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• Do I need data from previous experiments to use AutoGate?

  No. You can start from scratch, creating new experiments. But you will save enormous time and effort by making use of previous experiments even if you have to borrow them.

• From which formats can AutoGate read data from previous experiments?

  AutoGate can access fcs files from your flow cytometry instruments and files from DiVa, CellQuest Pro, and FlowJo. If your previous experiments are in any other format, please contact us.

• Could you elaborate on the efficacy of this software to handle large datasets. (It would be good to discuss computational complexity of EPP, QF match, QF tree and MDS; also, it would be good to provide some estimates, for example, ...it takes that many minutes to run each part of the pipeline on 500k cells...)?

  The software under discussion here is the AutoGate implementation of the paper’s pipeline. Previously AutoGate supported semi-supervised analysis after its first release in March 2014. The pipeline is mostly programmed in MatLab.

  The performance of the pipeline steps are as follows

  1) Epp. The Epp method will be published separately but we summarize it here to help the reviewers understanding.

  a) Epp is designed to find subsets based on phenotyping markers and scatter parameters but NOT based on stimulation or intra-cellular markers.

  b) Performance impacts come from the number of dimensions, cells and clusters per 2D projection

  i) Dimensions: each additional dimension increases performance quadratically by expanding the number of 2D projections. 11D has 55 2D pairings, 12D has 66,13D has 78….. 30D has 435, 31 requires has 465

  ii) Cells: increase in cell counts has a linear impact on the cost of creating DBM’s density grid. Most of the processing after this remains the same since it works with the grid. There is also linear cost related to memory use.

  iii) Clusters per 2D projection: each additional cluster that DBM finds impacts performance

  (1) Combinatorically for Epp’s contiguity check. Epp checks all 2-way splits of a 2D projection’s clusters to see if the clusters with each side of the split are contiguous with each other.

  (2) Near-combinatorically for computing the Separatrix of contiguous splits.

  (3) The combinatoric cost can be illustrated with a 4-cluster projection, this entails the following combinations: (a) 1 + (2 3 4) (b) (1 2) + (3 4) (c) (1 3) + (2 4) (d) (1 4) + (2 3) (e) (1 2 3) + 4 (f) (1 2 4) + 3 (g) (1 3 4) + 2 iv)

  The number of clusters per 2D projection is affected by

  (1) Cluster detail level. The 5 steps of very low to very high constitutes a narrowing of the bandwidth on the density grid. Wide bandwidth consults a wider neighborhood radius of grid points to compute each grid point’s density.

  (2) The diversity found in the dimensions’ measurements which depends on the markers, their stains (fluorophores/metals), the instrument and the sample preparations in terms of diversity of cell types.

   

  2) QF match

  a) Performance impacts come from the number of dimensions, cells, cell-overlap and merge candidates.

  i) Dimensions: unlike the Epp step, the QF match step handles additional dimensions in a linear manner and only during the initial sub task of adaptive binning as well as the sub task of calculating distances during QF dissimilarity computation.

  ii) Cells: this impacts performance when it increases the total adaptive bin count for a subset. AutoGate uses slower non vectorized programming modules for subsets with more than 200,000 cells because they risk running out of memory if they use the fast vectorized programming. Vectorized programming uses matrices without for loops for all terms of the QF dissimilarity formula instead of scalars and for loops. In MatLab this has been observed to improve performance over even a C implementation with scalars. MatLab r2017a may use the SIMD operations of the Intel hardware for vectorized operations. When larger subsets exist, the AutoGate user can choose to ignore the slower comparison operation for large subsets effectively removing the particular subset from the analysis. iii) Cell overlap: This can impact quality of performance more than time of performance.

  Why? When QF match compares 2 groups of subsets and subsets in either group have cells that other subsets in the same group also have, then the QF match has been observed to mislead. Thus, AutoGate does a check for overlap and warns the user of this situation.

  iv) Merge candidates: an increase in merge candidates impacts QF match combinatorically. It’s impact on QF match can be costlier than any other impact in the entire pipeline. Specific examples in the following section will indicate. A high number of merge candidates happens when between the 2 groups of subsets being matched there is one subset in one group that matches best with 2 or more subsets in the other group. To resolve the best match the algorithm requires choosing the best QF dissimilarity measure for every combination of merge candidates including single unmerged candidates. This likelihood of long running combinatorics increases as the difference in number of subsets between groups differs. E.g. matching a group of 8 subsets with a group of 12 subsets is less likely to suffer long merge testing than matching a group of 8 subsets with a group of 52 subsets.A subset “best match” is only ineligible for merge testing if one of the parameters is more than 4 standard deviation units different.

  b) The impact of large merging tasks is addressed by the

  i) Software (AutoGate) avoiding uncertain computer-frozen impressions through detailed progress reports that allow the user to cancel (no computer lock up) without data loss consequences.

  ii) User halting the matching and either (1) Cancelling the entire computation (2) De-selecting merge testing for subsets in which they are less interested (and then continuing the computation). If a user selects to ignore merge testing for one or more subsets then this action only alters the compute time and not the correctness of the matching results for the other subsets in which the user remains interested.

  iii) User defining additional known subsets when and if the smaller group of subsets being matched contains non Epp subsets. The additional subsets need only be density clusters for cells that are not already contained by the group’s subsets. Groups of Epp subsets cannot have this remedy since Epp addresses every cell. Thus, no remedy exists when the smaller match group is Epp subsets.

   

  3) MDS

  AutoGate does multi-dimensional scaling (MDS) of subset medians with MatLab’s built-in function cmdscale. MDS is done strictly for the purpose of a quick visualization in conventional MatLab 2D plots. The operation of MDS is always faster than the cost of the visualization so not much needs to be reported on it here. Moreover, since AutoGate’s MDS only considers subset medians, users are encouraged to use the subsequent visualization for a quick report on QF matching rather than a guide to the HiD relatedness of the subsets. For HiD relatedness the primary AutoGate visualization is its Phenogram which considers all of the data. The paper refers to Phenograms as QF tree.

  4) QF tree

  AutoGate produces a visual dendrogram to express HiD relatedness. The non-visual processing defaults to the use of QF dissimilarity metric on the adaptive binning of all of a subset’s data plus Euclidean distances on the subset medians. AutoGate offers the user other distance/dissimilarity measures. The distance-only alternatives, however, consider medians and thus suffer from the same risk of under-informing as does AutoGate’s MDS visualization. The majority of the QF tree’s computation cost is the non-visual processing. The visualization invokes MatLab’s phytree function and then performs customizations on the visual objects output by phytree.

  a) QF tree performance is impacted by number of cells and subsets in a linear manner.

  b) The impacts are not major since there is no quadratic or combinatorial change in workload when input factors scale up or down.

  c) QF trees have the risk of slowness for the same reasons as described previously with QF match: accelerating the QF dissimilarity and Euclidean distance calculations with vectorized programming requires pre-allocating memory in amounts which increase exponentially with larger subsets. Thus slower non vectorized programming must occur.

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• How do I get AutoGate?

  Easy. And it's free. Just click Download. 

• Will AutoGate work on both MAC and Windows?

  Yes, it works with Mac OS/X and Windows 8, 7, and Vista.

• After I upgraded my MAC OS to Yosemite, I got this alert To open AutoGate.app you need to install the legacy Java SE 6 runtime What should I do?

  Click More info. This will take you to the relevant Java SE6 page. Click Download. When it completes, double-click JavaForOSX.pkg. The installation window will display. Follow the directions and you will have what you need to run AutoGate.

  

• On trying to open AutoGate.app, I get this message Unable to load Java Runtime Environment. How do I proceed?

  

  This means the Java version on your computer is out of date. Please install the latest version from https://www.java.com/en/download/mac_download.jsp , which is an easy process.

  Once Java is installed, re-open AutoGate.

• On trying to launch AutoGate on Mac, I get a message that its from an Unidentified developer. What should I do?

  

  This is a common message, if Mac can't check whether AutoGate has been modifieid since it was released. This doesn't mean that's something wrong with AutoGate.Click on Open and you will be fine.

  If the above message does not have an 'Open' option, then follow the below steps.

  i). In the Finder window, locate AutoGate.app

  ii). Press the Control key, then click the app icon, then choose Open from the shortcut menu.

  iii). Click Open.

• On trying to launch AutoGate on Mac, I get a message that AutoGate is an application downloaded from the Internet and Should I open?. ?

  Again, this is a common message from Mac. Click Open to continue

  

• On trying to launch AutoGate on Mac, I get a message that its damaged and can't be opened

  

  1. Open Terminal

  2. Type sudo bash. Enter your password

  3. Type xattr -cr /Applications/AutoGate.app

  

  4. Close Terminal

  5. Open AutoGate now

  Note: If you are on older version of Mac OS, then instead of the above steps, Open Security & Privacy under System Preferences and choose Anywhere option, as below.

   

• Even after fixing the AutoGate.app is damaged error, AutoGate fails to launch on the new MAC OS Sierra. Error being AutoGate quit unexpectedly, as below. Why?

  

  This is due to incompativle Java 1.6 version

  i) Using Finder, go to /Library/Java/JavaVirtualMachines

  ii) Select the java 1.6 and delete

  

  iii) Launch AutoGate. It will alert to install the legacy Java SE6 runtime. Click on More info link and follow the instructions. Once installed, launch AutoGate again

• AutoGate crashes on trying to launch on Mac. Help?

  This is due to Java 1.6 version on your Mac. Install the Patch from Apple site here

• On trying to launch AutoGate on Mac, I get a message that the installed Matlab Runtime is not compatible with the application. What should I do?

  

  In almost 99% of the scenarios, AutoGate installer will install the pre-requisites (Java and Matlab Runtime) on users computer without any problem. But in few scenarios, it may prove difficult to install these 3rd party soffwares, due to number of factors( eg, users' computer configuration, computer capacity, conflicting softwares, antivirus, network policies,..). In those scenarios, it is recommedned that users install Matlab Runtime directly from Mathworks site - https://www.mathworks.com/products/compiler/mcr.html

  Once Matlab Runtime is installed, proceed with AutoGate launch.

• On trying to launch AutoGate on Mac, I get a unclear message Cannot locate a valid install area..What do I do?

  

  This is related to Matlab version not available or installed on the user computer.

  In almost 99% of the scenarios, AutoGate installer will install the pre-requisites (Java and Matlab Runtime) on users computer without any problem. But in few scenarios, it may prove difficult to install these 3rd party soffwares, due to number of factors( eg, users' computer configuration, computer capacity, conflicting softwares, antivirus, network policies,..). In those scenarios, it is recommedned that users install Matlab Runtime directly from Mathworks site - https://www.mathworks.com/products/compiler/mcr.html

  Once Matlab Runtime is installed, proceed with AutoGate launch.

• On trying to launch AutoGate on Mac, I get a message Test Matlab2016b or AutoGate2016b cannot be opened..What do I do?

  

  

  This is related to Matlab Runtime not properly installed or available in the expected location.

  In almost 99% of the scenarios, AutoGate installer will install the pre-requisites (Java and Matlab Runtime) on users computer without any problem. But in few scenarios, it may prove difficult to install these 3rd party soffwares, due to number of factors( eg, users' computer configuration, computer capacity, conflicting softwares, antivirus, network policies,..). In those scenarios, it is recommedned that users install Matlab Runtime directly from Mathworks site - https://www.mathworks.com/products/compiler/mcr.html

  Once Matlab Runtime is installed, proceed with AutoGate launch.

• Will AutoGate work on tablets and smaller mobile devices?

  We've designed AutoGate to work on desktop computers and laptops, preferable with larger screens. Some of our interactive windows will fill even an extra-wide desktop monitor. If you have a large tablet with a Windows operating system and sufficient memory, you might be able to run AutoGate, but we don't recommend it.

• How much memory does AutoGate require?

  6 Gigabytes of Random Access Memory, though 8 GB will work much faster, particularly if you are experimenting with large samples.

• How long would it take to download and launch AutoGate?

  On a computer with 8GB RAM,  i7 processor and with an internet speed of around 50 MBPS, it would usually take around 5-10 minutes for the first launch of AutoGate (as couple of AutoGate dependancies would have to be installed, if not available). Subsequent launches would take less than couple of minutes. 

• When I try to launch AutoGate, I get a prompt to about Incoming connections. Should I allow?

  Yes, it is safe to allow incoming connections from Cytogenie.org

• Will I get support?

  Yes, CytoGenie.org will provide Beta users with an easy-to-use online help guide, instructional videos, a community forum, and personal support if needed.

• To get updates, will I have to reinstall AutoGate?

  No. Every time you launch the program, it automatically checks for any updates and downloads them to your computer, tablet, or smart phone.

• Must I use FlowJo?

  No. AutoGate works with files of previous experiments from fcs files, DiVa, CellQuestPro, and others.