Installing Packages in R

R has thousands of packages that extend its use to almost every arena of research. It is highly likely that you will not need most of these packages but it is also likely that you will need several of them (perhaps from 5 to 20) depending on what you plan to do. Most of these packages are available on the the CRAN website.

http://www.cran.r-project.org/web/packages/available_packages_by_name.html

Bioconductor is another source of R packages for bioinformatics-related research packages can be downloaded from its website.

http://www.bioconductor.org/packages/release/bioc/

To use a package, there are two steps:

First Step: Download and Install a Package – you can download a package to a local directory and install it from there using drop down menu OR you can directly install it from the CRAN repository. Depending on the R GUI user interface that you use, the exact steps may be slightly different. Often, first you have to specify which mirror you want to use; chose a mirror that is geographically closer to you for faster downloads. Then you can chose a package from the package list.

I use RStudio GUI. In Rstudio, click on the tab labeled ‘Packages’. If this tab is not visible, press Ctrl+7 and the tab will become visible (usually in the right lower quadrant of the window). From there you can chose install, then type in the name of package (if multiple packages, enter package names separated by space or a comma). Make sure that ‘install dependencies’ box is checked. Make sure that the correct repository and installation location are selected. Then click Install. You can also chose to use installation command directly from the console; the command below will install ggplot2 package:

install.packages("ggplot2")

Note that you need to install packages only once

Second Step: Loading a Package – Installing a package makes it available for later use but packages are not automatically uploaded during a session. Once you have installed a package, you will need to load that package when you need it during a session. To load a package use the function ‘library()’. the following command will load the package ggplot2.

library(ggplot2)

Some may like to use ‘require()’ function instead of ‘library()’. However, see this post for the differences between the two and why one should prefer ‘library()’ over ‘require()’

Personally, I try to load all needed packages at the beginning of a script. However, this strategy may not work if there is an overlap in the names of functions between two packages and you may see a warning “The following objects were masked from ‘package:xyz’:”. 

Some other useful commands to know

.libpaths() # – will give you location of library for packages
library() # – will show you all installed packages
search() # – will show you currently loaded packages

Some Thoughts About Clinical Research

Clinical research requires a wide range of skills. These skill include the ability to work with a wide range of people, to lead teams with people from wide and vastly different backgrounds, to design appropriate studies, to ask right questions, to understand research methods specific to the study question, to develop in-depth content expertise in the area of research focus, to get funding for research projects, to present study results at national meetings, to write manuscripts for publication in peer-review journals, and so on and so forth.

A fundamental skill for a researcher is the ability to knit together the conceptual framework for a study (theory) with appropriate measurement, with the result either supporting or opposing the conceptual framework. The theory should be based on the most current state of knowledge, the data collected should have the ability to test the theory, the statistical models should reflect both the conceptual structure hypothesized to have given rise to the data and the nature of collected data, and the inferences should be based on the data and the tested statistical models. This process is not linear, rather it is a loop in which theoretical aspects inform the collection of data and results of the data analyses help in refining the theory, which generates more testable hypothesis, additional data collection, and so on.

Most research is probabilistic, as opposed to deterministic. In other words, the results we obtain are not always certain; we have to include uncertainty in our analyses and expect some uncertainty in our results and inferences. Thus, we have to accept that our results are unlikely to be laws governing the system we plan to study and more likely to be an approximation of what we expect to find in the real world, with some uncertainty. There are many reasons and sources of this uncertainty some of which can be addressed while others may still be there despite our best attempts.

A researcher should determine whether the interest of research is to build inferences at population level or at the level of individuals unit (often a patient in clinical research). The study design, data collection and analysis, and the inferences may be quite different depending on what is the object of our interest. While we study individuals, our results usually address inferences at population level. In general, it is much easier to predict about the response at a population level, that is on an average, individuals with higher body mass index (say >30) will have higher blood glucose than (say) 126mg/dL. However, it is much difficult to predict with certainty how likely a particular individual with a BMI>30 is to have higher blood glucose level than 126 mg/dL. For a predictor to work well at an individual level, among other things effect size needs to be quite large.

Another important concept is that of causality. While we often have a conceptual model in our mind that A is caused by B, it may be quite difficult to prove except perhaps in a clinical trial setting. There are several factors that can increase the likelihood that the direction of cause and effect in our conceptual model is correct, such as temporality and biological plausibility.  However, often there remains a possibility that B is in fact caused by A or that some other unknown (or unmeasured) factor, C, may be responsible for both A and B. Hence, we often claim an association or correlation between A and B and not causality.

Starting to work with R

There may be some who have just started working with R after someone convinced them that R is the way to go. For those souls, it may be difficult to get started quickly. Below are some of the steps to use to start working with R

Step 1: Go to the CRAN webpage and download the version of R that is appropriate for your operating system - http://cran.r-project.org/

Step 2: Install R

Step 3: Download a GUI for R – While R comes with a GUI, other GUIs are much better. My favorite is RStudio, To download RStudio, go to RStudio website and download the version that is appropriate for your operating system - http://www.rstudio.com/products/rstudio/download/

Step 4: Install RStudio (or a GUI of your choice).

Step 5: Start using RStudio (or GUI of your choice)

That’s it – Good luck!

Fractional Flow Reserve–Guided PCI for Stable CAD

The utility of PCI in stable CAD is unclear. Fractional flow reserve (FFR) may be used to stratify patients between those who will benefit from PCI from those who will not.

A randomized clinical trial published in NEJM examined this question. Patients with FFR<0.8 were randomized to PCI or medical therapy. Although the primary endpoint included a soft endpoint (revascularization) there was significant decrease in the primary endpoint among patients who underwent PCI (8.1 vs 19.5%). Further, the hard endpoints (death or nonfatal myocardial infarction) were also reduced significantly in FFR patients (4.6% vs 8.0%). Quite interestingly, the individuals with FFR greater than 0.8 met primary endpoint as often as those with FFR<0.8 and PCI (8.1 vs. 9.0%) although this was not a direct comparison group.

Interesting results! ……… Perhaps practice changing?

Thunder and Spark–Cluster computing made easy (a little)

Here it is

Epiviz–an interactive visual tool for genomics data

Epiviz is an interactive visualization tool for functional genomics data. It supports genome navigation like other genome browsers, but allows multiple visualizations of data within genomic regions using scatterplots, heatmaps and other user-supplied visualizations.

Cant Imagine this can happen

Even in this day and age when almost everything is available on internet how can this happen? Perhaps no one who was hiring or promoting this dude knew how to use internet. Someone can lie about his/her qualifications (such as getting a PhD) and no one checks before hiring for assistant or associate professor? Shouldn’t folks at NUS, WVU and VCU be ashamed of their gross negligence?

Maximizing Public Data Sources for Sequencing and GWAS Studies

An interesting presentation

CONFIRM-HF - Replete Iron in Heart Failure Patients?

Findings from the CONFIRM-HF (Ferric CarboxymaltOse evaluatioN on perFormance in patients with IRon deficiency in coMbination with chronic Heart Failure) trial, point to a simple and safe solution for heart failure patients with iron deficiency who can experience significant and sustainable improvements in functional capacity and quality of life as well as reduced risk of hospital admission for worsening heart failure by iron supplementation.

CONFIRM-HF is a double-blind, placebo-controlled trial, which enrolled 304 stable, symptomatic heart failure patients from 41 sites across nine European countries. All patients had iron deficiency, defined as a serum ferritin level < 100 ng/mL, or between 100 and 300 ng/mL with transferrin saturation < 20%. Subjects were randomized to receive either intravenous iron (n=152), given as ferric carboxymaltose solution (FCM), or a normal saline placebo (n=152), for 52 weeks. Completion of the six-minute walk test (6MWT) was required at baseline, and the primary endpoint of the study was improvement in this test at week 24.

Compared to placebo-treated subjects, those treated with FCM completed 33 extra meters in the 6MWT at week 24 (p=0.002), 42 extra meters at week 36, and 36 extra meters at week 52 (both p<0.001) and the improvement was seen in all subgroups. Despite the reduction in hospitalizations among FCM-treated patients, the number of deaths was similar in both groups, suggesting a one-year follow-up may not be long enough to detect differences in mortality.
Adverse events were mild, and occurred at a similar rate in both groups.

A New Drug For Heart Failure

ACE inhibitors are standard of therapy in patients with heart failure as clinical trials have shown mortality benefit with these drugs. A trial published in NEJM compared a new drug LCZ696 with enalapril (an ACE Inhibitor) and found this new drug to far better.

This trial randomly assigned 8442 patients with class II, III, or IV heart failure and an ejection fraction of 40% or less to receive either LCZ696 (at a dose of 200 mg twice daily) or enalapril (at a dose of 10 mg twice daily), in addition to recommended therapy.

The trial was stopped early (after a median follow-up of 27 months) because there was clear evidence of benefit of LCZ696 over enalapril. The primary outcome (a composite of death from cardiovascular causes or hospitalization for heart failure) had occurred in 914 patients (21.8%) in the LCZ696 group and 1117 patients (26.5%) in the enalapril group (hazard ratio in the LCZ696 group, 0.80; 95% confidence interval [CI], 0.73 to 0.87; P<0.001). A total of 711 patients (17.0%) receiving LCZ696 and 835 patients (19.8%) receiving enalapril died (hazard ratio for death from any cause, 0.84; 95% CI, 0.76 to 0.93; P<0.001); of these patients, 558 (13.3%) and 693 (16.5%), respectively, died from cardiovascular causes (hazard ratio, 0.80; 95% CI, 0.71 to 0.89; P<0.001). As compared with enalapril, LCZ696 also reduced the risk of hospitalization for heart failure by 21% (P<0.001) and decreased the symptoms and physical limitations of heart failure (P=0.001).

The incidence and type of adverse effects were different between the two drugs; the LCZ696 group had higher proportions of patients with hypotension and nonserious angioedema while enalapril group have higher proportions with renal impairment, hyperkalemia, and cough.

LCZ696 is an investigational combination drug consisting of two antihypertensives (blood pressure lowering drugs), valsartan and AHU-377, in a 1:1 mixture. It is being developed by Novartis. The combination is often described as a dual-acting angiotensin receptor-neprilysin inhibitor although the two effects are achieved by two different molecules. AHU-377 is a prodrug that is activated to LBQ657 by de-ethylation via esterases.LBQ657 inhibits the enzyme neprilysin, which is responsible for the degradation of atrial and brain natriuretic peptide, two blood pressure lowering peptides that work mainly by reducing blood volume.

On the financial side, projections on peak sales from the company's own bullish $2 billion to $5 billion. The highest estimate is by Deutsche's awestruck of $10 billion while the lowest is by EvaluatePharma which pegged nearer-term 2020 sales at a much more modest $1.3 billion.

Colchicine - Postpericardiotomy Syndrome - Postop AFib

Not surprisingly, increased morbidity and perhaps increased mortality, is associated with postpericardiotomy syndrome as well as post-operative development of atrial fibrillation and pericardial and pleural effusions. Colchicine may prevent these complications and COPPS-2 trial looked at this possibility.

This trial was reported in JAMA and also presented at ESC

The results were as below:

PRIMARY ENDPOINT: “The primary end point of postpericardiotomy syndrome occurred in 35 patients (19.4%) assigned to colchicine and in 53 (29.4%) assigned to placebo (absolute difference, 10.0%; 95% CI, 1.1%-18.7%; number needed to treat = 10).

SECONDARY ENDPOINT: “There were no significant differences between the colchicine and placebo groups for the secondary end points of postoperative AF (colchicine, 61 patients [33.9%]; placebo, 75 patients [41.7%]; absolute difference, 7.8%; 95% CI, −2.2% to 17.6%) or postoperative pericardial/pleural effusion (colchicine, 103 patients [57.2%]; placebo, 106 patients [58.9%]; absolute difference, 1.7%; 95% CI, −8.5% to 11.7%), although there was a reduction in postoperative AF in the prespecified on-treatment analysis (placebo, 61/148 patients [41.2%]; colchicine, 38/141 patients [27.0%]; absolute difference, 14.2%; 95% CI, 3.3%-24.7%).

ADVERSE EVENTS: “Adverse events occurred in 21 patients (11.7%) in the placebo group vs 36 (20.0%) in the colchicine group (absolute difference, 8.3%; 95% CI; 0.76%-15.9%; number needed to harm = 12), but discontinuation rates were similar”.

Platelet Glycoprotein IIIa and Aspirin Resistance

Aspirin is the mainstay of treatment for the prevention of cardiovascular disease. It acts by irreversibly inhibiting COX1 enzymes and hence blocking arachidonic acid-thromboxane pathway. Due to lack of a nucleus, platelets cannot generate new COX1 and hence COX1 is inhibited for the lifetime of platelets (8 days). Aspirin treatment results in marked decrease in the excretion of urinary metabolites of thromboxane; the residual excretion is thought to be of endothelial origin where presence of nucleus results in formation of new COX1.

Despite adequate aspirin therapy, a significant number of individuals continue to have higher platelet reactivity and incomplete inhibition of platelet function. Such individuals are also at increased risk of future cardiovascular events. However, the underlying mechanisms that result in higher residual platelet reactivity after aspirin treatment are unclear and are being extensively explored by several researchers.

To identify mechanism of aspirin resistance, this study examined the differences in proteome of 2 aspirin resistant and 4 aspirin sensitive individuals and found that the levels of glycoprotein IIIa were higher in aspirin resistant individuals than in those without aspirin resistance.

Due to small sample size, it is difficult to rule-out the possibility of a type I error, however, considering the role of glycoprotein IIIa in platelet biology, it is conceivable that protein may play a role in aspirin resistant although exact mechanism remains unknown.

Benefits of Aspirin Use in the General Population

Aspirin is perhaps the most commonly used drug world-wide for various ailments. Its prophylactic use in secondary prevention of cardiovascular diseases is well-established, however its use for cardiovascular disease prophylaxis in primary prevention has not been as clear. In particular, some recent meta-analysis has raised concern that aspirin use may not be beneficial for primary prevention of cardiovascular diseases. In support of a role of aspirin use in general population, a systematic review recently concluded:

“For average-risk individuals aged 50–65 years taking aspirin for 10 years, there would be a relative reduction of between 7% (women) and 9% (men) in the number of cancer, myocardial infarction or stroke events over a 15-year period and an overall 4% relative reduction in all deaths over a 20-year period”

In other words, aspirin use is beneficial for both cardiovascular and cancer standpoint, its takes three years to see a beneficial effect, and effect lasts long after aspirin use has been discontinued.

Platelet Diameters in Inherited Thrombocytopenia

An interesting article providing a systematic evidence for what is well-known but not reported in a systematic manner.

“To improve knowledge in this field, we evaluated platelet size on blood films obtained from 376 patients with all 19 forms of IT identified so far and found that these conditions differ not only in mean platelet diameter, but also in platelet diameter distribution width and the percentage of platelets with increased or reduced diameters………….. We also measured platelet diameters in 87 patients with immune thrombocytopenia and identified cutoff values for mean platelet diameter and the percentage of platelets with increased or reduced size….”

iRegulon: From a Gene List to a Gene Regulatory Network Using Large Motif and Track Collections

Identifying master regulators of biological processes and mapping their downstream gene networks are key challenges in systems biology. iRegulon is a software that implements a genome-wide ranking-and-recovery approach to detect enriched transcription factor motifs and their optimal sets of direct targets. The software can be obtained from this website

 

Circleator: Flexible Circular Visualization of Genome-Associated Data

Came through this interesting package which builds graphs similar to Circos but just a little easier

“Circleator is a Perl application that generates circular figures of genome-associated data. It leverages BioPerl to support standard annotation and sequence file formats and produces publication-quality SVG output. It is designed to be both flexible and easy to use. It includes a library of circular track types and predefined configuration files for common use-cases, including: 1. visualizing gene annotation and DNA sequence data from a GenBank flat file,
2. displaying patterns of gene conservation in related microbial strains,
3. showing SNPs and indels relative to a reference genome and gene set, and
4. viewing RNA-Seq plots.”

Greater Collagen-Induced Platelet Aggregation Following Cyclooxygenase 1 Inhibition Predicts Incident Acute Coronary Syndromes

Platelets have several pathways that initiate aggregation such as thrombin-, collagen-, thromboxane-, and ADP-mediated pathways. Individuals vary widely in their ability to have platelet aggregation through each of these pathways. Furthermore, this variability is not correlated. In other words, an individual may have low aggregation though one pathway but may have high aggregation through another pathway. This phenomenon may be more important when we try to measure platelet aggregation the laboratory to assess whether there is increased risk of in vivo platelet aggregation (and hence cardiovascular events).

Collagen is one of the first agonists that initiates platelet aggregation at the site of vessel wall injury. Therefore, examining an association of collagen-mediated platelet aggregation with subsequent cardiovascular events makes sense. Moreover, if we can decrease variability in platelet aggregation by blocking one or another pathway, we may be better able to assess activation through collagen pathway.

Aspirin is commonly used for prevention of cardiovascular disease and works by inhibiting thromboxane-pathway an dis very effective in completely inhibiting activity through this pathway. Thus aspirin can be used to inhibit variability through one pathway and effect of collagen can be studied with fewer interactions. We followed the same logic in this paper where we examined platelet aggregation after 2-week aspirin therapy in healthy individuals. Most of these individuals did not use aspirin after the 2-week study period. During follow-up increased collagen-mediated platelet aggregation was significantly associated with acute coronary syndrome.

“After COX1 pathway inhibition, collagen-induced aggregation was significantly greater in participants with ACS compared with those without (29.0 vs. 23.6 ohms, p < 0.001). In Cox proportional hazards models, this association remained significant after adjusting for traditional cardiovascular risk factors (HR = 1.10, 95%CI = 1.06-1.15; p < 0.001).”

The Science Publishing Complex – <1% publish 42% of all papers

There are not as many successful scientists as we think there are – most are just trying (or leaving).

“Using the entire Scopus database, we estimated that there are 15,153,100 publishing scientists (distinct author identifiers) in the period 1996–2011. However, only 150,608 (<1%) of them have published something in each and every year in this 16-year period (uninterrupted, continuous presence [UCP] in the literature). This small core of scientists with UCP are far more cited than others, and they account for 41.7% of all papers in the same period and 87.1% of all papers with >1000 citations in the same period.”

The End of HDL-raising Therapies?

Patients with cardiovascular disease are at increased risk of subsequent events than individuals without a history of cardiovascular disease despite optimal medical management. Various strategies has been proposed to decrease this increased risk among them increasing HDL.

The HPS2-THRIVE trial examined this question by randomly assigning almost 26,000 individuals with established vascular disease to either placebo or Naicin+laropiprant; a combination that should raise HDL cholesterol. Participants were followed for a median period of 3.9 years. Individuals randomized to the treatment arm had lower LDL (about 10 mg/dL) and higher HDL (about 6 mg.dL) than those who were randomized to placebo. During follow-up, there was no difference in the incidence of major cardiovascular events between the two groups13.2% vs. 13.7%; p = 0.29). On the other hand, individuals randomized to the treatment arm had increased incidence of adverse events such as poor diabetes control or increased incidence of new diagnosis of diabetes.

For now, this trial, puts to rest the use of niacin for decreasing the risk of cardiovascular disease. However, it also raises important questions about the interest in the development of pharmacological therapies directed towards raising HDL-cholesterol. It further questions our current understanding of the role of HDL in the pathogenesis of cardiovascular diseases.

Some interesting articles

Sticky Platelet Syndrome: History and Future Perspectives
in Seminars in thrombosis and hemostasis

The relationship between platelet to lymphocyte ratio and the clinical outcomes in ST elevation myocardial infarction
in Blood coagulation & fibrinolysis

Detection of platelet microRNA expression in patients with diabetes mellitus with or without ischemic stroke
in Journal of Diabetes Complications

Aspirin may modify tumor microenvironment via antiplatelet effect
in Medical Hypothesis

Some interesting articles

 

Optimizing current treatment of gout
in Nature Reviews Rheumatology

 

Anti-diabetic activity of insulin-degrading enzyme inhibitors mediated by multiple hormones
in Nature

 

Health eHeart: A Framingham Study for the Social Media Era?
An interesting concept

High-Platelet Reactivity and Stroke

Nevio Taglieri, and his colleagues from the University of Bologna (Bologna, Italy), conducted a meta-analysis of 14 studies (collectively enrolling 11,959 patients) to evaluate the risk of stroke in patients who had platelet testing while undergoing percutaneous coronary intervention (PCI). Among the studies included in the meta-analysis, prevalence of high platelet reactivity was 30% ±15% (range, 6% to 67%). As expected, prevalence of platelet hyperactivity was higher in studies using VerifyNow than in those using light-transmission aggregometery (LTA) (42% ±13% vs 22% ±10%; P = 0.006). Overall, the annual stroke rate was 0.9%. After pooled analysis, the risk of stroke was higher in patients with high platelet reactivity than in those without (1.2% vs 0.7%; RR 1.84; 95% CI 1.21-2.80).

The study provides interesting insights: first, it confirms the role of platelet aggregation in the athero-thrombo-embolic phenomena. Secondly, it suggests that there is a group of people who, despite having complete (near complete) blockage of P2Y12 receptors, may have higher activity through other platelet aggregation pathways and may benefit from a different drug. Of note, most patients in this meta-analysis were already on clopidogrel and aspirin, two of the several platelet aggregation pathways.

Multilevel Modeling and obtaining P-values

Something quick and to the point

A Gentle Introduction to Learning R

 

A gentle introduction to learning R – good resource for a beginner