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Trying to squeeze sense out of chemical data

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Exploring ChEMBL Targets with Neo4j

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As part of an internal project I’ve recently started working with Neo4j for representing and querying relationships between entities (targets, compounds, etc.). What has really caught my attention is the Cypher graph query language – by allowing you to construct queries using graph notation, many tasks that would be complex or tedious in a traditonal RDBMS become much easier.

As an example, I loaded the ChEMBL target hierarchy and the targets as a graph. On it’s own it’s not particularly useful – the real utility arises when other datasets (and datatypes) are linked to the targets. But even at this stage, one can easily ask questions such as

Find all kinase proteins

which is simply a matter of identifying proteins that have a direct path to the Kinase target class.

Assuming you have ChEMBL loaded in to a MySQL database, you can generate a Neo4j graph database containing the targets and classification hierarchy using code from the neo4jexpt repository. Simply compile and run as (appropriately changing host name, user and password)

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$ mvn package
$ java -Djdbc.url="jdbc:mysql://host.name/chembl_20?user=USER&password=PASS" \
       -jar target/neo4j-ctl-1.0-SNAPSHOT.jar graph.db

Once complete, you should see a folder named graph.db. Using the Neo4j application you can then explore the graph in your browser by executing Cypher queries. For example, lets get the graph of the entire ChEMBL target classification hierarchy (and ensuring that we don’t include actual proteins)

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MATCH (n {TargetType:'TargetFamily'})-[r]-(m {TargetType:'TargetFamily'})
  RETURN r

(The various annotations such as TargetType and TargetFamily are based on my code). When visualized we get

prolif-bp

Lets get more specific, and extract the kinase portion of the classification hierarchy

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MATCH (n {TargetType:'TargetFamily'}),
      (m {TargetID:'Kinase'}),
      p = shortestPath( (n)-[:ChildOf*]->(m) )
  RETURN p

prolif-bp

Given that we’ve linked the protein themselves to the target classes, we can now ask for all proteins that are kinases

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MATCH (m {TargetType:'MolecularTarget'}),
      (n {TargetID:'Kinase'}),
      p = shortestPath( (m)-[*]->(n) )
  RETURN m

Or identify the target classes that are linked to more than 25 proteins

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MATCH ()-[r1:IsA]-(m:TargetBiology {TargetType:"TargetFamily"})
  WITH m, COUNT(r1) AS relCount
  WHERE relCount > 25
  RETURN m

which gives us a table of target classes and counts, part of which is shown below

prolif-bp

Overall this seems to be a very powerful platform to integrate data sources and types and effectively query for relationships. The browser based view is useful to practice Cypher and answer questions of the dataset. But a REST API is available as well as other tools such as Gremlin that allow for much more flexible applications and sophisticated queries.

Written by Rajarshi Guha

November 14th, 2015 at 6:10 pm

Surveying the Opinion of Chemists

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As part of a project I was wondering about reports of surveys that collected chemists assessments of differnt things. More specifically, I wasn’t looking for crowd-sourcing efforts for data curation (such as the in the Spectral Game) or data collection. Rather, I was interested in reports where somebody asked a group of chemists what they thought of some particular molecular “feature”. Here, “feature” is pretty broadly defined and could range from the quality of a probe molecule to whether a molecule is complex or not.

Surveying the literature (and with pointers from @dgelemi, @baoilleach, Jun Li, @georgeisyourman and @DrBostrom) here’s the following papers:

The number of people surveyed across these studies ranges from less than 10 to more than 300. Recently there appears to be a trend towards developing predictive models based on the results of such surveys. Also, molecular complexity seems pretty popular. Modeling opinion is always a tricky thing, though in my mind some aspects (e.g., complexity, diversity) lend themselves to more robust models than others (e.g., quality of a probe).

If there are other examples of such surveys in chemistry, I’d appreciate any pointers

Written by Rajarshi Guha

November 7th, 2015 at 1:21 pm

Post-doc (Molecular Informatics) Opening at NCATS

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I have a post-doc opening in the Informatics group at NCATS, to work on computational aspects of high throughput combination screening – topics will include predicting drug combination response, visualizing large combination screens (> 5000 combinations) and so on. The NCATS combination screening platform thas tested more than 65,000 compound combinations (in checkerboard style which means more than 4.5M individual dose combinations) along with single agent dose responses. You can view publicly released data at https://tripod.nih.gov/matrix-client.

The NCATS Informatics group is a collection of very smart people, with wide ranging interests in molecular informatics. We work closely with colleagues in biology and chemistry. As a result, we eat a lot of our own dog food. In addition, we’re committed to implementing our ideas in publicly available software tools as well as publishing in journals.

Lots of data, great people and tough problems. If this piques your interest visit the job posting for more details

Written by Rajarshi Guha

February 6th, 2015 at 8:52 pm

Ridit Analysis

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While preparing material for an R workshop I ran at NCATS I was pointed to the topic of ridit analysis. Since I hadn’t heard of this technique before I decided to look into it and investigate how R could be used for such an analysis (and yes, there is a package for it).

Why ridit analysis?

First, lets consider why one might consider ridit analysis. In many scenarios one might have data that is categorical, but the categories are ordered. This type of data is termed ordinal (sometimes also called “nominal with order”). An example might be a trial of an analgesics ability to reduce pain, whose outcome could be no pain, some pain, extreme pain. While there are three categories, it’s clear that there is an ordering to them. Analysis of such data usually makes use of methods devised for categorical data – but such methods will not make use of the information contained within the ordering of the categories. Alternatively, one might numerically code the groups using 1, 2 and 3 and then apply methods devised for continuous or discrete variables. This is not appropriate since one can change the results by simply changing the category coding.

Ridit analysis essentially transforms ordinal data to a probability scale (one could call it a virtual continuous scale). The term actually stands for relative to an identified distribution integral transformation and is analoguous to probit or logit. (Importantly, ridit analysis is closely related to the Wilcoxon rank sum test. As shown by Selvin, the Wilcoxon test statistic and the mean ridit are directly related).

Definitions

Essentially, one must have at least two groups, one of which is selected as the reference group. Then for the non-reference group, the mean ridit is an

estimate of the probability that a random individual from that group will have a value on the underlying (virtual) continuous scale greater than or equal to the value for a random individual from the reference group.

So if larger values of the underlying scale imply a worse condition, then the mean ridit is the probability estimate that the random individual from the group is worse of than a random individual from the reference group (based on the interpretation from Bross). Based on the definition of a ridit (see here or here), one can compute confidence intervals (CI) or test the hypothesis that different groups have equal mean ridits. Lets see how we can do that using R

Mechanics of ridit analysis

Consider a dataset taken from Donaldson, (Eur. J. Pain, 1988) which looked at the effect of high and low levels of radiation treatment on trials participants’s sleep. The numbers are counts of patients:

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sleep <- data.frame(pain.level=factor(c('Slept all night with no pain',
                      'Slept all night with some pain',
                      'Woke with pain - medication provided relief',
                      'Woke with pain - medication provided no relief',
                      'Awake most or all of night with pain'),
                      levels=c('Slept all night with no pain',
                        'Slept all night with some pain',
                        'Woke with pain - medication provided relief',
                        'Woke with pain - medication provided no relief',
                        'Awake most or all of night with pain')),
                    low.dose=c(3, 10, 6,  2, 1),
                    high.dose=c(6,10,2,0,0))

Here the groups are in the columns (low.dose and high.dose) and the categories are ordered such tat Awake most or all of night with pain is the “maximum” category. To compute the mean ridits for each dose group we first reorder the table and then convert the counts to proportions and then compute ridits for each category (i.e., row).

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## reorder table
sleep <- sleep[ length(levels(sleep$pain.level)):1, ]
## compute proportions
sleep$low.dose.prop <- sleep$low.dose / sum(sleep$low.dose)
sleep$high.dose.prop <- sleep$high.dose / sum(sleep$high.dose)
## compute riddit
ridit <- function(props) { ## props should be in order of levels (highest to lowest)
  r <- rep(-1, length(props))
  for (i in 1:length(props)) {
    if (i == length(props)) vals <- 0
    else vals <- props[(i+1):length(props)]
    r[i] <- sum(vals) + 0.5*props[i]
  }
  return(r)
}
sleep$low.dose.ridit <- ridit(sleep$low.dose.prop)
sleep$high.dose.ridit <- ridit(sleep$high.dose.prop)

The resultant table is below

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                                      pain.level low.dose high.dose low.dose.prop high.dose.prop low.dose.ridit high.dose.ridit
5           Awake most or all of night with pain        1         0    0.04545455      0.0000000     0.97727273       1.0000000
4 Woke with pain - medication provided no relief        2         0    0.09090909      0.0000000     0.90909091       1.0000000
3    Woke with pain - medication provided relief        6         2    0.27272727      0.1111111     0.72727273       0.9444444
2                 Slept all night with some pain       10        10    0.45454545      0.5555556     0.36363636       0.6111111
1                   Slept all night with no pain        3         6    0.13636364      0.3333333     0.06818182       0.1666667

The last two columns represent the ridit values for each category and can be interpreted as

a probability estimate that an individuals value on the underlying continuous scale is less than or equal to the midpoint of the corresponding interval

The next step (and main point of the analysis) is to compute the mean ridit for a group (essentially the sum of the category proportions for that group weighted by the category ridits in the reference group) , based on a reference. In this case, lets assume the low dose group is the reference.

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mean.r.high <- sum(with(sleep, high.dose.prop * low.dose.ridit))

which is 0.305, and can be interpreted as the probability that a patient receiving the high dose of radiation will experience more sleep interference than a patient in the low dose group. Importantly, since ridits are estimates of probabilities, the complementary ridit (i.e., using the high dose group as reference) comes out to 0.694 and is the probability that a patient in the low radiation dose group will experience more sleep interferance than a patient in the high dose group.

Statistics on ridits

There are a number of ways to compute CI’s on mean ridits or else test the hypothesis that the mean ridits differ between \(k\) groups. Donaldsons method for CI calculation appears to be restricted to two groups. In contrast, Fleiss et al suggest an alternate method based on. Considering the latter, the CI for a group vs the reference group is given by

\(\overline{r}_i \pm B \frac{\sqrt{n_s +n_i}}{2\sqrt{3 n_s n_i}}\)

where \(\overline{r}_i\) is the mean ridit for the \(i\)’th group, \(n_s\) and \(n_i\) are the sizes of the reference and query groups, respectively and \(B\) is the multiple testing corrected standard error. If one uses the Bonferroni correction, it would be \(1.96 \times 1\) since there is only two groups being compared (and so 1 comparison). Thus the CI for the mean ridit for the low dose group, using the high dose as reference is given by

\(0.694 \pm 1.96 \frac{\sqrt{18 + 22}}{2\sqrt{3 \times 18 \times 22}}\)

which is 0.515 to 0.873. Given that the interval does not include 0.5, we can conclude that there is a statistically significant difference (\(\alpha = 0.05\)) in the mean ridits between the two groups. For the case of multiple groups, the CI for any group vs any other group (i.e., not considering the reference group) is given by

\( (\overline{r}_i – \overline{r}_j + 0.5) \pm B \frac{\sqrt{n_i + n_j}}{2\sqrt{3 n_i n_j}}\)

Fleiss et al also describes how one can test the hypothesis that the mean ridits across all groups (including the reference) are equal using a \(\chi^2\) statistic. In addition, they also describe how one can perform the same test between any group and the reference group.

R Implementation

I’ve implemented a function that computes mean ridits and their 95% confidence interval (which can be changed). It expects that the data is provided as counts for each category and that the input data.frame is ordered in descending order of the categories. You need to specify the variable representing the categories and the reference variable. As an example of its usage, we use the dataset from Fleiss et al which measured the degree of pain relief provided by different drugs after oral surgery. We perfrom a ridit analysis using aspirin as the reference group:

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dental <- data.frame(pain.relief = factor(c('Very good', 'Good', 'Fair', 'Poor', 'None'),
                       levels=c('Very good', 'Good', 'Fair', 'Poor', 'None')),
                     ibuprofen.low = c(61, 17, 10, 6, 0),
                     ibuprofen.high = c(52, 25, 5, 3, 1),
                     Placebo = c(32, 37, 10, 18, 0),                    
                     Aspirin = c(47, 25, 11, 4, 1)
                     )
ridit(dental, 'pain.relief', 'Aspirin')

which gives us

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$category.ridit
  pain.relief ibuprofen.low ibuprofen.high    Placebo
1   Very good    0.67553191    0.697674419 0.83505155
2        Good    0.26063830    0.250000000 0.47938144
3        Fair    0.11702128    0.075581395 0.23711340
4        Poor    0.03191489    0.029069767 0.09278351
5        None    0.00000000    0.005813953 0.00000000

$mean.ridit
 ibuprofen.low ibuprofen.high        Placebo
     0.5490812      0.5455206      0.3839620

$ci
           group       low      high
1  ibuprofen.low 0.4361128 0.6620496
2 ibuprofen.high 0.4300396 0.6610016
3        Placebo 0.2718415 0.4960826

The results suggest that patients receiving either dose of ibuprofen will get better pain relief compared to aspirin. However, if you consider the CI’s it’s clear that they both contain 0.5 and thus there is no statistical difference in the mean ridits for these two doses, compared to aspirin. On the other hand, placebo definitely leads to less pain relief compared to aspirin.

Written by Rajarshi Guha

January 18th, 2015 at 7:26 pm

Summarizing Collections of Curves

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boxplot

I was browsing live notes from the recent IEEE conference on visualization and came across a paper about functional boxplots. The idea is an extension of the boxplot visualization (shown alongside), to a set of functions. Intuitively, one can think of a functional box plot as specific envelopes for a set of functions. The construction of this plot is based on the notion of band depth (see the more general concept of data depth) which is a measure of how far a given function is from the collection of functions. As described in Sun & Genton the band depth for a given function can be computed by randomly selecting \(J\) functions and identifying wether the given function is contained within the minimum and maximum of the \(J \) functions. Repeating this multiple times, the fraction of times that the given function is fully contained within the \(J\) random functions gives the band depth, \(BD_j\). This is then used to order the functions, allowing one to compute a 50% band, analogous to the IQR in a traditional boxplot. There are more details (choice of \(J\), partial bounding, etc.) described in the papers and links above.

drc-bpMy interest in this approach was piqued since one way of summarizing a dose response screen, or comparing dose response data across multiple conditions is to generate a box plot of a single curve fit parameter – say, \(\log IC_{50} \). But if we wanted to consider the curves themselves, we have a few options. We could simply plot all of them, using translucency to avoid a blob. But this doesn’t scale visually. Another option, on the left, is to draw a series of box plots, one for each dose, and then optionally join the median of each boxplot giving a “median curve”. While these vary in their degree of utility, the idea of summarizing the distribution of a set of curves, and being able to compare these distributions is attractive. Functional box plots look like a way to do this. (A cool thing about functional boxplots is that they can be extended to multivariate functions such as surfaces and so on. See Mirzargar et al for examples)

Computing \(BD_j\) can be time consuming if the number of curves is large or \(J\) is large. Lopez-Pintado & Jornsten suggest a simple optimization to speed up this step, and for the special case of \(J = 2\), Sun et al proposed a ranking based procedure that scales to thousands of curves. The latter is implemented in the fda package for R which also generates the final functional box plots.

As an example I considered 6 cell proliferation assays run in dose response, each one running the same set of compounds, but under different growth conditions. For each assay I only considered good quality curves (giving from 349 to 602 curves). The first plot compares the actives identified in the different growth conditions using the \(\log IC_{50}\), and indicates a statistically significant increase in potency in the last three conditions compared to the first three.

prolif-bp

In contrast, the functional box plots for the 6 assays, suggest a somewhat different picture (% Response = 100 corresponds to no cell kill and 0 corresponds to full cell kill).

prolif-fpb

The red dashed curves correspond to outliers and the blue lines correspond to the ‘maximum’ and ‘minimum’ curves (analogous to the whiskers of the traditional boxplot). Importantly, these are not measured curves, but instead correspond to the dose-wise maximum (and minimum) of the real curves. The pink region represents 50% of the curves and the black line represents the (virtual) median curve. In each case the X-axis corresponds to dose (unlabeled to save space). Personally, I think this visualization is a little cleaner than the dose-wise box plot shown above.

The mess of red lines in the plot 1 suggest an issue with the assay itself. While the other plots do show differences, it’s not clear what one can conclude from this. For example, in the plot for 4, the dip on the left hand side (i.e., low dose) could suggest that there is a degree of cytotoxicity, which is comparatively less in 3, 5 and 6. Interestingly none of the median curves are really sigmoidal, suggesting that the distribution of dose responses has substantial variance.

Written by Rajarshi Guha

November 30th, 2014 at 3:02 pm