req[[1]])
annotations <- getAnnot(ACexample$req[[1]])
cat(annotations, sep = "\n")
I think that this would be a pretty time consuming way to tackle the problem but there doesn't seem to be an efficient way of searching the annotations directly. I'd be interested in any solutions you might come up with.
I was wondering what the most efficient way is to get the available articles for a given nntp group. The method I have implemented works as follows:
(i) Select the group:
GROUP group.name.subname
(ii) Get a list of article numbers from the group (pushed back into a vector 'codes'):
LISTGROUP
(iii) Loop over codes and grab articles (e.g. headers)
for code in codes do
HEAD code
end
However, this doesn't scale well with large groups with many article codes.
In RFC 3977, the GROUP command is indicated as also returning the 'low' and 'high' article numbers. For example,
[C] GROUP misc.test
[S] 211 1234 3000234 3002322 misc.test
where 3000234 and 2002322 are the low and high numbers. I'm therefore thinking of using these instead rather than initially pushing back all article codes. But can these numbers be relied upon? Is 3000234 definitely indicative of the first article id in the above-selected group and likewise is 3002322 definitely indicative of the last article id in the above-selected group or are they just estimates?
Many thanks,
Ben
It turns out I was thinking about this all wrong. All I need to do is
(i) set the group using GROUP
(ii) execute the NEXT command followed by HEAD for however many headers I want (up to count):
for c : count do
articleId <-- NEXT
HEAD articleID
end
EDIT: I'm sure there must be a better way but until anyone suggests otherwise I'll assume this way to be the most effective. Cheers.
I need to automatically match product names (cameras, laptops, tv-s etc) that come from different sources to a canonical name in the database.
For example "Canon PowerShot a20IS", "NEW powershot A20 IS from Canon" and "Digital Camera Canon PS A20IS"
should all match "Canon PowerShot A20 IS". I've worked with levenshtein distance with some added heuristics (removing obvious common words, assigning higher cost to number changes etc), which works to some extent, but not well enough unfortunately.
The main problem is that even single-letter changes in relevant keywords can make a huge difference, but it's not easy to detect which are the relevant keywords. Consider for example three product names:
Lenovo T400
Lenovo R400
New Lenovo T-400, Core 2 Duo
The first two are ridiculously similar strings by any standard (ok, soundex might help to disinguish the T and R in this case, but the names might as well be 400T and 400R), the first and the third are quite far from each other as strings, but are the same product.
Obviously, the matching algorithm cannot be a 100% precise, my goal is to automatically match around 80% of the names with a high confidence.
Any ideas or references is much appreciated
I think this will boil down to distinguishing key words such as Lenovo from chaff such as New.
I would run some analysis over the database of names to identify key words. You could use code similar to that used to generate a word cloud.
Then I would hand-edit the list to remove anything obviously chaff, like maybe New is actually common but not key.
Then you will have a list of key words that can be used to help identify similarities. You would associate the "raw" name with its keywords, and use those keywords when comparing two or more raw names for similarities (literally, percentage of shared keywords).
Not a perfect solution by any stretch, but I don't think you are expecting one?
The key understanding here is that you do have a proper distance metric. That is in fact not your problem at all. Your problem is in classification.
Let me give you an example. Say you have 20 entries for the Foo X1 and 20 for the Foo Y1. You can safely assume they are two groups. On the other hand, if you have 39 entries for the Bar X1 and 1 for the Bar Y1, you should treat them as a single group.
Now, the distance X1 <-> Y1 is the same in both examples, so why is there a difference in the classification? That is because Bar Y1 is an outlier, whereas Foo Y1 isn't.
The funny part is that you do not actually need to do a whole lot of work to determine these groups up front. You simply do an recursive classification. You start out with node per group, and then add the a supernode for the two closest nodes. In the supernode, store the best assumption, the size of its subtree and the variation in it. As many of your strings will be identical, you'll soon get large subtrees with identical entries. Recursion ends with the supernode containing at the root of the tree.
Now map the canonical names against this tree. You'll quickly see that each will match an entire subtree. Now, use the distances between these trees to pick the distance cutoff for that entry. If you have both Foo X1 and Foo Y1 products in the database, the cut-off distance will need to be lower to reflect that.
edg's answer is in the right direction, I think - you need to distinguish key words from fluff.
Context matters. To take your example, Core 2 Duo is fluff when looking at two instances of a T400, but not when looking at a a CPU OEM package.
If you can mark in your database which parts of the canonical form of a product name are more important and must appear in one form or another to identify a product, you should do that. Maybe through the use of some sort of semantic markup? Can you afford to have a human mark up the database?
You can try to define equivalency classes for things like "T-400", "T400", "T 400" etc. Maybe a set of rules that say "numbers bind more strongly than letters attached to those numbers."
Breaking down into cases based on manufacturer, model number, etc. might be a good approach. I would recommend that you look at techniques for term spotting to try and accomplish that: http://www.worldcat.org/isbn/9780262100854
Designing everything in a flexible framework that's mostly rule driven, where the rules can be modified based on your needs and emerging bad patterns (read: things that break your algorithm) would be a good idea, as well. This way you'd be able to improve the system's performance based on real world data.
You might be able to make use of a trigram search for this. I must admit I've never seen the algorithm to implement an index, but have seen it working in pharmaceutical applications, where it copes very well indeed with badly misspelt drug names. You might be able to apply the same kind of logic to this problem.
This is a problem of record linkage. The dedupe python library provides a complete implementation, but even if you don't use python, the documentation has a good overview of how to approach this problem.
Briefly, within the standard paradigm, this task is broken into three stages
Compare the fields, in this case just the name. You can use one or more comparator for this, for example an edit distance like the Levenshtein distance or something like the cosine distance that compares the number of common words.
Turn an array fo distance scores into a probability that a pair of records are truly about the same thing
Cluster those pairwise probability scores into groups of records that likely all refer to the same thing.
You might want to create logic that ignores the letter/number combination of model numbers (since they're nigh always extremely similar).
Not having any experience with this type of problem, but I think a very naive implementation would be to tokenize the search term, and search for matches that happen to contain any of the tokens.
"Canon PowerShot A20 IS", for example, tokenizes into:
Canon
Powershot
A20
IS
which would match each of the other items you want to show up in the results. Of course, this strategy will likely produce a whole lot of false matches as well.
Another strategy would be to store "keywords" with each item, such as "camera", "canon", "digital camera", and searching based on items that have matching keywords. In addition, if you stored other attributes such as Maker, Brand, etc., you could search on each of these.
Spell checking algorithms come to mind.
Although I could not find a good sample implementation, I believe you can modify a basic spell checking algorithm to comes up with satisfactory results. i.e. working with words as a unit instead of a character.
The bits and pieces left in my memory:
Strip out all common words (a, an, the, new). What is "common" depends on context.
Take the first letter of each word and its length and make that an word key.
When a suspect word comes up, looks for words with the same or similar word key.
It might not solve your problems directly... but you say you were looking for ideas, right?
:-)
That is exactly the problem I'm working on in my spare time. What I came up with is:
based on keywords narrow down the scope of search:
in this case you could have some hierarchy:
type --> company --> model
so that you'd match
"Digital Camera" for a type
"Canon" for company and there you'd be left with much narrower scope to search.
You could work this down even further by introducing product lines etc.
But the main point is, this probably has to be done iteratively.
We can use the Datadecision service for matching products.
It will allow you to automatically match your product data using statistical algorithms. This operation is done after defining a threshold score of confidence.
All data that cannot be automatically matched will have to be manually reviewed through a dedicated user interface.
The online service uses lookup tables to store synonyms as well as your manual matching history. This allows you to improve the data matching automation next time you import new data.
I worked on the exact same thing in the past. What I have done is using an NLP method; TF-IDF Vectorizer to assign weights to each word. For example in your case:
Canon PowerShot a20IS
Canon --> weight = 0.05 (not a very distinguishing word)
PowerShot --> weight = 0.37 (can be distinguishing)
a20IS --> weight = 0.96 (very distinguishing)
This will tell your model which words to care and which words to not. I had quite good matches thanks to TF-IDF.
But note this: a20IS cannot be recognized as a20 IS, you may consider to use some kind of regex to filter such cases.
After that, you can use a numeric calculation like cosine similarity.