60 Matching Annotations
  1. Oct 2020
    1. git push origin master explicitly says "push the local branch 'master' to the remote named 'origin'". This does not define a persistent relationship, it just executes a push this one time. Note that the remote branch is assumed to be named "master". git push -u origin master is the same thing, except it first adds a persistent tracking relationship between your local branch "master" and the remote named "origin". As before, it is assumed the remote branch is named "master". If you have done a push with -u already, then the relationship is already defined. In the future, you can simply say git push or git pull, and git will automatically use the defined remote tracking branch without being told explicitly. You can view your tracking relationships with git branch -vv, which will list your local branches along with their current HEAD commit and, if one is set, the remote tracking branch. Here is an example.
  2. Aug 2019
    1. Fight Aging! on the Kindle

      This links to a non-existing service, sadly.

  3. May 2019
    1. Gene set enrichment analysis is a leading computational method for placing newly acquired high content data in the context of prior biological knowledge (1). Gene set enrichment analysis tools such as DAVID (2), GenePattern (3), WebGestalt (4), AmiGO (5), Babelomics (6), GeneVestigator (7), GOEAST (8), Panther (9) and Enrichr (10,11) have been widely used, demonstrating the utility and relevance of this approach for many diverse studies.

      Is GSEA really the leading method now? Not NEA?

    1. compare between samples. Do you want to do differential expression analysis? If so, the most appropriate tools are DESeq2 and edgeR. These will also deal with normalisation and provide the most sophisticated method so you don't have to worry about absolute vs relative quantification.
    1. From literature (I dig a lot into blogs, papers, etc.. ) and essentially I've summed up the following: Both RPKM and FPKM measures shouldn't be used anymore since they contain an essentially arbitrary scaling factor which is dependent on the average effective length of the transcripts in the underlying sample. Not reproducible, not comparable... TPM measure seems to be more appropriate in dealing with this issue since the sums of normalized reads of each sample are the same across all samples, making it "more suitable" to compare samples. However, its calculation (specifically the denominator term) is also sample dependent and this would be the main reason why I shouldn't use it to directly compare expression values between samples. CPM seems to be a less-normalized measure since it takes into account only library size. On the opposite hand, estimated read count don't normalize samples at all, making it useless to my goal (unless I use some between-sample normalization method). My point is that TPM seems to be the most reliable expression measurement to compare different samples. Still, TMP performs within-sample normalization (although there's a lot of papers comparing samples based on TPM values). Do you think TPM is suitable to compare between-samples expression values?
    2. TPM is not suitable for between-sample normalization because it doesn't account for differences in library composition. It is also very dependent on a few highly expressed genes that may not be the same between your samples. Instead, you could use the TMM-normalized counts or the median of ratio normalization used in DESeq2.
    1. Parametric statistics is a branch of statistics which assumes that sample data comes from a population that can be adequately modelled by a probability distribution that has a fixed set of parameters.[1] Conversely a non-parametric model differs precisely in that the parameter set (or feature set in machine learning) is not fixed and can increase, or even decrease, if new relevant information is collected.[2] Most well-known statistical methods are parametric.[3] Regarding nonparametric (and semiparametric) models, Sir David Cox has said, "These typically involve fewer assumptions of structure and distributional form but usually contain strong assumptions about independencies".[4]

      Non-parametric vs parametric stats

    1. Statistical hypotheses concern the behavior of observable random variables.... For example, the hypothesis (a) that a normal distribution has a specified mean and variance is statistical; so is the hypothesis (b) that it has a given mean but unspecified variance; so is the hypothesis (c) that a distribution is of normal form with both mean and variance unspecified; finally, so is the hypothesis (d) that two unspecified continuous distributions are identical. It will have been noticed that in the examples (a) and (b) the distribution underlying the observations was taken to be of a certain form (the normal) and the hypothesis was concerned entirely with the value of one or both of its parameters. Such a hypothesis, for obvious reasons, is called parametric. Hypothesis (c) was of a different nature, as no parameter values are specified in the statement of the hypothesis; we might reasonably call such a hypothesis non-parametric. Hypothesis (d) is also non-parametric but, in addition, it does not even specify the underlying form of the distribution and may now be reasonably termed distribution-free. Notwithstanding these distinctions, the statistical literature now commonly applies the label "non-parametric" to test procedures that we have just termed "distribution-free", thereby losing a useful classification.

      Non-parametric vs parametric statistics

    2. Non-parametric methods are widely used for studying populations that take on a ranked order (such as movie reviews receiving one to four stars). The use of non-parametric methods may be necessary when data have a ranking but no clear numerical interpretation, such as when assessing preferences. In terms of levels of measurement, non-parametric methods result in ordinal data. As non-parametric methods make fewer assumptions, their applicability is much wider than the corresponding parametric methods. In particular, they may be applied in situations where less is known about the application in question. Also, due to the reliance on fewer assumptions, non-parametric methods are more robust. Another justification for the use of non-parametric methods is simplicity. In certain cases, even when the use of parametric methods is justified, non-parametric methods may be easier to use. Due both to this simplicity and to their greater robustness, non-parametric methods are seen by some statisticians as leaving less room for improper use and misunderstanding. The wider applicability and increased robustness of non-parametric tests comes at a cost: in cases where a parametric test would be appropriate, non-parametric tests have less power. In other words, a larger sample size can be required to draw conclusions with the same degree of confidence.

      Non-parametric vs parametric statistics

    1. annotation parameters include an evaluation of i) staining intensity (negative, weak, moderate or strong), ii) fraction of stained cells (rare, <25%, 25-75% or >75%) and iii) subcellular localization (nuclear and/or cytoplasmic/membranous).

      How are the expression levels for tissues derived from the primary annotations?

    2. For cancer tissues, two cores are sampled from each individual and protein expression is annotated in tumor cells.
    1. The concept of data type is similar to the concept of level of measurement, but more specific: For example, count data require a different distribution (e.g. a Poisson distribution or binomial distribution) than non-negative real-valued data require, but both fall under the same level of measurement (a ratio scale).
    1. Many people think that antibiotics will help fight any kind of infection. But antibiotics are actually only effective against infections caused by bacteria. They can't fight colds because they are powerless against viruses. Studies confirm that antibiotics can't shorten the length of time someone is ill with a simple cold. And antibiotics often have side effects: About 1 out of 10 people have side effects such as diarrhea, nausea, vomiting, headaches and skin rashes. In women, antibiotics can upset the balance of things in the vagina and increase the risk of thrush.Things are different if, as a result of a cold, bacteria spread to the airways and cause an infection there. Then treatment with antibiotics may be considered.The following may be signs of a bacterial infection:Green nasal mucus (snot) or green sputum (coughed-up phlegm) lasting several daysPersistent severe sore throat and pus on tonsilsStuffy nose that won't go away, and severe headache around the foreheadFever, chest pain and trouble breathing
    2. Many people find it pleasant to breathe in (inhale) steam with or without adding things like chamomile or peppermint oil, because the warmth and moisture can have a short-term soothing effect on the mucous membranes lining the nose. But this kind of inhalation doesn't have a clear effect on cold symptoms.Drinking a lot of fluids is also often recommended if you have a cold. There's no scientific proof that this will help, though, so there's no need to force yourself to drink more fluids than you feel like drinking when you have a cold. Still, people often find that hot tea or warm milk have a soothing and warming effect.
    3. None of the currently available treatments can shorten the length of a cold.
    4. Painkillers like acetylsalicylic acid (ASA – the drug in medicines such as Aspirin), ibuprofen and acetaminophen (paracetamol) can relieve cold-related symptoms such as headache, earache and aching joints. They don't help relieve a cough or stuffy nose. These painkillers can also lower a fever.
    5. Most people tend to get enough vitamin C in their usual diet. Still, commercials claim that taking larger amounts of vitamin C in the form of supplements can help relieve cold symptoms. But studies have shown that vitamin C products have no effect on the symptoms and don’t reduce the length of the cold if you start taking them when the cold starts.
    1. I wanted to try this on my laptop, but the only existing Linux implementation I could find involves recompiling the X11 keyboard driver, which is... not easy.So I've written one that uses evdev/uinput and runs in userspace. It's trivial to compile and it works both for X11 and in the Linux console. I've just got a very simple keymap in it to try, hopefully someone else finds it useful.You can find it here: https://github.com/abrasive/spacefn-evdev Logged
    2. I use, and can highly recommend, the Left Alt key. The Alt keys are well-placed for GuiFN-like modifiers, albeit more so on some keyboards than others. You basically want a key on the bottom row that's easy to press with the thumb. The Alt keys make decent thumb keys and I consider it to be a terrible waste not to use them for something more useful. GuiFN operations work best IMO with a left-hand key, and that's why I propose Left Alt rather than Right Alt/AltGr. Also it avoids any problem for those who use AltGr for accented/special characters etc.You may ask: How do I access Left Alt? Well, I simply have moved the function of Left Alt to the Left-Win (Super) key. This setup has the best of both worlds: The convenience of a thumb key but with no need to remap space to be dual-function. Left thumb -> GuiFNRight thumb -> SpaceBoth thumbs have useful jobs.
    1. The RNA-seq data for all 64 cell lines expressing 89% (n=17544) of all protein-coding human genes are presented in the Cell Atlas and can be used as a tool for selection of suitable cell lines for an experiment involving a particular gene or pathway or for further studies on the transcriptome of established human cell lines.
    2. 255 genes found only in cell lines and not tissues 1220 genes found only in tissues and not cell lines
    3. (Uhlén M et al, 2015). The cell lines have been harvested during log phase of growth and extracted high quality mRNA was used as input material for library construction and subsequent sequencing. The expression level of gene-specific transcripts is given as Transcript Per Million (TPM) values. Genes with a TPM value ≥1 are considered as detected. Altogether the transcriptome of 64 cell lines have been analyzed to form a basis of different expression categories.
  4. Apr 2019
    1. I recommend the FireTray extension to you.https://addons.mozilla.org/en-US/firefox/addon/firetray/But some modification is necessary to make it installable for zotero 5.0:1. Download the the extension as an xpi file.2. Open the xpi file with archive manager.3. Edit the file install.rdf to make it installable for zotero 5.0. (Change the maxVerision from 4.* to 5.* in the Zotero section)4. Save the file and archive manager will ask you to update the file (choose update, of course).Install the modified xpi file and restart zotero if needed. Then you can change the preference of FileTray and check the option "start application hidden to tray".

      Worked with Zotero 5 on Ubuntu 18.04

  5. Mar 2019
    1. How UHK Compares Ultimate Hacking Keyboard Matias Ergo Pro ErgoDox EZ Keyboardio Kinesis Advantage Price $250 $200 $250 $329 $299 Truly split Yes Yes Yes Yes No Super compact Yes No No No No Uses Cherry MX-style key-switches Yes No Yes No Yes Mouse control Yes Yes No Yes No Fully programmable Yes No Yes Yes No Easily customizable* Yes No No No No Familiar, staggered layout Yes Yes No No No Open Source Yes No Yes Yes No *”Easily customizable” is defined as one-click configuration
    2. I like that the keyboard halves can get so far apart, but how can I attach the halves where I want them? Like on the side of an armchair? We had this exact use-case in mind when designing the stainless steel threaded inserts. There are four per halve, and allow you to put screws in from the back so you can mount your keyboard to almost anything.
    3. Do you plan to release a Bluetooth version or a version featuring a matrix/columnar layout? We’re considering it but not anytime soon.
  6. Jan 2019
    1. Please check out Software Carpentry as well. This is a great intro that covers not just programming and data analysis (R/Python), but a lot of crucial stuff that every bioinformatician should know but usually is not covered in courses, such as Unix shell Git and version control Unit testing SQL and databases Data management and provenance I also like A Quick Guide to Organizing A Computational Biology Project for organizational techniques that usually have to be learned by experience
    1. In my opinion if you can get enrolled into a degree program for systems biology then that would be best. However, if you are just exploring the field on your own I would recommend going through these resources.Video lectures by Uri AlonVideo Lectures by Jeff GorePrinciples of Synthetic Biology (at edx)Coursera specialization on systems biology.If you are looking for mathematical intensive start with first 2 and if you are looking for biologically intensive begin with last 2. Either way go through all 4 of them as they provide diverse perspective on systems biology which is very important. As you will move through these materials all the necessary supplementary information like books, papers and softwares will be informed within these materials itself.Hope this helps!
    1. Surface/Interior Depth-Cueing Depth cues can contribute to the three-dimensional quality of projection images by giving perspective to projected structures. The depth-cueing parameters determine whether projected points originating near the viewer appear brighter, while points further away are dimmed linearly with distance. The trade-off for this increased realism is that data points shown in a depth-cued image no longer possess accurate densitometric values. Two kinds of depth-cueing are available: Surface Depth-Cueing and Interior Depth-Cueing. Surface Depth-Cueing works only on nearest-point projections and the nearest-point component of other projections with opacity turned on. Interior Depth-Cueing works only on brightest-point projections. For both kinds, depth-cueing is turned off when set to zero (i.e.100% of intensity in back to 100% of intensity in front) and is on when set at 0 < n 100 (i.e.(100 − n)% of intensity in back to 100% intensity in front). Having independent depth-cueing for surface (nearest-point) and interior (brightest-point) allows for more visualization possibilities.
    2. Opacity Can be used to reveal hidden spatial relationships, especially on overlapping objects of different colors and dimensions. The (surface) Opacity parameter permits the display of weighted combinations of nearest-point projection with either of the other two methods, often giving the observer the ability to view inner structures through translucent outer surfaces. To enable this feature, set Opacity to a value greater than zero and select either Mean Value or Brightest Point projection.
    3. Interpolate Check Interpolate to generate a temporary z-scaled stack that is used to generate the projections. Z-scaling eliminates the gaps seen in projections of volumes with slice spacing greater than 1.0 pixels. This option is equivalent to using the Scale plugin from the TransformJ package to scale the stack in the z-dimension by the slice spacing (in pixels). This checkbox is ignored if the slice spacing is less than or equal to 1.0 pixels.
    4. Lower/Upper Transparency Bound Determine the transparency of structures in the volume. Projection calculations disregard points having values less than the lower threshold or greater than the upper threshold. Setting these thresholds permits making background points (those not belonging to any structure) invisible. By setting appropriate thresholds, you can strip away layers having reasonably uniform and unique intensity values and highlight (or make invisible) inner structures. Note that you can also use Image▷Adjust▷Threshold… [T]↑ to set the transparency bounds.
    1. By utilizing the Deeplearning4j library1 for model representation, learning and prediction, KNIME builds upon a well performing open source solution with a thriving community.
    2. It is especially thanks to the work of Yann LeCun and Yoshua Bengio (LeCun et al., 2015) that the application of deep neural networks has boomed in recent years. The technique, which utilizes neural networks with many layers and enhanced backpropagation algorithms for learning, was made possible through both new research and the ever increasing performance of computer chips.
    3. KNIME includes Python in various processing nodes for data processing, model learning and prediction, and the generation of visualizations.
    4. One of KNIME's strengths is its multitude of nodes for data analysis and machine learning. While its base configuration already offers a variety of algorithms for this task, the plugin system is the factor that enables third-party developers to easily integrate their tools and make them compatible with the output of each other.
    5. KNIME allows nodes from different research areas to be mixed to create truly cross-domain workflows.
    6. Apart from the free and open source KNIME Analytics Platform, KNIME also has commercial offerings. The KNIME server provides a platform for sharing workflows. It has a web interface and is connected to a KNIME instance for executing workflows remotely on demand or according to a schedule. Also commercially available are the Big Data Extensions and the KNIME Spark executor.
    7. old nodes in KNIME are never completely removed from the program but are deprecated so that workflows built with old versions can still be run and produce the same results years later.
    8. Passing the data between those tools often involves complex scripts for controlling data flow, data transformation, and statistical analysis. Such scripts are not only prone to be platform dependent, they also tend to grow as the experiment progresses and are seldomly well documented, a fact that hinders the reproducibility of the experiment. Workflow systems such as KNIME Analytics Platform aim to solve these problems by providing a platform for connecting tools graphically and guaranteeing the same results on different operating systems. As an open source software, KNIME allows scientists and programmers to provide their own extensions to the scientific community.
    9. SeqAn implements various applications that can be used for different tasks for example to map reads, apply read error correction, conduct protein searches, run variant detection and many more. However, analysts are not interested in a single execution of one tool but design and execute entire pipelines using different tools for different tasks contained in the pipeline. Often they also require some downstream analysis steps, e.g. computing some statistics, generating reports and so on. Hence it was desirable to add SeqAn applications to the KNIME workflow engine, which offers many additional analysis and data mining features.
    10. The Taverna workbench comes with an integration to myExperiment,13 a website for publishing and sharing scientific workflows. KNIME offers this integration as part of the de.NBI/CIBI plugin.14
    11. The tools presented above are all used in various areas of the life sciences, but their main task is the orchestration of external tools that exchange files with each other. Natively, KNIME goes a different way by encouraging a deep tool integration that is compatible with KNIME's table format. With this approach data are embedded into table cells allowing for easy tool interoperability without the need for file conversions.
    12. Orchestrating the execution of many command line tools is a task for Galaxy, while an analysis of life science data with subsequent statistical analysis and visualization is best carried out in KNIME or Orange. Orange with its “ad-hoc” execution of nodes caters to scientists doing quick analyses on small amounts of data, while KNIME is built from the ground up for large tables and images. Noteworthy is that none of the mentioned tools provide image processing capabilities as extensive as those of the KNIME Image Processing plugin (KNIP).
    13. Compared to other tools KNIME focuses on a deeper integration of tools and tries to manage the data that flows in the workflow by itself. Tools like Galaxy and Taverna, on the other hand, rather orchestrate command line tools that exchange files. Orange is very similar to KNIME in that it has extensive machine learning capabilities, but focuses more on the analysis of smaller data sets. We conclude that there are workflow tools for a variety of different use cases and that it is the scientists task to choose the tool that fits the problem at hand best. While there are certainly overlaps, each tool excels at its intended purpose.
    14. KNIME's network mining extension also has an integration with the open source bioinformatics software platform Cytoscape,9 which can be used to visualize molecular interaction networks and biological pathways. Installing the KNIME Connector plugin in Cytoscape enables users to exchange networks between the two tools.
    15. In conclusion, the KNIME Image Processing extensions not only enable scientists to easily mix-and-match image processing algorithms with tools from other domains (e.g. machine-learning), scripting languages (e.g. R or Python) or perform a cross-domain analysis using heterogenous data-types (e.g. molecules or sequences), they also open the doors for explorative design of bioimage analysis workflows and their application to process hundreds of thousands of images.
    16. In order to further foster this “write once, run anywhere” framework, several independent projects collaborated closely in order to create ImageJ-Ops, an extensible Java framework for image processing algorithms. ImageJ-Ops allows image processing algorithms to be used within a wide range of scientific applications, particularly KNIME and ImageJ and consequently, users need not choose between those applications, but can take advantage of both worlds seamlessly.
    17. Most notably, integrating with ImageJ2 and FIJI allows scientists to easily turn ImageJ2 plugins into KNIME nodes, without having to be able to script or program a single line of code
  7. Dec 2018
    1. The Heads Up Health web app is designed specifically to help you track ketones alongside all of your other vital health data.
    2. urine strips test for unused acetoacetate rather than the main ketone that our body does use, B-hydroxybutyrate.
    3. Why Measuring Ketones MattersBeing able to frequently and accurately measure the level of ketosis can be important for those following the ketogenic diet for clinical purposes as well as for overall health and performance. Due to individual variability in response to different types of food, having frequent feedback will aid individuals towards improving their understanding of how their body responds while increasing motivation and adherence.
    4. Zhou, W., Mukherjee, P., Kiebish, M. A., Markis, W. T., Mantis, J. G., & Seyfried, T. N. (2007). The calorically restricted ketogenic diet, an effective alternative therapy for malignant brain cancer. Nutrition & metabolism, 4(1), 5.
    5. More specific for those following a ketogenic diet, to date, six studies have compared breath acetone levels with blood b-hydroxybutyrate levels and have found a strong correlation (R2= 0.77) (8). One study in particular directly compared blood, breath, and urine samples of 12 healthy individuals undergoing an experimental protocol designed to induce a state of ketosis. The results demonstrated that plasma acetoacetate was best predicted by breath acetone (6). Therefore, it appears that breath acetone assessments are a fast and accurate way to test for the degree of ketosis.
    6. When following a ketogenic diet, acetyl-CoA is produced in the liver from the breakdown of fat and is used to produce acetoacetate, one of three ketone bodies.  From there, acetoacetate can be converted to the other two “ketone bodies”, b-hydroxybutyrate and acetone.  While b-hydroxybutyrate is tested via blood meters, acetone actually diffuses into the lungs and can be measured by testing exhaled breath (7)! Acetone is a byproduct of fat metabolism and is present in the breath of all humans but in different concentrations.
    1. Android Apps Zandy, by Avram Lyon View and edit your Zotero library on your Android phone ZotDroid, by Benjamin Blundell View your Zotero library on your Android device Download and read attachment files
    2. Zotero plugin to sync PDFs from your Zotero library to your (mobile) PDF reader (e.g. an iPad, Android tablet, etc.) and extract annotations to Zotero notes
  8. Nov 2018
    1. At left, an image with surface depth cues at 100% and interior depth cues at 50%. The image on the right has surface depth cues at 100% and interior depth cues at 50%.

      According to the description both images have surface depth cues at 100% and interior depth cues at 50%. It's clearly a typo.