Schoolboy injured on Aer Lingus plane awarded €15,000
Child left with scar on face after part of bulkhead above seats fell and struck him
A schoolboy injured on an Aer Lingus plane while travelling to New York has been awarded €15,000 damages in the Circuit Civil Court.
Owen Donnelly, counsel for 11-year-old Dylan Lynch, told Judge John O’Connor on Tuesday that the boy was sitting beside his mother, Ann Lynch, awaiting take-off from Dublin Airport in March 2018. During this time part of the bulkhead above their seats fell and struck Dylan, of Cahernorry, Ballysimon, Co Limerick, in the face. The incident had not interfered with the flight schedule.
Mr Donnelly, who appeared with Thomas Dowling, said Dylan had been left with a small red scar on his left cheek. He was treated by his mother immediately after the incident.
Mr Donnelly told the judge that the injury had recovered to something more like a blemish on Dylan’s cheek and the airline had made a settlement offer of €12,000 to the boy.
Damages
The judge, having looked at the scar, said he felt the damages should be more in the line of €15,000 and adjourned proceedings briefly to allow the boy’s solicitors contact the airline with regard to his remarks.
Shortly afterwards Mr Donnelly told the court that the airline had revised its offer to €15,000 after. He said he was recommending acceptance for it by the court. The judge approved the settlement with costs.


Breadcrumb provides contextual information about page hierarchy.
Breadcrumb uses the common menumodel api to define its items, visit MenuModel API for details.
Breadcrumb requires a collection of menuitems connected to its model property.
| Name | Type | Default | Description |
|---|---|---|---|
| model | array | null | An array of menuitems. |
| home | MenuItem | null | MenuItem configuration for the home icon. |
| style | string | null | Inline style of the component. |
| styleClass | string | null | Style class of the component. |
| homeAriaLabel | string | null | Defines a string that labels the home icon for accessibility. |
| Name | Parameters | Description |
|---|---|---|
| onItemClick | originalEvent: Browser event item: Selected menu item |
Fired when an item is selected. |
Following is the list of structural style classes, for theming classes visit theming page.
| Name | Element |
|---|---|
| p-breadcrumb | Container element. |
| p-menuitem | Menuitem element. |
| p-menuitem-text | Label of a menuitem. |
| p-breadcrumb-chevron | Chevron element. |
None.


| DOSE: T+ 0:00 |
9.0 mg |
oral |
2C-P |
(liquid) |
| T+ 2:25 |
1 bowl |
smoked |
Cannabis |
(plant material) |
Drugs used:
2c-p- amount- 7 mgs. But I handled .5 grams of it earlier, and I probably ate 2 mgs. Off of the card which I was using to split up the pile with.
Cannabis- amount- hard to tell... At least 5 bowls throughout the nite (probably more)
Experience:
I have done 2C-I numerous times.
I have done 2C-B once, but I only have two more hits, so I'm waiting to do those.
I have done 5-MeO-AMT about 8 times.
I tried smoking small amounts of 2C-P, to get the feel of it.
Set:
In the weeks prior to this trip I was mostly in a good mood. I have been a little depressed here and there. But generally, I am in a good mindset.
Setting:
For the comeup, friend's house
Then we went to the forest for a bit, then to a small gathering of people for a graduation
Then back to the forest, untill I went home
Brief overview of 2C-P:
This is one strong drug. Everyone should proceed with caution.
It lasted extremely long, and is very euphoric.
Very potent.
No nausea noted from anyone.
Slight sweating/tremors on the comeup.
Effects:
Somewhat sedating.
Rather visual (moreso than 2C-B, in my opinion). Very deep mentally (a lot stronger than 2C-I, I cannot compare it to 2C-E yet). Long lasting. I was going for a good 12 hours, including comeup. It takes 3 hours to come on.
Report:
At t- 2: hours I dissolved 125 mgs. (split into a quarter from a half gram) into 7 shots of boiled water (to remove chlorine and sterilize)
At 5:00 pm: I took half a shot (~9 mgs.)
T+1.5 hours: I am starting to feel sedated and hav minor trembles (a friend also noticed tremors)
T+2.25 hours: smoked first bowl of cannabis. I start to see the ground swirl.
T+3.5 hours: the drug is really starting to kick in now, im seeing colors out in the woods, and all the lightening bugs looked like little fairies. I am starting to see red fireflies that arent there.
T+4 hours: we rolled up to some rich people's graduation party to get our friend andy. We offer him some of 'this bomb magic water' (which is what we were all calling the water bottle by this point...lol)
T+4.5 hours: we leave the party and go back into the woods. We proceed to smoke an unknown amount of cannabis. At least 4 bowls of some bomb nuggetz. While back there, I start seeing all kinds of colored dots and neon halos around everything.
T+5.75 hours: I believe it was around this point that we went to play hackie-sack in front of a small building by the woods. Visuals are insane. Someone will kick the sack, and it will be on the ground and I will see a trail from the entire trip of the last kick on the sack, from the llst persons foot touching it. The shadows or spots on the ground or whatever they were begin sliding towards me, while the sidewalk they are on slides away from me. Both seem to just keep moving, but never getting and further/closer to me. I see swirling vortexes in the ground, and the blades of gras wave at me.
 While back in the woods, a few people (mainly myself) start having uncontroblable laughing episodes. We also hear what seem to be animals dieing. I am talking about crazy loud horrible screams from somewhaere in the woods, that seems to not be coming from the mouth of a person. A few times we hear it, and get frightened and start rushing away from the tree we were sitting on, only to realize that we couldnt see anything and get lost real quick.
T+7 hours: we leave the forest. I go home.
T+7.5 hours: I finally arrive home after walking, I am startled by my parents greeting me, and telling me to eat dinner. As I am going up to the third floor, to my room, I see all of the hardwood floor bending and twisting. The patterns in the wood are all doing they're own little thing.
Until I go to sleep: after I am in my room for a bit, I watch tv. I still have visuals and deep thoughts.  I end up laying and thinking for about 5 hours before I go to sleep.
The next day: no real hangover, expect that I am very tired, because I did not get any sleep. I slept in until about 3, probably got around 4 hours of sleep in total. I take a nap in the afternoon, and later on, I meet up with my friends.
Overall: the best chemical I have worked with
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


2 Corinthians 3:1
Do we begin again to commend ourselves? or need we, as some others, epistles of commendation to you, or letters of commendation from you?


Contains classes for generating the help text for each page, following the help link in the upper right tool section of Web.


Rapunzel is stuck at home. And she’s bored as hell.
She’s baking. She’s doing jigsaw puzzles. She’s sweeping the floor and doing laundry and knitting an endless scarf she’ll probably never wear. (She’s also combing through 70 feet of hair, and playing hide and seek with a spunky animated chameleon. Otherwise, it’s all fairly banal.)
The whole scene, from the opening number of Disney’s "Tangled," may feel a tad too familiar right now to qualify as escapism.
But despite the parallels, I find myself returning to this film again and again, even during — especially during — a global crisis. It’s part classic, heartwarming princess tale, part princess fighting her way through the kingdom with nothing but a frying pan, some magic hair and a partner in crime who stumbled on her abode by mistake.
“Tangled,” one of Disney’s early forays into computer animation, came at a pivotal time for the studio. Its early 2000s lineup (remember “Chicken Little”?) was a far cry from the princess blockbusters of the ’90s (like “The Little Mermaid” or “Beauty and the Beast”). Disney needed another hit, and it was this 2010 action-filled take on Rapunzel’s story that finally delivered.
Nearly a decade later, “Tangled” still holds up. There’s witty dialogue, Zachary Levi, characters who blur the line between heroes and villains, Zachary Levi, delightfully emotive animal sidekicks and a swashbuckling rogue — Zachary Levi — with a smorgasbord of Hot Guy Features handpicked by the women of Walt Disney Animation Studios.(Ladies, we are forever in your debt.)
But at its core, “Tangled” (directed by Nathan Greno and Byron Howard) is a love story, a charming adventure — and, yes, a happily ever after. What else could we ask for right now? Here are three reasons it’s still a reliable comfort:
Flynn Rider — the aforementioned swashbuckler-turned-love-interest, voiced by Levi — makes for a great co-pilot, and it doesn’t hurt that our princely figure gets a back story for once: He was a normal guy with humble origins and a dream.
But he isn’t the hero of this story. It’s Rapunzel (voiced by Mandy Moore) who squashes one obstacle after another.
She takes out an intruder in her home with one well-aimed thwack of that frying pan.  She outruns palace cops and calculates an escape from a flooding cavern, both in the span of three minutes.
We finally get a Rapunzel who takes matters into her own hands instead of waiting around in her tower — a princess we can root for.
Alan Menken, once again, is doing the Lord’s work.
The composer is responsible for many of the classics in the Disney songbook: Aladdin and Jasmine’s magic carpet ride in “A Whole New World,” the maritime orchestrations of “Under the Sea,” the silverware showstopper “Be Our Guest.”
“Tangled” has a similarly memorable soundtrack (with lyrics by Glenn Slater), and one that, perhaps most important, you can listen to afterward with no skips. We’ve got a wonderfully ominous ballad from the villain, a romantic lantern-lit duet, more than one clever reprise. (I’ll say it: Menkenian reprises are the best kind.)

There’s something ethereal about the wide shots of Rapunzel’s tower tucked away in the forest, her hair dangling out the window and floating in the breeze. And when the kingdom sends hundreds of lanterns into the sky — a scene Rapunzel has dreamed of watching her whole life — each one flickers with a warm glow, casting an enchanting light over the whole screen.
Not to mention: Someone had to animate all that hair. Kudos.


aws backup
Unified backup service designed to protect Amazon Web Services services and their associated data.
More information: home page
- Return BackupPlan details for a specific BackupPlanId:
aws backup get-backup-plan --backup-plan-id id
- Create a backup plan using a specific backup plan name and backup rules:
aws backup create-backup-plan --backup-plan plan
- Delete a specific backup plan:
aws backup delete-backup-plan --backup-plan-id id
- Return a list of all active backup plans for the current account:
aws backup list-backup-plans
- Display details about your report jobs:
aws backup list-report-jobs


AWS::ApiGatewayV2::Api
The
AWS::ApiGatewayV2::Api
resource contains an Amazon API Gateway API. For more information, see
CreateApi in the
Amazon API Gateway V2 API Reference.
Syntax
To declare this entity in your AWS CloudFormation template, use the following syntax:
JSON
{ "Type" : "AWS::ApiGatewayV2::Api", "Properties" : { "ApiKeySelectionExpression" :
String
, "Description" :String
, "DisableSchemaValidation" :Boolean
, "Name" :String
, "ProtocolType" :String
, "RouteSelectionExpression" :String
, "Version" :String
} }
YAML
Type: "AWS::ApiGatewayV2::Api" Properties: ApiKeySelectionExpression:
String
Description:String
DisableSchemaValidation:Boolean
Name:String
ProtocolType:String
RouteSelectionExpression:String
Version:String
Properties
ApiKeySelectionExpression
-
An API key selection expression.
Required: No
Type: String
Update requires: No interruption
Description
-
The description of the API.
Required: No
Type: String
Update requires: No interruption
DisableSchemaValidation
-
Avoid validating models when creating a deployment.
Required: No
Type: Boolean
Update requires: No interruption
Name
-
The name of the API.
Required: Yes
Type: String
Update requires: No interruption
ProtocolType
-
The API protocol.
Required: Yes
Type: String
Update requires: Replacement
RouteSelectionExpression
-
The route selection expression for the API.
Required: Yes
Type: String
Update requires: No interruption
Version
-
A version identifier for the API.
Required: No
Type: String
Update requires: No interruption
Return Values
Ref
When you pass the logical ID of an
AWS::ApiGatewayV2::Api
resource to the intrinsic
Ref
function, the function returns the
API ID, such as
a1bcdef2gh
.
For more information about using the
Ref
function, see
Ref.
Examples
API creation example
The following example creates an Api
resource called MyApi
.
JSON
{ "MyApi": { "Type": "AWS::ApiGatewayV2::Api", "Properties": { "Name": "MyApi", "ProtocolType": "WEBSOCKET", "RouteSelectionExpression": "$request.body.action", "ApiKeySelectionExpression": "$request.header.x-api-key" } } }
YAML
MyApi: Type: 'AWS::ApiGatewayV2::Api' Properties: Name: MyApi ProtocolType: WEBSOCKET RouteSelectionExpression: $request.body.action ApiKeySelectionExpression: $request.header.x-api-key
See Also
-
CreateApi in the Amazon API Gateway V2 API Reference


--- layout: page title: "all" nav: false description: "Time flies like an arrow, fruit flies like a banana" header-img: "img/post-bg-coffee.jpeg" head_padding: "55px" --- {% for post in site.posts %} {% capture ym %}{{ post.date | date:"%Y%m" }}{% endcapture %} {% if yearmonth != ym %} {% assign yearmonth = ym %}


Haffenden House / PARA
- Curated by Igor Fracalossi
- about 3 hours
Houses · Syracuse, United States
- version="1.0" encoding="UTF-8"? Architects: PARA Project
- AreaArea of this architecture projectArea: 104.5 ft2
- version="1.0" encoding="UTF-8"?YearCompletion year of this architecture project Year: 2014
- version="1.0" encoding="UTF-8"?PhotographsPhotographs: Nathan Rader
- version="1.0" encoding="UTF-8"?ManufacturersBrands with products used in this architecture project Manufacturers: Buddy Rhodes, Buffalo Felt, Rochester Insulated Glass, Sepp Leaf
- Architect in Charge Jon Lott
- Project Team Jon Lott, Hilary Pinnington, Paul Kneeply
More Specs Less Specs
Text description provided by the architects. The Haffenden House is a writing studio with a garage/breezeway at ground-level, library and writing space on the second level, and a curved/soft reading room on the third level. The project finds itself within the suburban realm, referencing Gianni Pettena’s Ice House from 1972, like a blank spot within the repetitive image of “house.”
In the rear yard, the fabric solution is used on the interior as a curtain, offering the owners the flexibility to control the relationship of privacy within their context.
The poet’s studio uses a translucent silicon-impregnated fabric skin for a light-filled writing room without the visual distraction of the suburbs. Inside the blank box, between the second and third levels, the section uses a bowl-shaped division, maximizing indirect light for the second level and avoiding any association with the landscape on the third.
Originally published February 12, 2014.


Pathway enrichment analysis and visualization of omics data using g:Profiler, GSEA and Enrichment Map in Cytoscape
This step-by-step protocol explains how to complete pathway enrichment analysis using g:Profiler (filtered gene list) and GSEA (unfiltered, whole genome, ranked gene list), followed by visualization and interpretation using EnrichmentMap.
The above can be conveniently downloaded and installed together by installing the “EnrichmentMap Pipeline Collection” from the Cytoscape App Store.
Data
We provide downloadable example files referred throughout the protocol (You can also download all the data file at once here. We recommend saving all these files in a personal project data folder before starting. We also recommend creating an additional result data folder to save the files generated while performing the protocol.
A list of genes with frequent somatic single nucleotide variants (SNVs) identified in The Cancer Genome Atlas (TCGA) exome sequencing data of 3,200 tumors of 12 types.The MuSiC cancer driver mutation detection software was used to find 127 cancer driver genes that displayed higher than expected mutation frequencies in cancer samples. Genes are ranked in decreasing order of significance (FDR q-value) and mutation frequency
A list of differentially expressed genes in two subtypes of ovarian cancer defined by TCGA.
An RNK file is a two-column text file with gene IDs in the first column and gene scores in the second column. All (or most) genes in the genome need to have a score and the gene IDs need to match those used in the GMT file.
Contains a database of pathway gene sets used for pathway enrichment analysis in the standard GMT format, downloaded from http://baderlab.org/GeneSets. This file contains pathways downloaded on July 1, 2017 from eight data sources: Gene Ontology, Reactome, Panther, NetPath, NCI, MSigDB curated gene sets (C2 collection, excluding Reactome and KEGG), MSigDB Hallmark (H collection) and HumanCyc.
A GMT file is a text file where every line represents a gene set for a single pathway. Each line includes a pathway ID, name and the list of associated genes in a tab-separated format.
Check the box next to No electronic GO annotations.
Click on Show Advanced Options
6A. g:Profiler - Input data and set parameters - cont'd
Set filters on gene annotation data using the legend on the right. We recommend that initial pathway enrichment analyses only includes biological processes (BP) of GO and molecular pathways of Reactome. Keep the two checkboxes checked and uncheck all other boxes in the legend.
6A. g:Profiler - Input data and set parameters - cont'd
Set the dropdown values of Size of functional category to 5 (‘min’) and 350 (‘max’). Large pathways are of limited interpretative value, while numerous small pathways decrease the statistical power because of excessive multiple testing.
Set the dropdown Size of query/term intersection to 3. The analysis will only consider more reliable pathways that have three or more genes in the input gene list.
Click g:Profile! to run the analysis.
A graphical heat map image will be shown with detected pathways shown along the y axis (left) and associated genes of the input list shown along the x axis (top). Resulting pathways are organized hierarchically into related groups.
g:Profiler returns only statistically significant pathways with p-values adjusted for multiple testing correction (called q-values).
By default, results with q-values below 0.05 are reported.
6A. g:Profiler - Input data and set parameters - cont'd
Use the dropdown menu Output type and select the option Generic Enrichment Map (TAB). This file is required for visualizing pathway results with Cytoscape and EnrichmentMap.
Click g:Profile! again to run the analysis with the updated parameters.
The required link Download data in Generic Enrichment Map (GEM) format will appear under the g:Profiler interface.
6A. g:Profiler - Input data and set parameters - cont'd
cont'd - Download enrichment file.
Download the file from the link and save it on your computer in your result data folder created in Step 1i. Example results are provided in Supplementary_Table4_gprofiler_results.txt.
Download the required GMT file by clicking on the link name at the bottom of the Advanced Options form. The GMT file is a compressed ZIP archive that contains all gene sets used by g:Profiler (e.g., gprofiler_hsapiens.NAME.gmt.zip).
The gene set files are divided by data source.
Download and uncompress the ZIP archive to your project folder.
All required gene sets for this analysis will be in the file hsapiens.pathways.Name.gmt (Supplementary_Table5_hsapiens.pathways.NAME.gmt).
Place the saved file in your result data folder created in Step 1i.
6B. Pathway enrichment analysis of a ranked gene list using GSEA
Launch GSEA by opening the downloaded GSEA file (gsea.jnlp)
Troubleshooting - GSEA launch
TROUBLESHOOTING: Unable to launch GSEA
macOS for the first time,
error “gsea.jnlp cannot be opened because it is from an unidentified developer”.
Click on “Ok”.
Instead of double clicking on the gsea.jnlp icon/file, right click and select “open”.
The same error will appear but this time it will give you the option to “Open” or “Cancel”. Click on “Open”.
After this initial opening, subsequent double clicks on gsea.jnlp will launch GSEA without errors or warnings.
If GSEA still fails to launch through the Java Web Start downloaded from the GSEA website,
GSEA can alternatively be launched from the command line.
Go to the GSEA download site and download javaGSEA JAR file.
Open a command line terminal.
navigate to the directory where the file javaGSEA.jar was downloaded.
Run the command "java –Xmx4G –jar gsea-3.0.jar".
6B. Pathway enrichment analysis of a ranked gene list using GSEA - cont'd
Load the required data files into GSEA:
Click on Load Data in the top left corner in the Steps in GSEA Analysis section.
Also select the pathway gene set definition (GMT) file using a multiple select method such as shift-click (genesets gmt file).
Click the Choose button to continue.
A message box indicates that the files were loaded successfully. Click the OK button to continue.
Click on Run GSEAPreranked in the side bar under Tools.
Troubleshooting - GMT files
TROUBLESHOOTING: GSEA may seem non-responsive but it is actually loading in large files.
Possible reason:Large GMT file
It may take 5-10 seconds for GSEA to load input files. The files are loaded successfully once a message appears on the screen “Files loaded successfully: 2/2. There were no errors”.
TROUBLESHOOTING: How to use MSigdb or alternate GMT files.
GSEA also supplies its own gene set files that are accessible directly through the GSEA interface from the MSigDB resource.
These files do not need to be imported into GSEA. When you define the GMT file, the MSigDB gene set files can be found in the first tab “Gene Matrix (from website)” of the “Select one or more genesets” dialog. The latest versions of the MSigDB gene set files are in bold but previous versions can also be accessed. To select multiple gene set files, use multi-file select by simultaneously clicking on the desired files and holding the control key on Windows or command on macOS.
6B. Pathway enrichment analysis of a ranked gene list using GSEA - cont'd
Specify the following parameters::
Gene sets database –
click on the button (…) located to the right and wait a few seconds for the gene set selection window to appear.
Go to the Gene matrix (local gmx/gmt) tab using the top right arrow.
Number of permutations – number of times that the gene sets will be randomized to create the null distribution to calculate the p-value and FDR q-value. Use the default value of 1000 permutations.
Click the Show button next to Basic fields to display extra options.
Troubleshooting - Permutations
TROUBLESHOOTING: Increasing the number of permutations will increase computation time.
The higher the number of permutations, the longer the analysis will take.
To calculate the FDR q-value for each gene set, the data is randomized by permuting the genes in each gene set and recalculating the p-values for the randomized set. This parameter specifies how many times this randomization is done.
The more randomizations are performed, the more precise the FDR q-value estimation will be (to a limit, as eventually the FDR q-value will stabilize at the actual value). On a Windows machine with 16G of RAM and i7 3.4 GHz processor, an analysis with 10, 100, 500, or 1000 randomizations on our example set with above defined parameters takes 155, 224, 544, and 1012 seconds, respectively.
6B. Pathway enrichment analysis of a ranked gene list using GSEA - cont'd
Specify the following parameters: cont'd
Analysis name – change default “my_analysis” to a specific name, for example “Mesen_vs_Immuno”.
Max size: exclude larger sets – By default GSEA sets the upper limit to 500. Set this to 200 to remove the larger sets from the analysis.
Save results in this folder – navigate to the folder where GSEA should save the results. We recommend you choose the result data folder created in Step 1i. Otherwise, GSEA will use the default location gsea_home/output/[date] in your home directory.
6B. Pathway enrichment analysis of a ranked gene list using GSEA - cont'd
Run GSEA:
Run GSEA – click on the Run button located at the bottom of the window.
The GSEA reports pane at the bottom left of the window will show the status “Running”.
It will be updated to “Success” upon completion.
This is expected to be a long running process, depending on the speed of your computer.
Troubleshooting - Running GSEA
TROUBLESHOOTING: GSEA looks non-responsive but it is actually computing enrichments.
Possible reason: No progress bar.
GSEA has no progress bar to indicate estimated time to completion.
A run can take a few minutes or hours depending on your data size and computer speed.
Click on the “+” in the bottom left corner of the screen to see messages such as “shuffleGeneSet for GeneSet 4661/4715 nperm: 1000”
This message indicates that GSEA is shuffling 4,715 gene sets 1,000 times each, 4,661 of which are complete. Once the permutations are complete, GSEA generates the report.
Troubleshooting - Running GSEA
TROUBLESHOOTING: Error message "Java Heap space".
Possible reason: GSEA was launched with insufficient memory.
The error message “Java Heap space” indicates that the software has run out of memory.
Another version of GSEA is needed if you are running the Web Start application. There are multiple options available for download from the GSEA website. You can download a webstart application that launches GSEA with 1, 2, 4, or 8GB. Upgrade to a webstart that launches with more memory.
If you are already using the webstart that launches with 8GB then you require GSEA JAVA jar file which can be executed from the command line with increased memory .
6B. Pathway enrichment analysis of a ranked gene list using GSEA - cont'd
Examine GSEA Results:
Once the GSEA analysis is complete, a green notification “Success” will appear in the bottom left section of the screen.
All GSEA output files will be automatically saved and available in the folder you specified in the GseaPreranked interface (Specified in Step 6Bxii).
Click on “Success” to open the results in your web browser.
Pathways enriched in top-ranking genes (i.e. up-regulated) are shown in the first set (‘na_pos’; ‘mesenchymal’ in this protocol)
Pathways enriched in bottom-ranked genes (i.e. down-regulated) in the second set (‘na_neg’; ‘immunoreactive’)
6B. Pathway enrichment analysis of a ranked gene list using GSEA - cont'd
Examine GSEA Results- cont'd:
In the web browser results summary, click on the “Snapshot” link under the results to get an overview of the top 20 findings.
The most significant pathways for the first phenotype (‘na_pos’) should clearly display enrichment in top-ranking (i.e. up-regulated) genes (left side of the plot.
the most significant pathways for the second phenotype (‘na_neg’) should clearly display enrichment in bottom-ranked (i.e. down-regulated) genes (right side of the plot)
Troubleshooting - Examining GSEA Results
TROUBLESHOOTING: How to access previous results.
Possible reason: GSEA application was closed since running the analysis but you would like to see previous results.
If the GSEA software is closed, you can access previous results by opening the working folder and opening the ‘index.html’ file.
Alternatively, you can re-launch GSEA and click on “Analysis history”, then “History” and then navigate to date of your analysis.
Although all analyses, regardless of where the results files were saved, are listed under history, it is organized by date the analysis was run. If you cannot remember when you ran a specific analysis, then you may have to manually search through a few directories to find the desired analysis.
Troubleshooting - Examining GSEA Results
TROUBLESHOOTING: What will GSEA use for phenotype labels in a CLS file that specifies more than two phenotypes?
When running GSEA with expression data as input (instead of a pre-calculated rank file), a phenotype label (i.e. biological condition or sample class) is provided as input for each sample and specified in the GSEA ‘cls’ file.
When running GSEA, the two phenotypes to compare for differential gene expression analysis are specified and these phenotypes are used in the pathway enrichment result files.
In contrast, in a GSEA preranked analysis (i.e. when a ranked gene list is provided by the user), GSEA automatically labels one phenotype “na_pos” (corresponding to enrichment in the genes at the top of the ranked list, where ‘na’ means the phenotype label is "not available") and the other “na_neg” (corresponding to enrichment in the genes at the bottom of the ranked list). This convention is also used by the EnrichmentMap software, designating the first phenotype as “positive” and the second phenotype as “negative”.
6B. Pathway enrichment analysis of a ranked gene list using GSEA - cont'd
Examine GSEA Results- cont'd:
In the web browser results summary, click on ‘Detailed enrichment results in HTML format’.
Use the row numbering to check the number of pathways that have FDR q-values below 0.05 to determine appropriate thresholds for EnrichmentMap in the next step of the protocol.
If no pathways are reported at q< 0.05, more lenient thresholds such as q<0.1 or q<0.25 could be used
The threshold q<0.25 provides very lenient filtering and it is not uncommon to find thousands of enriched pathways at this level. Robust analyses should use a cutoff of q< 0.05 or lower.
Filtering only by uncorrected p-values is not recommended.
Troubleshooting - Examining GSEA Results
TROUBLESHOOTING: Few or no results returned by GSEA
Possible reason: Possible identifier mapping issue.
Check the number of gene sets that were analyzed. If the number is low (e.g. low hundreds), it could indicate gene ID mapping problems.
Create Enrichment Map panel will appear. Creating enrichment maps with g:Profiler (follow steps in 9A) and GSEA (follow steps in 9B) requires different input files.
9A. Visualize enrichment results with EnrichmentMap use g:Profiler results
In the Create Enrichment Map panel, click on folder icon.
Locate and select your result data folder containing the g:Profiler results and click on Open.
In the right-hand pane g:Profiler output files will be automatically populated into their specified fields. Alternatively, users can click on the + symbol to specify each of the required files manually.
Troubleshooting - Visualizing g:Profiler Results
TROUBLESHOOTING: Autoload of g:Profiler results creates many datasets with incorrect file specification.
Possible reason: There are too many text files within the directory specified.
9A. Visualize enrichment results with EnrichmentMap use g:Profiler results - cont'd
In the right-hand pane, modify Name of the created data set if desired. By default, EnrichmentMap will use the name of the g:Profiler enrichment results file (e.g. Supplementary_Table4_gprofiler_results.txt).
Verify the Analysis Type is set to “Generic/gProfiler”.
Verify the GMT specified is the file retrieved from the g:Profiler website in Step 6Axii . Use the file hsapiens.pathways.NAME.gmt (or alternately manually specify Supplementary_Table5_hsapiens.pathways.NAME.gmt) that contains gene sets corresponding to GO biological processes and Reactome pathways.
9A. Visualize enrichment results with EnrichmentMap use g:Profiler results - cont'd
Expressions - (Optional) Upload an expression matrix for the genes analyzed in g:Profiler or alternatively an expression data set of all genes. If the expression data set contains additional genes not used for the g:Profiler search, their expression values will still appear in the heat map of the enrichment map (for example file see Supplementary_Table6_TCGA_OV_RNAseq_expression.txt).
Classes – (Optional) GSEA CLS file defining the phenotype (i.e. biological conditions) of each sample in the expression file, for example, see Supplementary_Table7_TCGA_OV_RNAseq_classes.cls. ).
Phenotypes – (Optional) If there are two different phenotypes in the expression data, update the phenotype labels so that ‘positive’ represents the phenotype associated with positive values (Mesenchymal in this example) and ‘negative’ with negative values (Immunoreactive in this example).
Troubleshooting - Visualizing g:Profiler Results
TROUBLESHOOTING: How to create g:Profiler map with more than one phenotype.
Although an individual g:Profiler analysis only has one phenotype, it is possible to modify a single results file to contain two analyses.
This is relevant when the phenotypes are mutually exclusive.
For the analysis you want to associate with the additional phenotype (which would correspond to down-regulated genes in GSEA PreRanked, thus called “negative”) open the g:Profiler results file (preferably in a spreadsheet so you can easily modify a single column).
The fifth column specifies the phenotype. Update the column to have the value of “-1” for each result in the file.
Open the second analysis file. Copy all the results from the second file and paste them into the updated negative g:Profiler file.
Save the file and use it as the g:Profiler enrichment results file in the EnrichmentMap interface instead of the original results files.
Pathways corresponding to two phenotypes will be colored red and blue in the resulting enrichment map. One limitation with this approach is that a pathway cannot be included in both positive and negative sets.
9A. Visualize enrichment results with EnrichmentMap use g:Profiler results - cont'd
Tune Parameters:
Number of Nodes: -
g:Profiler returns only statistically significant results (q < 0.05) by default, so the FDR q-value cutoff parameter can be set to 1 in the EnrichmentMap Input panel unless a more stringent filtering is desired. For this protocol, set FDR q-value to 0.01.
Select Filter genes by expressions to exclude any genes in the gene set definition file (i.e. the gmt file) that are not found in the supplied expression file.
If Filter genes by expressions is not selected, any gene that is not found in the expression file will be retained and will be presented in the expression heat map viewer with all of its associated expression values grayed out.
9A. Visualize enrichment results with EnrichmentMap use g:Profiler results - cont'd
Tune Parameters cont'd:
Number of Edges: -
Keep the connectivity slider in the centre.
If the network is too cluttered because of too many connections (edges), move the slider to the left to make the network sparser.
Alternatively, if the network is too sparse (i.e. there are too many disconnected pathways), move the slider to the right to obtain a more densely connected network.
Troubleshooting - Visualizing g:Profiler Results
TROUBLESHOOTING: What happens when you move the similarity slider bar?
Moving the slider to the left (or right) will adjust the underlying similarity statistic threshold to make the resulting network sparser (or denser).
The slider is set with predefined defaults but users can fine-tune the similarity metric by selecting "Show advanced options" at the bottom of the "Create Enrichment Map" panel.
Predefined values appear as tick marks on the slider and include Jaccard > 0.35, Jaccard > 0.25, combined > 0.375, overlap > 0.5, overlap > 0.25.
9A. Visualize enrichment results with EnrichmentMap use g:Profiler results - cont'd
Click the Build button at the bottom of the EnrichmentMap Input panel. A Building EnrichmentMap box appears and indicates the progress status. This box will disappear once the map has been created successfully.
9B. Visualize enrichment results with EnrichmentMap use GSEA results
In the Create Enrichment Map panel, click on folder icon.
Locate and select your result data folder containing the GSEA results and click on Open.
In the right-hand pane, GSEA output files will be auto populated into their specified fields. Alternately the + symbol can be clicked to specify each of the required files manually.
Troubleshooting - Visualizing GSEA Results
TROUBLESHOOTING: How to easily create GSEA map with more than one dataset.
If you specify a directory that contains multiple GSEA results rather than an individual GSEA results folder, EnrichmentMap will treat every GSEA results folder as its own data set. This enables easy multi-data set analyses. If you only want one data set but inadvertently selected the directory containing multiple GSEA results instead of selecting an individual folder, simply select the data sets you do not want to use and click on the trash can at the top of the EnrichmentMap input panel to remove them.
9B. Visualize enrichment results with EnrichmentMap use GSEA results - cont'd
In the right-hand pane, modify Name of the created data set if desired. By default, EM will use the first part of the GSEA folder name prior to the last dot ‘.’ to create the data set name. For example, if the directory is called Mesen_vs_Immuno.GseaPreranked.12345, the name will be populated as Mesen_vs_Immuno.GseaPreranked.
Verify the Analysis Type is set to “GSEA”.
Enrichment Pos - Verify that file is set to [your_path_to_gsea_dir]/Mesen_vs_Immuno.GseaPreranked.12345/gsea_report_for_na_pos_12345.xls where 12345 is a unique number generated by GSEA. Alternately navigate to the file Supplementary_Table8_gsea_report_for_na_pos.xls.
Enrichment Neg - Verify that file is set to [your_path_to_gsea_dir]/Mesen_vs_Immuno.GseaPreranked.12345/gsea_report_for_na_neg_12345.xls where 12345 is a unique number generated by GSEA. Alternately navigate to the file Supplementary_Table9_gsea_report_for_na_neg.xls.
TROUBLESHOOTING: What is the random number appended to the GSEA directory name?
Every GSEA analysis generates a random number that is appended to the names of the files and directories. The number will be different for every new analysis.
TROUBLESHOOTING: Enrichment map uses a GMT file that was not the original file input to GSEA.
If EnrichmentMap cannot find the original GMT file used in the GSEA analysis, it will use a filtered GMT file found in the GSEA ‘edb’ results directory. EnrichmentMap will not be able to find your original GMT file if you have moved it since running GSEA analysis. Although it is a GMT file, it has been filtered to contain only genes found in the expression file. If you use this filtered file, you will get different pathway connectivity depending on the expression data being used. You should always use the original GMT file used for the GSEA analysis and not the filtered one in the results directory.
9B. Visualize enrichment results with EnrichmentMap use GSEA results - cont'd
Ranks - (Optional) Verify that the file is set to ranked_gene_list_na_pos_versus_na_neg_12345.xls where 12345 is a unique number generated by GSEA. Alternately, navigate to the file Supplementary_Table2_MesenvsImmuno_RNASeq_ranks.rnk).
Classes – (Optional) GSEA CLS file defining the phenotype (i.e. biological conditions) of each sample in the expression file, for example, see Supplementary_Table7_TCGA_OV_RNAseq_classes.cls).
Phenotypes – (Optional) In the text boxes replace ‘na_pos’ with ‘Mesenchymal’ and ‘na_neg’ with ‘Immunoreactive’. ‘Mesenchymal’ will be associated with red nodes as it corresponds to the positive phenotype while ‘Immunoreactive’ will be labeled blue.
Troubleshooting - Visualizing GSEA Results
TROUBLESHOOTING: What is the random number appended to the GSEA directory name?
Every GSEA analysis generates a random number that is appended to the names of the files and directories. The number will be different for every new analysis.
9B. Visualize enrichment results with EnrichmentMap use GSEA results - cont'd
Tune Parameters:
Number of Nodes: -
Set FDR q-value cutoff to 0.01.
Select Filter genes by expressions to exclude any genes in the gene set definition file (i.e. the gmt file) that are not found in the supplied expression file.
If Filter genes by expressions is not selected, any gene that is not found in the expression file will be retained and will be presented in the expression heat map viewer with all of its associated expression values grayed out.
Troubleshooting - Visualizing GSEA Results
TROUBLESHOOTING: Cannot provide a more precise value.
Scientific notation is not enabled. To set the threshold to a small number, select ‘Scientific Notation’ and set a q-value cutoff such as 1E-04.
9B. Visualize enrichment results with EnrichmentMap use GSEA results - cont'd
Tune Parameters cont'd:
Number of Edges: -
Keep the connectivity slider in the centre.
If the network is too cluttered because of too many connections (edges), move the slider to the left to make the network sparser.
Alternatively, if the network is too sparse (i.e. there are too many disconnected pathways), move the slider to the right to obtain a more densely connected network.
Troubleshooting - Visualizing GSEA Results
TROUBLESHOOTING: What happens when you move the similarity slider bar?
Moving the slider to the left (or right) will adjust the underlying similarity statistic threshold to make the resulting network sparser (or denser).
The slider is set with predefined defaults but users can fine-tune the similarity metric by selecting "Show advanced options" at the bottom of the "Create Enrichment Map" panel.
Predefined values appear as tick marks on the slider and include Jaccard > 0.35, Jaccard > 0.25, combined > 0.375, overlap > 0.5, overlap > 0.25.
9B. Visualize enrichment results with EnrichmentMap use GSEA results - cont'd
Click the Build button at the bottom of the EnrichmentMap Input panel. A Building EnrichmentMap box appears and indicates the progress status. This box will disappear once the map has been created successfully.
10. Resulting enrichment maps from g:Profiler and GSEA pipelines
11. Navigating and interpreting the enrichment map
An enrichment map must be interpreted to discover novel information about a data set and must be manually refined to create a publication quality figure.
To explore the enrichment map, select the network of interest in the Control Panel located at the left side of the Cytoscape window.
The Network Panel can be selected using the left most tab of the Control Panel.
The selected network appears in the main window, navigate to it (zoom and pan) using Cytoscape controls (section A) and explore the pathways by reading the gene set labels.
Pathways with many common genes often represent similar biological processes and are grouped together as sub-networks or themes in the network. Click on a node to display the corresponding genes in the table below the network view (section B).
Troubleshooting - Navigating Results
TROUBLESHOOTING: Few or no pathways present in enrichment map.
A pathway enrichment analysis resulting in few or no enriched pathways may be caused by suboptimal statistical processing used to define the original gene list.
Enriched pathways are unlikely to be found if the gene list ranks are too noisy and the most important genes are not at the top of the list, no genes are highly significant, or a large fraction of genes are highly significant.
If the gene list has been correctly defined, analyzing further databases of pathways and gene sets or setting more liberal filters may improve results.
12. Find specific genes
To find a gene or pathway of interest, type its name in the search bar located in the top right corner (section C).
All pathways containing that gene will be highlighted. For example, TP53 and BGN are the top genes in g:Profiler and GSEA analyses, respectively.
Troubleshooting - Navigating Results
TROUBLESHOOTING: Cannot find any pathways with my search gene.
Check that the gene identifier type used for the search matches the identifier type used in the analysis.
Multiple genes separated by spaces can be entered into the search bar. Any pathway that contains the gene will be selected and highlighted in the network.
Adding keywords “AND” into the search bar will show only pathways that contain all genes in the search query (e.g. "geneA AND geneB").
If the original analysis did not use gene symbols then you will not be able to search by gene symbols. Instead use the identifier type that the analysis was based on, for example Entrez Gene ID or Ensembl gene ID.
13. Find most enriched pathways
To find the most enriched pathways, look at the Table Panel located below the network view.
Select the Node Table tab (section D)
select and sort the column named “EM#_fdr_qvalue” (for g:Profiler) or “EM#_NES” (for GSEA) by clicking on the column header.
To highlight a subset of these pathways in the network, select rows corresponding to pathways of interest, right-click on any selected row in the table and select “Select nodes from selected rows”.
Troubleshooting - Navigating Results
TROUBLESHOOTING: Very few entries in node table although the network contains many nodes.
Some nodes in the network are already selected.
If there are very few records in the node table make sure that no nodes are selected in the network. Or click on the gear icon and change the setting from “Auto” to “Show all”.
14. Expression Viewer
When a gene expression matrix is provided as input to EnrichmentMap, we can study the expression pattern of the genes included in enriched pathways.
Click on an individual node to generate a gene expression heat map that will appear in the Heat Map tab of Table Panel.
If the analysis is based on GSEA results and a rank file is supplied, the leading edge genes will be highlighted in yellow for individual node selections. Several options for heat map visualization are available.
Troubleshooting - Navigating Results
TROUBLESHOOTING: An expression matrix was not supplied.
If no expression file is input to EnrichmentMap it will automatically create a dummy expression file where any gene found in the enrichment file will be given a placeholder expression value of 0.25, and any gene found in a pathway but not found in the enrichment results file be assigned a placeholder expression value of NA. Therefore clicking on any node in the enrichment map will show the genes used for the analysis as well as genes in the pathway that are not part of the query set.
14. Expression Viewer - cont'd
In the Table Panel Heat Map tab, it is possible to:
Adjust the Sort option: sorting options include hierarchical clustering, ranks, or no sorting. To change the sorting option, click on the Sort button visible in the top left corner of the heat map table (section A).
By default, the heat map is sorted by ranks if a rank file is supplied. In the absence of a rank file, no sort is applied. Additional rank files can be uploaded for comparison through the settings menu located at the top right corner in the Heat Map Panel (section F). .
If the analysis is based on GSEA results and a rank file is supplied, the leading edge genes will be highlighted in yellow for individual node selections. Several options for heat map visualization are available.
14. Expression Viewer - cont'd
Define Genes you wish to visualize in the heat map (section Bb). Data can be viewed for all genes contained in the selected nodes (union of nodes) or just for the genes common to selected nodes (intersection of nodes). By default, all genes are shown.
Change Expressions value visualization depending on your data type (section C). Data can be viewed as it was loaded (Values), or row normalized where the row mean is subtracted from every value and then divided by the row’s standard deviation (Row Norm), or log transformed (Log).
14. Expression Viewer - cont'd
Compress heat map columns (section 8D). By default, all expression values are visible as individual columns in the heat map for expression sets with less than 50 samples. It is possible to compress the data in a single column by selecting Compress, Data Set using the aggregation methods Median, Max (maximum) or Min (minimum). If a CLS file has been uploaded, the expression set can be compressed using one column per defined sample group using the Class option. If the expression matrix contains 50 samples or more, EnrichmentMap will automatically compress the values to their median value by default.
Check Values (section E) to show the expression numerical values in addition to the heat map color scale.
Additional fine tuning of the heat map can be done through the Settings Panel (section F) that includes functionality to add new rank files, export the heat map data as a tab delimited text file or PDF image, change the distance metric for hierarchical clustering, or turn on the node table heat map autofocus.
14. Expression Viewer - cont'd
The resulting heat map.
On this figure, genes are sorted using the GSEA rank file, highlighting the leading edge in yellow.
All genes contained in selected nodes are shown,
expression values are row normalized,
no compression is applied and individual expression numerical values are not shown.
Column headings are colored according to sample phenotype. Red color refers to the first phenotype (Mesenchymal), and blue to the second phenotype (Immunoreactive).
Troubleshooting - Navigating Results
TROUBLESHOOTING: Heatmap column names are not colored by dataset
No CLS file loaded or mismatch between CLS file and phenotype definition.If the heat map columns are not colored for a GSEA analysis, make sure the phenotype names specified in the EnrichmentMap input panel match the class names specified in the class file (MesenchymalvsImmunoreactive_RNA-Seq_classes.cls). Also see TROUBLESHOOTING 9Bxiii.
14. Expression Viewer - cont'd
The heat map can be exported to a text file for further analysis
Click on the Settings icon of the heat map (section F) and select Export as TXT.
If only an individual node is selected, a dialog will offer to export the Leading edge only for GSEA analysis. If selected, only the highlighted genes will be exported; otherwise, the entire set of genes is saved.
Specify the file name and location and click Save.
Troubleshooting - Navigating Results
TROUBLESHOOTING: Option to save only leading edge genes is not given.
Selection includes more than one node or data set contains no leading edge information (i.e was not built from GSEA results)
The leading edge is only available for GSEA analyses. The option will only appear if the Enrichment map was built with GSEA results and a rank file was specified.
15. Organize and clarify the network
If the network has too many nodes, go to the EnrichmentMap tab in the Control Panel and use the Node Cutoff Q-value threshold slider. Adjusting to a numerical value closer to 0 will remove less significant nodes (section E)
If the network is too interconnected, go to the EnrichmentMap tab in the Control Panel and increasing the Edge Cutoff (Similarity) threshold will remove connections between less related nodes (section F ).
The network layout may be applied again after adjusting the cutoffs (see the Layout menu in Cytoscape). The default layout algorithm is the unweighted Prefuse Force Directed layout. We also recommend the prefuse force-directed layouts weighted using the gene set similarity coefficient. Alternative layout algorithms are available and we encourage experimenting with them.
To restore nodes or edges, adjust threshold sliders to their original positions.
15. Organize and clarify the network - cont'd
Separate two different phenotypes:
It can be helpful to separate two different phenotypes (i.e. place all the red nodes to one side and all blue nodes to the other).
Go to the Select tab in the Control Panel
Click on the + symbol and select Column filter
Click on Choose column… and select “EM1_NES (Mesem_vs_Immuno)”
Click on the box next to between and change the value to zero. Click Apply at the bottom of the panel.
All red nodes should now be selected. Click and hold on any selected node and drag selection to the left until it does not overlap any blue nodes.
Select Layouts from the Cytoscape menu and apply Prefuse Force Directed Layout --> Selected Nodes Only --> (none).
15. Organize and clarify the network - cont'd
Do the same but for the blue nodes:
Go back to the Control Panel Select tab, adjust slider to select all negative values. Click on Apply at the bottom of the Select tab.
All blue nodes should now be selected. Click and hold on any selected node and drag selection to the right until it does not overlap any red nodes.
Select Layouts from the Cytoscape menu and apply Prefuse Force Directed Layout --> Selected Nodes Only --> (none).
Troubleshooting - Organizing Results
TROUBLESHOOTING: Which is the best layout to use?
There are many different layout algorithms available in Cytoscape that can be used for EnrichmentMap.
We recommend using an edge weighted layout, which considers the overlap score between pathways.
Most layouts (except yFiles) offer the ability to organize just the selected nodes.
Experiment with different layouts to see which works best with your data. If you do not like the resulting layout, press command-Z on macOS or ctrl-Z on Windows or click on Edit --> Undo to revert to the previous view.
16. Define major biological themes.
Enrichment maps typically include clusters of similar pathways representing major biological themes. Clusters can be automatically defined and summarized using the AutoAnnotate Cytoscape app. AutoAnnotate first clusters the network using the clusterMaker2 app and then summarizes each cluster based on word frequency within the pathway names via the WordCloud app.
From the Cytoscape menu bar, launch AutoAnnotate by selecting Apps, AutoAnnotate, New Annotation Set…
The AutoAnnotate: Create Annotation Set panel will appear.
In the Quick Start tab click on Create Annotations.
Node names contain uninformative words that are not excluded by default or are not considered during network normalization.
If particular non-informative words keep appearing in the labels generated by AutoAnnotate, try adjusting the WordCloud normalization factor.
The significance of each word is calculated based on the number of occurrences in the given cluster of pathways.
This causes frequent words such as “pathway” or “regulation” to be prominent. By increasing the normalization factor, we reduce the priority of such recurrent words in cluster labels. If that doesn’t help, you can add the non-informative words to the WordCloud word exclusion list.
16. Define major biological themes - cont'd
Each cluster in the network will have a circle annotation drawn around it and will be associated with a set of words (by default three) that correspond to the most frequent node labels in the cluster.
These words are automatically selected and often must be manually renamed (see Step 18iii).
Moving individual nodes within a cluster will automatically resize the surrounding circle and moving an entire cluster will redraw the surrounding circle in the new cluster location.
Troubleshooting - Defining major themes
TROUBLESHOOTING: Labels are bigger for bigger clusters but I want all the labels to be the same size.
Setting to scale labels to the size of the cluster is enabled.
The number of nodes in a cluster determines label size by default. Thus, the cluster size may relate to pathway popularity instead of importance in the experiment. Annotation labels can all be set to the same size by unchecking the option “Scale font by cluster size” in the AutoAnnotate results panel.
16. Define major biological themes - cont'd
Manually arrange clusters to clean up the figure. Move nodes to reduce node and label overlap. Figure above shows the results of this process.
17. Create a simplified network view
This creates a single group node for every cluster with a summarized name and provides an overview of the enrichment result themes that is useful for enrichment maps containing many nodes.
In Control Panel select the AutoAnnotate tab.
Click on the Menu icon in the upper right corner.
Select Collapse All.
17. Create a simplified network view - cont'd
Scale collapsed network for better viewing:
In the Cytoscape menu bar, select View --> Show Tool Panel.
Go to the Node Layout Tools panel located at the bottom of the Control Panel.
Locate the Scale slider bar and use it on unselected nodes (uncheck Selected only).
Move slider left to tighten the node spacing. Close the Node Layout Tools panel when finished.
TROUBLESHOOTING: Pop up after selecting "Collapse all" shows up everytime I collapse my clusters.
Have not specified "Don't ask me again" option on pop up.
Once you click on “Collapse All” a pop-up window will show the message “Before collapsing clusters please go to the menu Edit->Preferences->Group preferences and select “Enable attribute aggregation”. There is no need to adjust this parameter repeatedly. Click on “Don’t ask me again” and “OK” if you have set this parameter previously.
TROUBLESHOOTING: Collapsing my network takes a long time.
The larger the network or the more clusters in a network, the longer collapsing will take.
For large networks, collapsing and expanding may take time. For a quick view of the collapsed network you can create a summary network by selecting the “Create summary Network…”. There are two options for the summary network: “clusters only” which creates a summary network with just the circled clusters, or “clusters and unclustered nodes” which creates a summary network that also includes the singleton nodes not part of any cluster.
TROUBLESHOOTING: Collapsed network contain grey nodes instead of colored as they were in the pre-collapsed network.
Attribute aggregation in not enabled.
If the nodes in the resulting collapsed network are grey then you forgot to enable attribute aggregation. Expand clusters and before collapsing clusters again, go to the menu Edit->Preferences->Group preferences and select “Enable attribute aggregation".
18. Manually arranging the network
Manually arranging the network nodes and custom labeling the major themes is required for the clearest network view and for a publication quality figure. For instance, it is useful to bring together similar themes, such as signaling or metabolic pathways, even if they are not connected in the map.  This is a time consuming step, but the more effort spent, the higher quality the resulting figure will be.
If the focus of the figure is only on a subset of the network, it can be easier to work with just the subset.
To create this, select the nodes of interest, then in the Cytoscape menu bar select File --> New --> Network --> From selected nodes --> all edges
or alternatively use the corresponding icon in the Cytoscape tools menu bar (New network from selection, (all edges)).
18. Manually arranging the network - cont'd
When the purpose of the figure is to show a large network and highlight only the main themes,
clicking on Publication ready located at the bottom of the EM Control panel will remove node labels.
To revert to the original network, click on the Publication ready button again.
Automatic WordCloud-generated theme names are useful for quickly exploring an enrichment map, but frequently need to be renamed for publication quality figures.
Naming should carefully consider all pathways and genes within each theme.
Themes can be renamed in AutoAnnotate by right-clicking the name in the AutoAnnotate panel in the “Cluster” column, and selecting Rename…
Alternatively, labels can be changed in external drawing software (see Step 20). Note that re-clustering the network will reset theme names.
Troubleshooting - Arranging Network
TROUBLESHOOTING: In the EnrichmentMap input panel, the bottom options "Publication Ready" and "Set Signature Edge Width" are not visible.
The Node Layout Tool is open.
Close the Node Layout Tools window using the x symbol located at the top right corner.
19. Create a subnetwork
Create a subnetwork that highlights a specific theme subset. Enrichment maps of rich omics data sets are often large and complicated and it is often useful to emphasize specific themes or relevant pathways in a final figure. For example, we will select the top mesenchymal and immunoreactive pathways and create a subnetwork for detailed visualization.
Click on the Select tab in the Control Panel.
Click on the + symbol and select Column filter.
Click on Choose column… and select “EM1_NES (Mesem_vs_Immuno)”.
Click on the box next to between and replace the negative value by 2.5. Do not change the positive value next to inclusive. Click Enter.
19. Create a subnetwork - cont'd
Click on the + symbol and select Column filter.
Click on Choose column… and select “EM1_NES (Mesem_vs_Immuno)”.
Click on the box next to inclusive and change the value to -2.5. Do not change the negative value next to between. Click Enter.
Above the two column filters you just added, change the drop down from Match all (AND) to Match any (OR).
Click on Apply. Under the apply button, it should display the number of nodes and edges selected. In this example, 32 nodes should be selected.
19. Create a subnetwork - cont'd
From the Cytoscape menu, Select File --> New --> Network --> From selected nodes, all edges.
A new smaller network should appear. Manually move nodes around to optimize the layout.
Annotate the network as described in Step 16
Troubleshooting - Create subnetwork
TROUBLESHOOTING: The created subnetwork is empty.
Nodes were not selected prior to the creation of the subnetwork.
Make sure that the nodes that will be part of the subnetwork are selected before creation of the subnetwork.
20. Export an image
In the Cytoscape menu bar, select File --> Export as Image…
Set the Export File Format to PDF (*.pdf).
Click on Browse… to specify file name and location.
Click on Save to close the browser window and then on OK.
Troubleshooting - Export image
TROUBLESHOOTING: Exported image only contains a small subset of my network.
Only what is visible in the view is exported.
In image export, only the visible part of the map will be exported. Make sure that the entire network is visible on your screen before exporting.
21. Identify network creation parameters
Identify network creation parameters. In the previous step we exported the network as an image, but the parameters used to create the map are needed to interpret and reproduce the image.
In the Control Panel, locate the EnrichmentMap Input panel and click on the cog (settings) icon in the top right-hand corner.
Click on show Creation Parameters…
In the displayed panel you will find the FDR q-value, similarity metric and threshold parameters to be added to the text legend of figure. In this example: “Enrichment map was created with parameters q < 0.01, and Jaccard Overlap combined coefficient > 0.375 with combined constant = 0.5”.
TROUBLESHOOTING: The parameters shown do not match what I expected them to be.
The creation parameters panel only shows the parameters that were used at network creation. If you modified the network using filters or the EnrichmentMap slider bars you will have to update the thresholds accordingly.
22. Show and create a legend.
In the Control Panel, locate the EnrichmentMap Input panel and click on the cog icon in the top right corner.
Click on Show Legend.
The example shows a simplified legend, however many different node and edge attributes like size, shape and color can be used in the enrichment map to represent different aspects of the data.
It is important to describe them in the text associated with the figure or in the figure itself as a legend.
The basic legend components (available as SVG and PDF images at http://baderlab.org/Software/EnrichmentMap#Legends) that can be used for an enrichment map figure. You can manually select the components relevant to your analysis.
23. Save all work as a Cytoscape session
In the Cytoscape menu, select File --> Save As…
Navigate to the directory you wish to save the session and specify the desired file name.
Acknowledgements
Presentation is based on the publication "Pathway enrichment analysis and visualization of omics data using g:Profiler, GSEA and Cytoscape EnrichmentMap"
Written by:
Jüri Reimand, Ruth Isserlin, Veronique Voisin, Mike Kucera, Christian Tannus-Lopes, Asha Rostamianfar, Lina Wadi, Mona Meyer, Jeff Wong, Changjiang Xu, Daniele Merico, Gary D. Bader


Some believe the Cretaceous–Paleogene extinction event was a mass extinction of some three-quarters of plant and animal species on Earth including all dinosaurs.
Some believe that the extinction was triggered by a massive comet/asteroid impact and its catastrophic effects on the global environment and weather.


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Hi, I am Ziyin Zhang, a first year graduate student studying MS Digital Media at Georgia Tech.
I found my passion in interaction design and wearable computing after taking Computer Audio course in Summer 2016, in which I constructed and implemented a music wristband.
I have completed an internship with BeenaVision in Fall 2016 during which I worked on data visualization and front-end web development.
Currently I am looking for 2019 summer internship in interaction design and development. Shoot me an E-mail if you think I am a good fit for you team! My E-mail address is lindazhanghf @ gmail.com
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Read the Rules carefully before submitting your game!
Also, remember that only
top 100 entries
will get free t-shirts and CDs, so submitting another Breakout or
Flappy Bird clone,
stolen from the Internet, or any type of game taken from somebody
else's tutorial, will make no sense at all.


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Upload an image (aka your Facebook profile picture) to apply a filter for QUEST Recruiting!


Exercise 2
In this exercise, we will create a shopping checklist and write functions that will manipulate the list. We will use "shoplist.html" as a starting point and proceed with the following steps:
- Design the HTML to present a shopping checklist.
- Write a function that will create a shopping checklist item.
- Write a function that will add a new item to the checklist.


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Tn-Core web server
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--- layout: post title: ! 'Chef pain point 1: multiple repos' date: '2013-08-28T09:42:06-04:00' tags: tumblr_url: http://www.pburkholder.com/post/59585264397/chef-pain-point-1-multiple-repos ---
At $WORK we've been migrating from Puppet to Chef. I was in the minority in voting to stay with Puppet, since we were already 1/2 way through refactoring our initial Puppet implementation.
I have nothing against Chef as such, but there are some pain points that others considering a Puppet to Chef migration should consider. I need to write a full analysis of this migration, but with time short, I'll start by just sharing some pain points which I'll later pull into that magnum opus.
Multiple repos
Our Puppet code was in one repository. Our Chef code is in 24 repos so we can use Berkshelf that's tied to versions and branches. When a Puppet module failed due to some odd dependency, I could 'ack' through the repo to find the a clue for what I was missing. Try that with 15 repos. Ugh.


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When you first open Transient Events the app asks the LSST Transient Event Server for the most recent transient events which satisfy the criteria defined in Settings. These events are loaded showing their type and the date and magnitude of the event. In order to minimize the amount of data to be downloaded Transient Events only downloads a limited number of events at a time. If all events have not been downloaded the last row in the table will say "More...". Touch this row and the next group of data will be downloaded.
There may not be any events which match your filter settings. Try changing the Magnitude Limit Slider to a larger number, increasing the maximum event age and selecting more event types.
The surveys used by Transient Events are limited by the weather, the moon and occassional mechanical telescope problems. For about 7 days each month the telescope(s) cannot acquire data due to the full moon. Also, the telescope(s) cannot operate if the skies are not clear or the winds are to strong. The CRTS and CRTS2 telescopes are both in Arizona so they are both affected by the same weather patterns. These telescopes will shut down for about 2 months during July and August due to the Tucson area monsoon season. The CRTS3 telescope is located in Australia so it should provide data during the period when CRTS and CRTS2 are closed during the monsoons. For more information on the CRTS and CRTS2 status see the clear sky chart. For additional information on the CRTS telescopes visit the Catalina Real-Time Transient Survey home page.
The colors indicate the visibility of the object from the location and minimum altitude which are set in the Settings screen. Green means that the object is currently visible. Yellow means that the object is currently below the minimum altitude but will exceed that altitude at some later time. Red means that this object never gets above the minimum altitude at this location.
In Settings you can set Transient Events to only show you visible objects. If this switch is on then all objects will be green because they are all visible.
If want to see more information about an event simply touch the event. Touching the event will take you to a page containing more detailed information about the event as well as a series of images. The images show the original reference images and typically 4 of the new images which caused the event to be issued. The first image you see will be the first new image. The reference image is to the left (swipe to the right) and the other three new images are to the right (swipe to the left).
If you want to view this object in your telescope a finder chart is available by touching the "Finder Chart" button.
For objects of particular interest you can press the "+" button and save this object to your bookmarks or you can press the Compose button and email information about the event to anyone in your address book. If you save the information to your bookmarks, it will save all of the information locally so that you do not have to be connected to the internet to view the details. Unfortunately is is not possible to store the finder chart locally so that information will be unavailable if you do not have network access.


An ever-innovative giant in P2P payments exceeds expectations another time with its Q3 earnings results. In Q3 2017, PayPal added 8.2 million net new active accounts – a record addition to the platform in a quarter. PayPal ended the quarter with approximately 70 million consumer accounts opted into One Touch, and approximately 6 million merchants accepting One Touch.
More importantly, the company continues to make aggressive strides to create a strong supportive network. Since early 2016, the company added 26 partnership agreements.
Image source:PayPal-Q3 2017 Investor Update
Image source: PayPal-Q3 2017 Investor Update
Revenues of $3.239 billion (+21%, or 22% FX neutral basis) beat consensus of $3.178 billion and guidance of $3.140-$3.190 billion.
EPS of $0.46 beat consensus of $0.43 and guidance of $0.42-$0.44.
Early 2018 guidance above consensus. Management provided an initial outlook for FY18, calling for 20% growth in net revenue and non-GAAP operating income, implying flat operating margins.
Management views mid to high single-digit growth in operating expenses in 2018, after adjusting for expenses associated with two recent acquisitions (TIO and Swift Financial).
The biggest surprise was guidance on funding costs. Transaction expense was 97 bps, versus 100 bps last quarter and below the 100 bps expectation.
Management viewed 97 bps (+/- a few bps) as a sustainable level going forward, which was an unexpected surprise.
Transaction and loan losses were uncharacteristically high, worth monitoring in light of PayPal’s growing consumer lending book.
Guidance and estimates: FY17 net revenue guidance was raised to $12.920- $12.980 billion (19-20%, or 20-21% FX neutral), while FY17 non-GAAP EPS guidance was increased to $1.86-$1.88 (24-25%). The company is increasing its EPS to FY17 $1.89, FY18 $2.30, and FY19 $2.70. Increasing PT to $64, from $50 and maintaining neutral rating.
Qudian, an online small credit provider from China went public on the New York Stock Exchange Wednesday morning.
The Alibaba-backed China-based company, which was founded by 34-year-old Min Luo, targets China’s younger and underserved markets with small loans via its mobile app. This is great news for FinTech in general and lending startups in particular.
Qudian, raised $900 million from the IPO, making it one of the largest initial public offerings of a Chinese company in the US this year.
The software prototypes of three different models for decentralized interbank payment and settlements have been successfully developed by the blockchain consortium led by the Monetary Authority of Singapore (MAS), the city-state’s central bank and financial regulator, and the Association of Banks in Singapore (ABS).
The project, conducted together with 11 financial institutions and five technology companies, is the Phase 2 of Project Ubin, which explored the use of distributed ledger technology (DLT) for clearing and settlement of payments and securities.
Project Ubin was triggered in late-2016 with an aim to better understand blockchain and its potential benefits through practical experimentation. MAS has said that “digital central bank issued tokens” could enable the development of simpler to use and more efficient alternatives to today’s systems.
In Singapore, OCBC Bank, HSBC, and MUFG are already working on a KYC blockchain project to eliminate the duplicity of KYC processes and provide more convenience to customers and efficiency across the banks.
In trade finance, DBS Bank and Standard Chartered Bank are currently exploring the use of blockchain to prevent the problem of duplicate invoicing while preserving client confidentiality.


{% extends '_main.html' %} {% block page_title %}Request access to tools - you have access to our tools{% endblock %} {% block breadcrumbs %}
You can use our tools to analyse data


Class GlobalFilters
Sets of header and parameter names to filter out of response hashing and serialization. Used to prevent ephemeral values types from changing the hashed name of a response and to prevent sensitive information like api keys or auth tokens from being stored.
Inherited Members
Namespace: FakeHttp
Assembly: FakeHttp.dll
Syntax
public static class GlobalFilters
Properties
| Improve this Doc View SourceHeaderNames
A list of names that will prevent specific headers from being serialized
Declaration
public static HashSet<string> HeaderNames { get; }
Property Value
| Type | Description |
|---|---|
| HashSet<String> |
ParameterNames
A list of parameter names that will prevent them from being being serialized or included as part of responses' hashed names
Declaration
public static HashSet<string> ParameterNames { get; }
Property Value
| Type | Description |
|---|---|
| HashSet<String> |
SensitiveHeaderNames
A list of names that represent senstive data and will prevent specific headers from being serialized
Declaration
public static HashSet<string> SensitiveHeaderNames { get; }
Property Value
| Type | Description |
|---|---|
| HashSet<String> |
SensitiveParameterNames
A list of parameter names that represent senstive data and will prevent them from being being serialized or included as part of responses' hashed names
Declaration
public static HashSet<string> SensitiveParameterNames { get; }
Property Value
| Type | Description |
|---|---|
| HashSet<String> |


This folder contains a number of simple slurm script examples, from very basic, slurm_simple and slurm_basic to slurm_array which does array jobs. These can be used as templates for your own scripts.
For more information about slurm scripts see the discussion Batch Scripting for Parallel Systems off of http://geco.mines.edu/prototype/Show_me_some_local_HPC_tutorials.
There are a number of programs to be driven by the scripts.
helloc.c and hellof.f90 are basic hello world programs in MPI.
invertf.f90 is an OpenMP program that does 8 matrix inversions in parallel.
stc_01.c and stf_01.f90 are finite difference applications also in MPI
phostone.c is hybrid MPI/OpenMP glorified hello world program. It has a number of options which can be used to generate information about nodes and cores on which the program is being run. The options can bee seen by running with the -h option.
The makefile can be used to build the applications. You will need to have MPI compilers in your path for the makefile to work. Most MPI compilers brought in to your environment via the module system should work. For example:
module purge module load PrgEnv/devtoolset-6 PrgEnv/mpi/openmpi/gcc/3.0.0 make
slurm_simple
Near minimum slurm script. Run "hello" on two nodes, with four tasks per node. Output will be in the default slurm-#######.out file, where ####### is the job number
slurm_basic
Standard out and standard error are put in the files %J.out and %J.error where %J is the job number. The script runs the finite difference codes stc_01 and stc_01 on two nodes, eight total tasks. These receive input from the file st.in. For the Fortran version of the program output is redirected to a file. Finally, the glorified hello world hybrid MPI/OpenMP program is run on four tasks with four threads per task. With the -F option you will get a report of mapping of threads/tasks to nodes and cores.
slurm_openmp
This runs the matrix inversion program using 2, 4, and 8 threads. Output is put in the files two, four, and eight.
heterogen
This shows one way to run different MPI codes (Fortran and C in this case) on various cores as part of the MPI run.
slurm_record
This runs the glorified hello world hybrid MPI/OpenMP program. With lots of bells and whistles. Some of the other things it does:
# Go to the directoy from which our job was launched # Create a short JOBID base on the one provided by the scheduler # Create a "base name" for a directory in which our job will run # Create a directoy for our run based on the $JOBID and go there # Create a link back to our starting directory # Save a copy of our environment and script # Set the path to our program # Set a string which is the argument list for our program # Run the program with stdout going to our starting director # Copy the standard output to our run directory.
slurm_array
This runs an array of slurm jobs. For more information about array jobs see the end of Batch Scripting for Parallel Systems as discussed above. Array jobs allow a large number of similar jobs to be run, (bag of tasks) easily.
In this example each job will be given its own directory. Then a simple python program will be run that just reports environmental information along with printing the command line arguments. The script will read two files if they exist, pars, and list. These files will contain sets of command line arguments and directory names. Each subarray job will read a single line from these two files. The line read is determined by the subarray job number, $SLURM_ARRAY_TASK_ID. If the files pars and list do not exist then the directory name will default to mysub_$SLURM_ARRAY_TASK_ID and the string containing the command line arguments will default to the date.
To run 16 subarray jobs you can use the command:
sbatch --array=1-16 slurm_array
Note the array range does not need to start with 1.
You can generate the files pars and list by running the command:
make dat


Written by: Jeffrey Astrof & Mike Sikowitz.
Transcribed by: guineapig
[Scene: Central Perk, everyone but Phoebe is there.]
Phoebe: (entering) Hi guys!
All: Hey, Pheebs! Hi!
Ross: Hey. Oh, oh, how'd it go?
Phoebe: Um, not so good. He walked me to the subway and said 'We should do this again!'
All: Ohh. Ouch.
Rachel: What? He said 'we should do it again', that's good, right?
Monica: Uh, no. Loosely translated 'We should do this again' means 'You will never see me naked'.
Rachel: Since when?
Joey: Since always. It's like dating language. Y'know, like 'It's not you' means 'It is you'.

Phoebe: Or, or, y'know, um, 'I think we should see other people' means 'Ha, ha, I already am'.
Rachel: And everybody knows this?
Joey: Yeah. Cushions the blow.

Ross: That's funny, that, no, because, uh, our parents actually did, uh, send our dog off to live on a farm.
Monica: Uh, Ross.
Ross: What? Wh- hello? The Millners' farm in Connecticut? The Millners, they had this unbelievable farm, they had horses, and, and rabbits that he could chase and it was- it w- .....Oh my God, Chi Chi!
Opening Credits
[Scene: Chandler and Joey's, Chandler is helping Joey rehearse for a part.]
Chandler: "So how does it feel knowing you're about to die?"
Joey: "Warden, in five minutes my pain will be over. But you'll have to live with the knowledge that you sent an honest man to die."
Chandler: Hey, that was really good!
Joey: Thanks! Let's keep going.
Chandler: Okay. "So. Whaddya want from me, Damone, huh?"
Joey: "I just wanna go back to my cell. 'Cause in my cell, I can smoke."
Chandler: "Smoke away."
(Joey takes out a pack of cigarettes and a lighter. He fumbles and drops the lighter. Then he lights a cigarett, takes a drag, and coughs.)
Chandler: I think this is probably why Damone smokes in his cell alone.
Joey: What?
Chandler: Relax your hand!
(Joey lets his wrist go limp.)
Chandler: Not so much!
Joey: Whoah!
Chandler: Hey!
Joey: Hey!
Chandler: Alright, now try taking a puff.
(Joey tries and visibly winces.)
Chandler: Alright.. okay. No. Give it to me.
Joey: No no no, I am not giving you a cigarette.
Chandler: It's fine, it's fine. Look, do you wanna get this part, or not? Here.
(Joey reluctantly gives him the cigarette.)
Chandler: Don't think of it as a cigarette. Think of it as the thing that's been missing from your hand. When you're holding it, you feel right. You feel complete.
Joey: Y'miss it?
Chandler: Nah, not so much. Alright, now we smoke. (Takes a puff.) Oh.. my.. God. (He continues to smoke.)
[Scene, Central Perk, everyone except Phoebe and Rachel is there.]
Monica: No, no, no. They say it's the same as the distance from the tip of a guy's thumb to the tip of his index finger.
(The guys stretch out their fingers.)
Joey: That's ridiculous!
Ross: Can I use.. either thumb?
Rachel: (carrying a tray of drinks) Alright, don't tell me, don't tell me! (Starts handing them out.) Decaf cappucino for Joey.. Coffee black.. Late.. And an iced tea. I'm getting pretty good at this!
All: Yeah. Yeah, excellent.
Rachel: (leaving to serve others) Good for me!
(The gang swaps all the drinks for what they ordered as Phoebe enters. She sits down without saying hi.)
Joey: Y'okay, Phoebe?
Phoebe: Yeah- no- I'm just- it's, I haven't worked- It's my bank.
Monica: What did they do to you?
Phoebe: It's nothing, it's just- Okay.
Phoebe: - and there's five hundred extra dollars in my account.
Chandler: Oh, Satan's minions at work again...
Phoebe: Yes, 'cause now I have to go down there, and deal with them.
Joey: What are you talking about? Keep it!
Phoebe: It's not mine, I didn't earn it, if I kept it, it would be like stealing.
Rachel: Yeah, but if you spent it, it would be like shopping!
Phoebe: Okay. Okay, let's say I bought a really great pair of shoes. Do you know what I'd hear, with every step I took? 'Not-mine. Not-mine. Not-mine.' And even if I was happy, okay, and, and skipping- 'Not-not-mine, not-not-mine, not-not-mine, not-not-mine'...
Monica: We're with you. We got it.
(Chandler leans over the back of the couch out of sight.)
Phoebe: Okay. I'd- just- I'd never be able to enjoy it. It would be like this giant karmic debt.
Rachel: Chandler, what are you doing?
Monica: (puling him up) Hey. Whaddya doing?
(Chandler tries to shrug nonchalantly but eventually he has to exhale a mouthful of smoke.)
All: Oh! Oh, God!
Ross: What is this?!
Chandler: I'm smoking. I'm smoking, I'm smoking.
Phoebe: Oh, I can't believe you! You've been so good, for three years!
Chandler: And this- is my reward!
Ross: Hold on a second, alright? Just think about what you went through the last time you quit.
Chandler: Okay, so this time I won't quit!
All: Ohhh! Put it out!
Chandler: All right! I'm putting it out, I'm putting it out. (He drops it in Phoebe's coffee.)
Phoebe: Oh, no! I- I can't drink this now!
Monica: Alright. I'm gonna go change, I've got a date.
Rachel: This Alan again? How's it goin'?
Monica: 'S'going pretty good, y'know? It's nice, and, we're having fun.
Joey: So when do we get to meet the guy?
Monica: Let's see, today's Monday... Never.
All: Oh, come on! Come on!
Monica: No. Not after what happened with Steve.
Chandler: What are you talking about? We love Schhteve! Schhteve was schhexy!.. Sorry.
Monica: Look, I don't even know how I feel about him yet. Just give me a chance to figure that out.
Rachel: Well, then can we meet him?
Monica: Nope. Schhorry.
[Scene: Iridium, Monica and Paula are at work.]
Monica: I mean, why should I let them meet him? I mean, I bring a guy home, and within five minutes they're all over him.
Paula: Listen. As someone who's seen more than her fair share of bad beef, I'll tell you: that is not such a terrible thing.
Monica: I know. I just wish that once, I'd bring a guy home that they actually liked.
Paula: Well, you do realise the odds of that happening are a little slimmer if they never get to meet the guy..
[Scene: Monica and Rachel's, Chandler is smoking out on the balcony, Phoebe is absent.]
Joey: Let it go, Ross.
Ross: Yeah, well, you didn't know Chi Chi.
Monica: Do you all promise?
All: Yeah! We promise! We'll be good!
Monica: (shouts to Chandler) Chandler? Do you promise to be good?
(Chandler makes a 'Cross my heart' sign. It starts to rain and he taps on the window.)
Joey: You can come in, but your filter-tipped little buddy has to stay outside!
(Chandler sulkilty picks up a garbage can lid and uses it as an umbrella.)
(Phoebe enters, walks to the couch, sits down, and begins to read a letter without saying hi.)
Ross: Hey, Pheebs.
Phoebe: 'Dear Ms. Buffay. Thank you for calling attention to our error. We have credited your account with five hundred dollars. We're sorry for the inconvenience, and hope you'll accept this- (Searches in her purse) -football phone as our free gift.' Do you believe this?! Now I have a thousand dollars, and a football phone!
Rachel: What bank is this?
(The intercom buzzes.)
Monica: Hey. It's him. (On the intercom) Who is it?
Alan: (on the intercom) It's Alan.
Joey: (shouting to Chandler) Chandler! He's here!
(Chandler comes in, dripping wet.)
Monica: (to all) Okay, please be good, please. Just remember how much you all like me.
(She opens the door and Alan enters.)
Monica: Hi. Alan, this is everybody. Everybody, this is Alan.
Alan: Hi.
All: Hi, Alan.
Alan: I've heard schho much about all you guyschh!
(Everyone laughs.)
[Time lapse, Alan is leaving.]
Monica: (to Alan) Thanks. I'll call you tomorrow. (Alan exits, to all) Okay. Okay, let's let the Alan-bashing begin. Who's gonna take the first shot, hmm?
(Silence.)
Monica: C'mon!
Ross: ...I'll go. Let's start with the way he kept picking at- no, I'm sorry, I can't do this, can't do this. We loved him.
All: Loved him! Yeah! He's great!
Monica: Wait a minute!
All: Yeah!
Rachel: And did you notice...? (She spreads her thumb and index finger.)
The Guys: (reluctantly) Yeah.
Joey: Know what was great? The way his smile was kinda crooked.
Phoebe: Yes, yes! Like the man in the shoe!
Ross: ...What shoe?
Phoebe: From the nursery rhyme. 'There was a crooked man, Who had a crooked smile, Who lived in a shoe, For a... while...'
(Dubious pause.)
Ross: ...So I think Alan will become the yardstick against which all future boyfriends will be measured.
Rachel: What future boyfriends? Nono, I th- I think this could be, y'know, it.
Monica: Really!
Chandler: Oh, yeah. I'd marry him just for his David Hasselhof impression alone. You know I'm gonna be doing that at parties, right? (Does the impression)
Ross: You know what I like most about him, though?
All: What?
Ross: The way he makes me feel about myself.
All: Yeah...
Commercial Break
[Scene: Central Perk, Monica is alone as Ross, Rachel, Chandler, and Joey enter dejectedly in softball gear.]
Monica: Hi.. how was the game?
Ross: Well..
All: WE WON!! Thank you! Yes!
Monica: Fantastic! I have one question: How is that possible?
Joey: Alan.
Ross: He was unbelievable. He was like that-that-that Bugs Bunny cartoon where Bugs is playing all the positions, right, but instead of Bugs it was first base-Alan, second base-Alan, third base-...
Rachel: I mean, it-it was like, it was like he made us into a team.
Chandler: Yep, we sure showed those Hassidic jewellers a thing or two about softball..
Monica: Can I ask you guys a question? D'you ever think that Alan is maybe.. sometimes..
Ross: What?
Monica: ..I dunno, a little too Alan?
Rachel: Well, no. That's impossible. You can never be too Alan.
Ross: Yeah, it's his, uh, innate Alan-ness that-that-that we adore.
Chandler: I personally could have a gallon of Alan.
[Scene: A street, Phoebe walks up to a homeless person (Lizzie) she knows.]
Phoebe: Hey, Lizzie.
Lizzie: Hey, Weird Girl.
Phoebe: I brought you alphabet soup.
Lizzie: Did you pick out the vowels?
Phoebe: Yes. But I left in the Ys. 'Cause, y'know, "sometimes y". Uh, I also have something else for you. (She searches in her purse.)
Lizzie: Saltines?
Phoebe: No, but would you like a thousand dollars and a football phone?
Lizzie: What? (She opens the envelope Phoebe has given her.) Oh my God, there's really money in here.
Phoebe: I know.
Lizzie: Weird Girl, what are you doing?
Phoebe: No, I want you to have it. I don't want it.
Lizzie: No, no, I ha-I have to give you something.
Phoebe: Oh, that's fine, no.
Lizzie: Would you like my tin-foil hat?
Phoebe: No. 'Cause you need that. No, it's okay, thanks.
Lizzie: Please, let me do something.
Phoebe: Okay, alright, you buy me a soda, and then we're even. Okay?
Lizzie: Okay.
Phoebe: Okay.
[Scene: Chandler's office, Chandler looks around, opens his desk drawer, takes a puff of a cigarette, sprays around some air freshener, and takes some breath spray. He types for a little while, opens the drawer again, and takes another drag of the cigarette. While not paying attention, he sprays the breath spray around the room, takes a squirt of air freshener and gags.]
[Scene: A Street, Phoebe and Lizzie are at a hot dog vendor.]
Lizzie: Keep the change. (To Phoebe) Sure you don't wanna pretzel?
Phoebe: No, I'm fine.
Lizzie: (leaves) See ya.
(Phoebe opens the can and reacts.)
Phoebe: Huh!
[Scene: Central Perk, Phoebe is telling everyone about her discovery.]
Ross: A thumb?!
(Phoebe nods.)
All: Eww!
Phoebe: I know! I know, I opened it up and there it was, just floating in there, like this tiny little hitch-hiker!
Chandler: Well, maybe it's a contest, y'know? Like, collect all five?
Phoebe: Does, um, anyone wanna see?
All: Nooo!
(Chandler lights a cigarette.)
All: Oh, hey, don't do that! Cut it out!
Rachel: It's worse than the thumb!
Chandler: Hey, this is so unfair!
Monica: Oh, why is it unfair?
Chandler: So I have a flaw! Big deal! Like Joey's constant knuckle-cracking isn't annoying? And Ross, with his over-pronouncing every single word? And Monica, with that snort when she laughs? I accept all those flaws, why can't you accept me for this?
(An awkward silence ensues.)
Joey: ...Does the knuckle-cracking bother everybody?
Rachel: Well, I-I could live without it.

(Phoebe spits out her hair.)
Ross: Oh, now, don't listen to him, Pheebs, I think it's endearing.
Joey: Oh, (Imitating Ross) "you do, do you"?
(Monica laughs and snorts.)
Ross: You know, there's nothing wrong with speaking correctly.
Rachel: "Indeed there isn't"... I should really get back to work.
Phoebe: Yeah, 'cause otherwise someone might get what they actually ordered.
Rachel: Ohh-ho-hooohhh. The hair comes out, and the gloves come on.
(They degenerate into bickering and Chandler happily starts to smoke, undisturbed.)
[Scene: Iridium, Monica and Paula are working.]
Monica: Did you ever go out with a guy your friends all really like?
Paula: No.
Monica: Okay.. Well, I'm going out with a guy my friends all really like.
Paula: Waitwait.. we talking about the coyotes here? All right, a cow got through!
Monica: Can you believe it? ...Y'know what? I just don't feel the thing. I mean, they feel the thing, I don't feel the thing.
Paula: Honey.. you should always feel the thing. Listen, if that's how you feel about the guy, Monica, dump him!
Monica: I know.. it's gonna be really hard.
Paula: Well, he's a big boy, he'll get over it.
Monica: No, he'll be fine. It's the other five I'm worried about.
[Scene: Cental Perk, Joey and Ross are persecuting Chandler about his smoking.]
Joey: Do you have any respect for your body?

Chandler: Hey, y'know, I have had it with you guys and your cancer and your emphysema and your heart disease. The bottom line is, smoking is cool, and you know it.
Rachel: (holding the phone out to Chandler) Chandler? It's Alan, he wants to speak to you.
Chandler: Really?  Oh, she told you about that, huh. Well, yeah, I have one now and then. Well, yeah, now. Well, it's not that big- ..well, that's true,.. Gee, y'know, no-one- no-one's ever put it like that before. Well, okay, thanks! (He hands the phone back and stubs out his cigarette.)
Rachel: (to Ross, who has wandered up) God, he's good.
Ross: If only he were a woman.
Rachel: Yeah.
(They give each other a dubious look.)
[Scene: Monica and Rachel's, everyond except Monica and Joey is watching Lambchop.]
Chandler: Ooh, Lambchop. How old is that sock?
Ross: Okay. I think it's time to change somebody's nicotine patch. (Does so.)
Monica: (entering) Hey. Where's Joey?
Chandler: Joey ate my last stick of gum, so I killed him. Do you think that was wrong?
Rachel: I think he's across the hall.
Monica: Thanks. (Goes to fetch him.)
Ross: (finishing changing Chandler's nicotine patch) There y'go.
Chandler: (deadpan) Ooh, I'm alive with pleasure now.
 Pheebs?
Phoebe: Does anyone want the rest of this Pop-Tart?
Ross: Hey, I might!
Phoebe: Sorry. ..
All: You're kidding. Oh my God.
Phoebe: And on my way over here, I stepped in gum. ...What is up with the universe?!
Joey: (dragged in by Monica, he has just gotten out of the shower) What's going on?
Monica: Nothing. I just think it's nice when we're all here together.
Joey: Even nicer when everyone gets to wear their underwear..
Rachel: Uh, Joey..
Joey: Oh, God! (Hurriedly closes his legs.)
Monica: (turns off the TV) Okay..
All: Oh! That was Lambchop!
Monica: Please, guys, we have to talk.
Phoebe: Wait, wait, I'm getting a deja vu...no, I'm not.
Monica: Alright, we have to talk.
Phoebe: There it is!
Monica: Okay. It's-it's about Alan. There's something that you should know. I mean, there's really no easy way to say this.. uh.. I've decided to break up with Alan.
(They all gasp and clutch each other.)
Ross: Is there somebody else?
Monica: No, nononono.. it's just.. things change. People change.
Rachel: We didn't change..
Joey: So that's it? It's over? Just like that?

Monica: -but that wouldn't be fair to me, it wouldn't be fair to Alan- It wouldn't be fair to you!
Ross: Who-who wants fair? Y'know, I just want things back. Y'know, the way they were.
Monica: I'm sorry..
Chandler: (sarcastic) Oh, she's sorry! I feel better!
Rachel: (tearful) I just can't believe this! I mean, with the holidays coming up- I wanted him to meet my family-
Monica: I'll meet somone else. There'll be other Alans.
All: Oh, yeah! Right!
Monica: Are you guys gonna be okay?
Ross: Hey hey, we'll be fine. We're just gonna need a little time.
Monica: (dubious) I understand.
[Scene: A Restaurant, Monica is breaking the news to Alan.]
Alan: Wow.
Monica: I'm, I'm really sorry.
Alan: Yeah, I'm sorry too. But, I gotta tell you, I am a little relieved.
Monica: Relieved?
Alan: Yeah, well, I had a great time with you.. I just can't stand your friends.
Closing Credits
[Scene: Monica and Rachel's, everyone is mopping around and eating ice cream.]
Rachel: Remember when we went to Central Park and rented boats?.. That was fun.
Ross: Yeah. He could row like a viking.
Monica: (entering) Hi.
All: Mmm.
Ross: So how'd it go?
Monica: Oh, y'know..
Phoebe: Did he mention us?
 (She sits down and Ross strokes her forehead.)
Chandler: ...That's it. I'm getting cigarettes.
All: No no no!
Chandler: (leaving) I don't care, I don't care! Game's over! I'm weak! I've gotta smoke! I've gotta have the smoke!
Phoebe: (shouting as he leaves) If you never smoke again I'll give you seven thousand dollars!
Chandler: (returns) Yeah, alright.
End