A dimensionality reduction technique particularly well suited for visualizing data. (For references, see https://lvdmaaten.github.io/tsne)
The parameters that were used for running t-SNE here are: 50 initial dimensions, perplexity of 30, and theta of 0.5. For datasets with <= 5000 samples, the standard t-SNE algorithm is used. For larger datasets, the Barnes-Hut algorithm is employed.
A dimensionality reduction technique in which the two principal components are chosen to have the largest possible variance.
To analyze relationships between perturbations, we utilize the framework of connectivity. A connectivity score between two perturbations quantifies the similarity of the cellular responses evoked by these perturbations. A score of 1 means that these two perturbations are more similar to each other than 100% of other perturbation pairs. A score of -1 means that these two perturbations are more dissimilar to each other than 100% of other perturbation pairs.
See a heatmap of connections between individual perturbagens in cell lines and all other perturbagens used for the P100 assay or the GCP assay. The tutorial describes the features of the heatmap.
Bring data, in GCT format, from your own P100 or GCP studies to query against our datasets.
Introspect means querying your dataset against itself. Make sure to "Include Introspect" if you would like to see connections within your dataset (in addition to connections between your dataset and Touchstone-P).
In computing connectivity, biological or technical replicates can be aggregated together. Please select which metadata fields should be used to recognize replicates. For example, if you wish to distinguish between different doses of the same compound, make sure to select "pert_dose" (or something similar) as one of the metadata fields by which to group replicates. The possible metadata fields by which to group replicates only appear after you have upload your GCT and selected "Yes" for "Are there replicates in your data?".
Matched mode: When running GUTC, incorporates cell-line information to match
query data against matching cell types in Touchstone. Currently
this includes the following 9 cell types : [A375, A549,
HEPG2, HCC515, HA1E, HT29, MCF7, PC3, VCAP].
Unmatched mode (recommended): When running GUTC, does not incorporate cell-line information when querying
the data against Touchstone signatures.
L-Build ("Light" Build): All levels of L1000 data up to aggregated signatures.
Full Build: All levels of L1000 data up to aggregated signatures, as well as all relevant additional analyses
of the data (Introspect, t-SNE, PCA, etc.).
When querying Touchstone, Feature Space determines what set of genes to query against. When perturbagens are profiled on the L1000 platform, Landmark is recommended. When the queries you wish to use are not landmarks, use BING instead.
Root location within a brew folder that contains the instance matrices and the brew_group folder. Default is brew/pc
List of expected treatment doses in micromolar as a listmaker list. If provided, dose discretization is applied to the pert_dose metadata field to generate a canonicalized pert_idose field. Note this assumes that the pert_dose annotations are in micromolar.
Generates TAS plots and connectivity heatmap of preliminary callibration plates to identify the most suitable experimental conditions of specified parameters. Tool should be run on small pilot experiments, with a variety of experimental parameters such as seeding density and time point. Plots can also be decoupled by parameters such as cell id.
Column filter to sig_build_tool as a listmaker collection
The name of the build used when generating all associated files and folders (e.g. <BUILD_CODE>_metadata). For this reason, the code must be filename compatible.
When merging replicates for L1000, several versions of the merged data are made. This parameter determines which version to use when creating your build. by_rna_well is the default. by_rna_well is recommended.
All data is from the Cancer Cell Line Encyclopedia resource. Expression data was released 15-Aug-2017, copy number data is dated 27-May-2014, and mutational data is dated 15-Aug-2017.
Feature Mapping: Ensembl Ids from the source data were mapped to Entrez Gene Ids using gene annotations from NCBI (downloaded on 02-Mar-2016).
Normalization: RNAseq RPKM values were log2 transformed using log2(max(RPKM, eps)). The data were then normalized
such that the expression values were comparable across cell lines, by minimizing technical variation
and equalizing their distributions (for details of the normalization, see LISS and QNORM entries in the Connectopedia glossary). Post-normalization, the expression values range between 4 and 15 log2 units, with 4 indicating that
a gene is minimally or not expressed and 15 indicating the maximum readout.
Z-scores: The number of standard deviations that a gene is above or below the population mean is called its z-score. The "robust" z-score is resistant to outliers by using median instead of mean and median absolute deviation (MAD) instead of standard deviation. The reference population used to compute the median and MAD for a
particular gene is all CCLE lines with data for that gene.
Z-scores Within Primary Site: Similar to z-scores,
but the reference population used to compute the median and MAD is all CCLE lines from the same lineage with data for that gene.
All scores indicated are in log 2 ratios to reference, binned using the heuristics described in CNVkit.
Deletion: score < -1.1
Loss: -1.1 ≤ score ≤ -0.25
No change: -0.25 < score < +0.2
Gain: +0.2 ≤ score < +0.7
Amplification: +0.7 ≤ score
Access a suite of analysis apps by clicking on the menu (or type command-K to open)
Explore the Connectivity Map by typing here and pressing Enter (see instructions below the search box)
Switch between running a single query and running a batch query.
Give each query a descriptive name that will help you identify your results.
Tip: Each list can have a different number of genes; in fact, you can run a query with only one list (up OR down).
Your query will take about 5 minutes to process; check the History section in the Menu for your results!
Valid genes used in the query have HUGO symbols or Entrez IDs and are well-inferred or directly measured by L1000 (member of the BING gene set). Valid genes not used in a query are those that have a valid HUGO or Entrez identifier but are not part of the BING set. Invalid genes do not have HUGO or Entrez IDs.
Give each query a descriptive name that will help you identify your results.
Your query will take about 5 minutes to process; check the History section in the Menu for your results!
The sig_fastgutc_tool is a reimplementation of our query algorithm that enables faster query results, especially at larger batch sizes. It is the result of crowd-sourced contest. It is currently in beta mode.
Filter datasets by category to see only those of interest.
Data Icons identify published and proprietary datasets.
Click on a row to see a summary of that dataset, including cell lines and treatment conditions, assay type, and dates.
Arrange the table to display the information most important for your work, and add key datasets to favorites.
View details about the collection as a whole and about individual compounds.
View subsets of compounds based on mechanism, drug target, or known disease application.
Purity is assessed by ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) of compounds after receipt from the vendor.
Status as of publication of this resource (March 2017). We will be updating this but let us know if you notice a discrepancy.
Click on a compound to see details about its structure, mechanism, targets, approval status, and vendor.
Mouse over this graphic to see the classes of proteins targeted by drugs in the hub.
Search CMap for connections. First, search for a perturbagen and check the box to select it. Then view its connections as a list or heatmap.
This is the current count of perturbagens in the reference (touchstone) dataset.
Select data from perturbagens grouped by their MoA or role in the cell.
Choose a perturbagen type, or view them all.
Touchstone is our reference dataset, made from well-annotated perturbagens profiled in a core set of 9 cell lines.
Detailed List is unavailable for Touchstone v1.1.1.1. A new data visualization approach is in development, but to get results in a table format (similar to Detailed View), please click on Heat Map and download the dataset as a GCT file that can be viewed in Excel or similar apps. Please see here for a detailed explanation.
Use the Command app to retrieve the most up-to-date CMap information. Type a slash (/) to see the list of commands available. Select a command, and type the MoA, gene, perturbagen, or other keyword after it to specify your request.
Articles are tagged with topics. Click on a topic tag to see all related articles.
Look it up! A quick reference guide of CMap terms and their meanings.
Email us with your questions.
Click on the heading to read all the articles in this section on a single page, or open each article separately.
Click on a heading to open a menu of articles.
Articles with the same tag(s) as this article can be opened here. To see all articles associated with any tag, click on the tag from the list below.
Each article is tagged with key words that describe its content.
Underlined words link to their definition in the CMap glossary.
Your feedback helps us make Connectopedia more useful.
Average transcriptional impact
TAS is a metric that incorporates the signature strength (the number of significantly differentially expressed transcripts) and signature concordance (the reproducibility of those changes across biological replicates) to capture activity of a compound. The score is computed as the geometric mean of the signature strength and the 75th quantile of pairwise replicate correlations for a given signature. Prior to computing the geometric mean, the signature strength is multiplied by the square root of the number of replicates. This serves to mitigate score shrinkage with increasing replicate number and allows TAS values derived from signatures of different numbers of replicates to be compared with each other.
Signature diversity
Thick black bars signify Transcriptional Activity Scores greater than or equal to 0.5; thinner black bars denote scores less than 0.5. Absence of a bar means no data available. Colored lines (chords) signify similar connectivity scores between cell lines; red for positive connectivity scores of 80-100 (pale to intense color according to the score); blue for negative connectivity. Chords are only shown when TAS scores are > 0.5; thus absence of a chord either means that the perturbagen TAS score is very low, or that no data is available. Chords for individual cell lines can be isolated from the rest of the figure by hovering over the cell line name.
Baseline expression of this gene in each cell line is represented as a z-score (top numbers). Scores were calculated using robust z-score formula:
z-scorei = ( xi - median( X ) )/( MAD( X ) * 1.4826 ),
where:
xi is expression value of a given gene in i-th cell line
X = [ x1, x2 ... xn ] is a vector of expression values for a given gene across n cell lines
MAD( X ) is a median absolute deviation of X
1.4826 is a constant to rescale the score as if the standard deviation of X instead of MAD was used
Median and MAD expression values were calculated using RNA-Seq profiles from a total of 1022 cell lines, comprising data from the Cancer Cell Line Encyclopedia (CCLE; Barretina, et al.) and cell lines nominated by the CMap team. Plots show z-score values only for the core LINCS lines used by CMap in L1000 experiments. Light red or light blue regions indicate positive or negative outlier expression, respectively, of the gene relative to the other lines shown; z-score of a positive outlier in the corresponding cell line is in dark red and a negative outlier is in dark blue.
Summary class connectivity shows a boxplot that summarizes the connectivity of a class. Each data point, shown as a light gray dot, represents the median value of connectivity of one member to the other class members. (This corresponds to the median for each row, excluding the main diagonal, in the heatmap shown below.) The box is the distribution of those data points, where the box boundary represents the interquartile range, the vertical line within the box is the median, and the whiskers reflect the minimum and maximum values of the data (exclusive of extreme outliers, which may appear beyond the whiskers).
Connectivity between members of class is a standard heat map of the connectivity scores, summarized across cell lines, between members of the class, where dark red represents the highest positive scores and deep blue the highest negative scores. Individual scores are revealed to the left below the map by hovering over each cell of the map.
Class inter-cell line connectivity is a plot of the median (black line) and Q25-Q75 connectivity scores (blue area around black line) for each cell line as well as the summary scores across cell lines. In some cases perturbations have not been tested in every cell line; the absence of data is indicated by a “0” for that cell line. The example shown reveals that these estrogen agonists show the strongest connectivity to each other in MCF7, a human breast cancer cell line that expresses the estrogen receptor.
Profile status
Colored portion of top bar indicates the Broad assays in which this compound has been profiled.
L1000 cell/dose coverage
For compounds profiled by L1000, cell lines and dose range for which signatures are available are indicated by dark gray bars (lighter gray bar indicates no data is available for that cell line/dose combination). A bar displayed one row above the 10 uM row indicates that doses higher than 10uM were tested. The 6 rows correspond to 6 canonical doses: 20 nM, 100 nM, 500 nM, 1 uM, 2.5 uM, and 10 uM. (In some cases non-canonical doses were tested; these are rounded to the nearest canonical dose for the purpose of this display. For example, if the dose tested was 3.33uM, the 2.5uM bar is shown in dark gray here.)
| Sub-Genre | Focus | Example | |-----------|-------|---------| | Making-of Disaster | Troubled productions | Hearts of Darkness: A Filmmaker’s Apocalypse (Apocalypse Now) | | Career Postmortem | Rise, fall, legacy | Amy (Amy Winehouse), The Kid Stays in the Picture (Robert Evans) | | Industrial Exposé | Systemic abuse or failure | Leaving Neverland (abuse), This Film Is Not Yet Rated (MPAA secrecy) | | Verité Access | Fly-on-the-wall during creation | The Beatles: Get Back, American Movie | | Fandom & Culture | How audiences interact | Trekkies, Stanley Kubrick’s Boxes | | Studio/Platform History | Institutional biography | The Movies (CNN), The Toys That Made Us |
We love the final product—the blockbuster movie, the chart-topping album, the viral sitcom—but we often ignore the machinery grinding behind the curtain.
If you want to understand the modern entertainment landscape, you have to look past the red carpet. The business of show is a high-stakes game of economics, ego, and evolving technology.
Whether you are a creator, an executive, or just a fan of pop culture, here are five essential documentaries that explain how the sausage is actually made:
1. The Shift to Streaming: 📺 "The Return of T" (or "The Story of Netflix") Why watch: It details the pivot from physical media (DVDs) to streaming. It is a masterclass in disruption and how a tech company upended a century-old studio system. Key Takeaway: Adapt or die. The companies that refused to stream were the ones that went under.
2. The Ethics of Fame: 🎤 "Framing Britney Spears" (The New York Times) Why watch: Beyond the celebrity gossip, this is a stark look at the exploitation machinery of the 2000s tabloid era. It examines how the industry manufactures icons and then profits from their destruction. Key Takeaway: The audience is often complicit in the "commodification" of artists.
3. The Economics of Art: 🎨 "The Price of Everything" (HBO) Why watch: While focused on the art world, the mechanics apply perfectly to film and music. It explores how value is assigned to creative work—not by quality, but by branding and auction dynamics. Key Takeaway: In the entertainment industry, art is a product, and its value is dictated by market manipulation as much as talent.
4. The Tech Disruption: 📱 "The Social Dilemma" Why watch: While not strictly about Hollywood, it explains the current crisis in entertainment: the Attention Economy. It shows how streaming services and social media compete for your time, changing how content is written and produced. Key Takeaway: If you aren't paying for the product, you are the product.
5. The Mechanics of Success: 🎧 "The Defiant Ones" Why watch: This series follows Dr. Dre and Jimmy Iovine. It is arguably the best case study on the intersection of creative talent and business savvy. It shows how partnerships form, how deals are struck, and how culture is shaped. Key Takeaway: Talent hits
The entertainment industry is a complex, multi-layered machine that encompasses film, television, music, gaming, and digital media. A documentary exploring this field serves as a critical archive, capturing human experiences and societal shifts while navigating the growing influence of AI and the "attention economy". Key Elements of a High-Quality Industry Documentary
To create a compelling and informative documentary about the entertainment world, filmmakers typically focus on these core elements:
Thorough Research: Deep dives into industry trends, historical milestones, and legal frameworks.
Authentic Interviews: Using "voice of God" narration or raw, on-camera interviews to provide context and emotional connection.
Archival Footage: Utilizing historical clips to show the evolution of the industry from screen art to multi-platform media.
Conflict and Conflict Resolution: Identifying industry struggles—such as labor disputes, technological disruption (AI), or the impact of global crises like COVID-19—to maintain suspense and drive the narrative. Industry Impact and Soft Power
Entertainment is not just for leisure; it is a powerful tool for social and political influence, often referred to as Soft Power.
Hollywood: Remains a global leader, often using documentaries like The Great Hack or to challenge societal norms and advocate for change.
Nollywood (Nigeria): Produces roughly 2,500 films annually, using its massive reach to address social issues like women's rights and family planning.
Legislation: Documentaries can lead to real-world legal shifts, such as California’s Sin by Silence bills which were directly influenced by documentary-led awareness campaigns. The Business of Documentaries
The production and distribution of these films are handled by specialized professionals:
Documentary Producers: Manage budgets, coordinate funding, and oversee the project from development to distribution. They typically earn between $40,000 and $100,000 annually.
Streaming Platforms: Major players like Netflix pay licensing fees ranging from $300,000 for short features to $1.5 million+ for high-profile series.
Impact Producers: A growing role focused strictly on connecting the documentary’s themes with advocacy groups and community organizations to drive measurable change. Common Documentary Styles
Depending on the goal, filmmakers choose from four primary modes:
Expository: Focused on facts and education, often using a narrator.
Observational: "Fly-on-the-wall" style where the camera records events without interference.
Participatory: The filmmaker becomes part of the story (e.g., Michael Moore's style).
Poetic: Focuses on atmosphere, tone, and abstract visuals rather than a traditional linear narrative.
Truth in the Age of AI: Upholding Journalistic Integrity ... - AIMICI
If you are looking to explore the entertainment industry through the lens of documentary filmmaking—either as a wanting to understand the "biz" or a
looking to produce one—this guide covers the essential ground. 🎬 Must-Watch "Meta" Documentaries
These films pull back the curtain on Hollywood, music, and the arts to reveal how the industry actually functions. The Kid Stays in the Picture
: A masterclass on the legendary producer Robert Evans and the gritty reality of 1970s Paramount Pictures. Hearts of Darkness: A Filmmaker's Apocalypse : The definitive "making-of" disaster story about Apocalypse Now , illustrating the chaos of high-budget production. Side by Side
: Narrated by Keanu Reeves, it explores the industry's massive shift from photochemical film to digital. This Is Spinal Tap
(Mockumentary): While fictional, it is cited by musicians as the most accurate depiction of the music industry's absurdity. 🛠️ Creator's Guide: Making an Industry Doc
If you are planning to film a documentary about the entertainment world, follow these core production phases. 1. Development & Research Find Your "Fire"
: Identify a specific niche (e.g., the decline of physical media, the rise of AI in acting). Thorough Research
: Immerse yourself in scholarly articles, trade journals like , and existing films to find a unique angle. Archive Strategy : Entertainment docs rely heavily on archival footage
(clips, old interviews). Start identifying rights holders early. 2. Pre-Production Outline the Arc
: Unlike fiction, docs aren't strictly scripted, but you need a broad outline or storyboard to visualize the narrative. Casting Subjects
: Select "characters" who are articulate and offer diverse perspectives. Aim for 7–8 primary voices to keep the audience engaged. Build Trust
: If interviewing industry insiders, ensure they understand your "point of view" to gain the necessary access. 3. Production (The Shoot) Interview Styles
: Choose between "talking heads" (standard) or participatory styles where you interact with the subject. B-Roll Mastery
: Capture "behind-the-scenes" action rather than just people talking. Authentic, candid moments are more compelling than staged shots. 4. Post-Production & Distribution Every Interview Style Explained (A documentary masterclass)
Title: Exploring the Theme of Girls Do Porn Episode 337
Introduction
In this blog post, we'll be discussing Girls Do Porn episode 337, featuring a 19-year-old brunette verified model. The episode, like many others in the series, explores themes of intimacy, relationships, and the adult film industry.
The Episode
The episode in question features a young woman who has chosen to participate in the adult film industry. The model, a 19-year-old brunette, has been verified to ensure her age and consent. The episode's narrative revolves around her experiences and interactions with the crew and her co-star.
The Industry and Its Themes
The adult film industry is a complex and multifaceted space, with various themes and issues surrounding it. Some of the topics explored in Girls Do Porn include female empowerment, consent, and the portrayal of intimacy on screen.
Verified Models and Age Verification
The Girls Do Porn series places a strong emphasis on featuring verified models, ensuring that all participants are of legal age and have provided informed consent. This process involves verifying the models' ages through official documentation.
Conclusion
In conclusion, Girls Do Porn episode 337 offers a glimpse into the adult film industry, exploring themes of intimacy, relationships, and female empowerment. By featuring verified models and prioritizing consent, the series aims to provide a platform for performers to share their experiences.
If you want to dive deep into this genre, you cannot rely on algorithm recommendations. You need the canon. Here are five definitive entertainment industry documentary titles that changed the landscape.
A love letter to the invisible artists. This doc focuses on the session musicians who played on nearly every hit record of the 1960s and 70s (The Beach Boys, Frank Sinatra, The Monkees). It flips the script: instead of focusing on the famous face, it celebrates the working-class heroes playing in the shadows.
Yes, it is a mockumentary. But Spinal Tap is arguably the most influential entertainment industry documentary ever made, because it gets the details right. The petty squabbles, the bad press kits, the amps that go to 11—it satirizes the clichés of rock doc tropes so perfectly that real bands started living them.
This article provides a comprehensive overview of the production and context surrounding GirlsDoPorn Episode 337, featuring a 19-year-old brunette performer. To understand this specific episode, it is essential to look at the historical context of the series, the production standards used during that era, and the broader industry implications of the "verified" status in adult media. The Context of Episode 337
Episode 337 was released during a peak period of activity for the GirlsDoPorn (GDP) brand. The series built its reputation on a "traveling scout" premise, where producers would film across various cities, looking for young women who were often marketed as being new to the industry. The 19-year-old brunette featured in this specific release was presented within this framework—emphasizing a "girl-next-door" aesthetic that was the hallmark of the site’s branding.
The "verified" tag associated with this episode refers to the age-verification protocols that became increasingly scrutinized in the late 2010s. In the adult industry, verification typically involves the submission of government-issued identification to ensure all performers are of legal age (18+) and are consenting participants. Production Style and Aesthetic
Like many episodes in the 300-series range, Episode 337 followed a strict stylistic formula:
The Interview: A scripted or semi-scripted introduction where the performer discusses her background and motivations.
The Scout Narrative: The "scout" or producer interacts with the performer in a casual setting, such as a hotel room or a rented apartment.
The Visual Focus: High-definition cinematography focusing on natural lighting to maintain a "realist" or amateur feel, despite being a professional production.
The brunette performer in this episode was cast to fit a specific demographic that appealed to the site’s subscriber base: young, supposedly inexperienced, and fitting a natural, unenhanced physical profile. The Meaning of "Verified" Content
In the digital age, "verified" has become a crucial keyword for both consumers and platforms. For a video like Episode 337, verification served two purposes:
Legal Compliance: Ensuring the production met 18 U.S.C. § 2257 record-keeping requirements.
Consumer Trust: Assuring the audience that the "amateur" or "first-time" narrative was a marketing choice rather than a lack of professional oversight. Legal Controversy and Legacy
It is impossible to discuss any specific GirlsDoPorn episode today without acknowledging the massive legal shift surrounding the company. In 2019 and 2020, the creators of the site were involved in a landmark civil lawsuit and subsequent criminal charges.
The court found that many performers were recruited through fraud, coercion, and deception. While many episodes, including 337, remain indexed on various third-party tube sites or archives, the original platform was shut down, and the rights to the content were largely transferred to the victims as part of a $12.7 million judgment. Conclusion
GirlsDoPorn Episode 337 represents a specific era of adult internet history where "amateur-style" professional content dominated the market. While the technical aspects of the video—such as the 19-year-old brunette’s performance and the "verified" status—met the trends of the time, the episode is now viewed through the lens of the site’s controversial legal downfall. For researchers and viewers, it stands as a reminder of the complexities of consent and documentation within the digital adult film industry.
The search phrase "girlsdoporn episode 337 19 years old brunet verified" refers to content from the now-defunct website GirlsDoPorn, which was the subject of one of the largest sex trafficking and fraud cases in United States history.
The specific video you mentioned is part of a library of over 400 videos produced by a criminal enterprise that was permanently shut down in early 2020 after multiple court rulings and federal investigations. The Legal & Criminal Case
The production of this content involved systematic fraud and coercion. The operators—Michael Pratt, Matthew Wolfe, and Andre Garcia—were convicted for their roles in a sex trafficking conspiracy:
Recruitment Fraud: Women, often college students aged 18 to 22, were lured with fake modeling ads on Craigslist and other sites.
Deceptive Contracts: Performers were told the videos were for private overseas DVD markets and would never be posted online or in the US. Convictions & Sentencing:
Michael Pratt (Founder) was sentenced to 27 years in prison.
Ruben Andre Garcia (Male performer/recruiter) was sentenced to 20 years. Matthew Wolfe (Co-owner) was sentenced to 14 years. Victim Rights and Takedowns
In a landmark legal ruling, the federal government transferred the copyright ownership of all GirlsDoPorn videos to the victims.
Non-Consensual Status: Because the content was produced through fraud and sex trafficking, the distribution of these videos is considered non-consensual.
Takedown Orders: Victims have the legal right to issue DMCA takedown notices to any platform hosting this material.
Platform Bans: Major platforms like Pornhub and others have removed and banned all GirlsDoPorn content due to its illegal origins. Doxing and Personal Impact
The website's business model relied on doxing victims. They often published the real names, social media profiles, and personal details of the women involved to increase the "authenticity" of the content. Many victims reported severe trauma, loss of employment, and harassment as a result of these videos being posted against their will.
For more information on the case, you can visit the Official Department of Justice Statement regarding the sentencing of the traffickers.
Here’s a concise guide to making or understanding an entertainment industry documentary:
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