Graded Assignment -3 (May Term 2023):- Effective Visualization of Data

[Jimmi-Kr]

After World War II, antibiotics were considered "wonder drugs", since they were easy cures for what had been intractable ailments. To learn which drug worked most effectively for which bacterial infection, the performance of the three most popular antibiotics on 16 bacteria was gathered. The values in the table represent the minimum inhibitory concentration (MIC), a measure of the effectiveness of the antibiotic, which represents the concentration of antibiotic required to prevent growth in vitro. The reaction of the bacteria to Gram staining is described by the covariate “gram staining”. Bacteria that are stained dark blue or violet are Gram-positive. Otherwise, they are Gram-negative.

Your task is to design a chart that you believe effectively communicates the data and provide a short write-up (no more than 4 paragraphs) describing your design.

• Your chart image should be interpretable without recourse to your short write-up.
• Do not forget to include a title, axis labels, or legends as needed!

As different visualizations can emphasize different aspects of a data set, you should document what aspects of the data you are attempting to most effectively communicate. Just as important, also note which aspects of the data might be obscured or downplayed due to your visualization design.

In your write-up, you should provide a rigorous rationale for your design decisions. Document the visual encodings you used and why they are appropriate for the data. These decisions include the choice of visualization type, size, color, scale, and other visual elements, as well as the use of sorting or other data transformations. How do these decisions facilitate effective communication?

Here is the link for the dataset: Butin_antibiotic_data.xlsx

[sayan10rakshit]

Sayan Rakshit 21F1002696

Link to the visualization

Brief Information

Antibiotics are substances that can kill or inhibit the growth of bacteria. They are used to treat bacterial infections and are one of the most important tools in modern medicine. However, not all antibiotics work against all bacteria. The effectiveness of an antibiotic depends on the type of bacteria it is being used against.

The Minimum Inhibitory Concentration (MIC) is a measure of how effective an antibiotic is against a particular type of bacteria. It is the lowest concentration of the antibiotic that can prevent the growth of the bacteria. The lower the MIC, the more effective the antibiotic is against that type of bacteria.

Bacteria can be classified as either gram-positive or gram-negative based on the structure of their cell walls. Gram-positive bacteria have thick cell walls made up of a substance called peptidoglycan. When subjected to a laboratory test called a Gram stain, these bacteria retain a purple dye and appear purple under a microscope. Gram-negative bacteria, on the other hand, have thinner cell walls and an additional outer membrane. They do not retain the purple dye during a Gram stain and appear pink or red under a microscope.

Aim of the Visualization

• Finding out the effectiveness of antibiotics across different bacterium
• Figuring out which antibiotic is the most effective for a given bacterium
• Looking at any emerging patterns coming out after visualizing the data

Design Decision

• Three different graphs (one for each antibiotic) are constructed on top of each other for ease of comparison
• Y-Axis (of three graphs): $log{_e}{(MIC)}$; (All the graphs have same scale for the ease of comparison)
• X-Axis: Bacteria Names (We have many Bacteria in comparison to Antibiotics in our data, so this setting will be appropriate)
• Different color coding is used for gram +ve and -ve bacteria
• This kind of format helps us to visually compare different antibiotics against all bacteria

Scale, Visual Encoding and Color

• All MIC values were normalized by using natural log ( $log{_e}{(MIC)}$ ) to bound their values to a smaller range
• Different color coding is used for Gram +ve/-ve bacteria
• The best antibiotic for a given Bacterium is annotated using a green tick mark
• Appropriate labels are given for the visualization
• A readable bold font is used throughout the visualization

Noteworthy observations

• Penicillin works exceptionally well for gram positive bacteria rather than gram negative ones
• Overall Neomycin dominates by achieving lowest MIC for majority cases of Bacteria
• Penicillin achieves lowest MIC and is ahead by a huge margin for Streptococcus hemolyticus and Streptococcus viridans

Tool/s Used

The entire visualization was made using Tableau Public

[anant7k]

Comparing Effectiveness of Antibiotics using Minimum Inhibitory Concentration [MIC]

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Anant Kumar 21f1000683

About Minimum Inhibitory Concentration, Research Significance & Industrial Applications

Initially, I went through some research papers, videos of industrial experts trying to understand how MIC is used in Research and development processes, industries. Minimum Inhibitory Concentration (MIC) holds significant value in both research and industrial applications, particularly in the study of antibiotic effectiveness against bacterial growth in vitro. Some key applications in industries and research include:

• Comparative Drug Effectiveness: MIC is employed by research teams to assess the efficacy of novel drug combinations, enabling them to compare and identify the most potent treatments against bacterial infections.

• Drug Concentration Regulation: Microbiologists in various industries utilize MIC data to regulate the appropriate concentration of antibacterial agents in pharmaceutical products, ensuring optimal effectiveness and safety.

• Informed Antibiotic Prescription: MIC data is used by microbiologists and doctors as a crucial reference while prescribing antibiotics to patients, helping them select the most suitable and effective treatment for specific bacterial infections.

The understanding and application of MIC play a pivotal role in advancing research and development efforts within the pharmaceutical and medical fields, leading to improved antibiotic formulations and better healthcare outcomes.

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Aim/Target Audience for this visualization

The aim of this visualization is to cater to the target audience, primarily consisting of research and development teams as well as microbiologists. The main objective is to present a simple and easily understandable chart that facilitates the reading of Minimum Inhibitory Concentration (MIC) values. This chart will allow the audience to compare the relative effectiveness of different antibiotics against various bacterial strains, enhancing their decision-making process in drug development and prescription practices.

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Design Decisions

Based on the key uses of Minimum Inhibitory Concentration (MIC) and its relevance in various applications, the following design decisions were made for the visualization:

1. Data Scale Preservation: To ensure compatibility with the industrial standard, the MIC values are presented on the same scale as provided, such as ug/ml or ppm. This maintains the integrity of the data and facilitates direct usage by industrial experts, researchers, and microbiologists.

2. Practical Applicability: Recognizing that MIC values are directly employed in prescription practices and the comparison of drug effectiveness, the visualization focuses on presenting the data in a manner that is readily applicable and relevant to these contexts.

3. Font Size and Clarity: To enhance readability and user experience, the fonts used in the visualization are large and clearly visible. This decision ensures that even intricate MIC values can be easily interpreted by the target audience without any ambiguity.

4. Radial Charts for Relative Effectiveness: To simplify the understanding of relative effectiveness among different antibiotics against various bacterial strains, the visualization utilizes radial charts. These charts enable an intuitive representation of comparative data, allowing the audience to quickly grasp the variations in MIC values and make informed decisions.

The combination of these design decisions aims to create a user-friendly, informative, and practical visualization of MIC values, catering to the needs of research and development teams, microbiologists, and industrial experts in the pharmaceutical and medical domains.

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Scaling, Visual Encoding and Coloring

The visualization design incorporates the following choices related to scaling, visual encoding, and coloring:

Scaling:

1. Data Scale Preservation: The MIC values are retained in their original units (e.g., ug/ml or ppm) to align with the industrial standard, ensuring seamless integration and direct usability by the target audience, which includes research and development teams and microbiologists.

Visual Encodings and Color:

1. Radial Charts for Relative MIC Values: Radial charts are employed to compare the relative MIC values among three different drugs. The visualization smoothes these values, representing them as significant percentage differences, allowing for easier interpretation and comprehension within a large ranged dataset.

2. Color Scheme: The radial charts use a carefully chosen color scheme to visually encode the effectiveness of antibiotics. The most effective antibiotics are represented in GREEN, the least effective in RED, and those with intermediate effectiveness in BLUE. This color differentiation aids in quickly identifying the varying levels of effectiveness among the drugs.

3. Large and Clear Fonts: MIC values are prominently displayed with large fonts, ensuring optimal visibility and clarity. This decision makes it easier for the target audience to read and comprehend the MIC values without any ambiguity.

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Short coverings/ Downplays

1. Non-Proportional Radial Charts: The decision to represent the effectiveness of MIC values using non-proportional radial charts may lead to confusion for some viewers. The lack of scale in these charts can make it challenging to accurately observe and compare exact MIC values, potentially affecting data interpretation.

2. Potential Misinterpretation: Viewers accustomed to precise visual scaling may find it difficult to discern the actual magnitude of differences between MIC values in the radial charts. This could lead to misinterpretation of the relative effectiveness of antibiotics, hindering their ability to make accurate comparisons.

3. Consideration of Alternatives: It might be beneficial to explore alternative chart types that retain proportional scaling while still effectively conveying the comparative MIC values. Such alternatives could provide a clearer and more accurate representation of the data, reducing the risk of misinterpretation.

4. Clarifying Chart Design: To address potential confusion, additional visual cues or annotations could be included in the radial charts to emphasize the non-scale nature of the visualization. Ensuring clarity in the chart design can help viewers better understand and interpret the presented MIC values.

   Final Visualization

   

[Prahlad19]

Visualizing Effectiveness of several Antibiotics using Minimum Inhibitory Concentration (MIC)

Name: Prahlad Singhania Roll no: 21f1006059

Introduction: Antibiotics are medicines that fight bacterial infections in people and animals. They work by killing the bacteria or by making it hard for the bacteria to grow and multiply. Some of the antibiotics are penicillin, streptomycin, neomycin etc. In microbiology and bacteriology, Gram stain is a method of staining used to classify bacterial species into two large groups which are gram-positive bacteria and gram-negative bacteria. Gram-positive cells have a thick layer of peptidoglycan in the cell wall whereas Gram-negative cells have a thinner peptidoglycan layer. The values in the visualization represent the minimum inhibitory concentration (MIC), a measure of the effectiveness of the antibiotic, which represents the concentration of antibiotic required to prevent growth in vitro. It is preferably to take antibiotic for a particular bacterial infection where the MIC is least and therefore most effective.

Objectives of Visualization: The main objective of this Visualization is to visualize the most efficient antibiotic for different kinds of bacterial infection exist in this dataset. Data augmentation is a key aspect for this kinds of scenario. Therefore making a heat map with standard colour encoding to determine the efficient antibiotic for a bacteria with their MIC is one of the most efficient way to visualize this type of scenario. It is purely data driven visualization.

Design Decision: I have created several different visualization before the stated one, but I felt that the data is not captured very well in those case. Some cases visualization were looking misleading. So I focus on Heat map as data is clearly depicted in these kind of visualization. As our objective was to find the efficient antibiotic for a bacterial infection, so this visualization makes it very easy for someone to identify the antibiotic needed for a particular bacterium.

Visual Encodings: Standardized & Traditional colour encoding is populated. Positive gram staining results in crystal violet whereas negative gram staining results in stained pink. Scaling is performed on different range of data values and the colour code on that scale depicts the efficiency of antibiotic for a particular bacteria. Scaling color code is also standardized that makes it easy to interpret for anyone by looking at the visualization itself. Though data range is very wide but I have avoided data transformation because I want actual data in the visualization so that it won’t mislead anyone. The textual representation for bacterial infection and antibiotic is segmented in lower case and italics as per the standardized format.

Key findings: neomycin is the most efficient antibiotic in the dataset. It is preferred over others in exactly ten different bacterial infection. penicillin is more effective in case of brucella anthracis and diplococcus pneumoniae bacterial infection whereas streptomycin is preferred in case of aerobacter aerogenes and proteus vulgaris. proteus vulgaris is the only bacterial infection in the dataset in which both streptomycin and neomycin are equally effective.

Visualization:

[sejalanandIITM]

Communicating the Effectiveness of Antibiotics on Bacterial Infections

Name: Sejal Anand Roll No: 21f1002620

Purpose

My objective was to create a visualization that effectively communicates the performance of three popular antibiotics (Penicillin, Streptomycin, and Neomycin) on 16 different bacteria. The dataset contains minimum inhibitory concentration (MIC) values, representing the effectiveness of each antibiotic against specific bacterial infections. To achieve this, I designed a scatterplot using Tableau, where each bacteria is represented as a data point, and the MIC values of the three antibiotics are displayed on the respective axes. The color and shape of the data points were used to indicate the type of antibiotic and the Gram staining of each bacteria, respectively. Additionally, I included a table with color-coded bacteria rows to further support quick interpretation.

Design Decision and Rationale

I chose a scatterplot as it is suitable for comparing multiple data points across different categories. Each bacteria is represented as a data point to show its response to different antibiotics. Using log transformation on the MIC values ensures that data with a wide range of concentrations can be visualized effectively on the same chart, preventing large differences in values from dominating the visualization.

Visual Encodings, Scale and Color

I used position encoding for the data points, placing them on the chart according to their MIC values for each antibiotic. This allows viewers to compare the effectiveness of different antibiotics on the same bacteria easily. Additionally, colour encoding was applied to represent the three antibiotics, making it effortless to identify them. Shape encoding was employed to differentiate between Gram-positive and Gram-negative bacteria, further enhancing the clarity of the visualization.

Using log transformation on the MIC values ensures that the data is appropriately scaled on the chart, allowing viewers to perceive differences accurately. This transformation also prevents extreme values from skewing the visualization and enables the visualization of a wide range of concentrations on the same chart.

Shortcomings

While the scatterplot effectively communicates the relative effectiveness of antibiotics on different bacteria, it may not show the full extent of the variations in MIC values. Extreme outliers may not be as apparent due to the log transformation, potentially downplaying their significance. Additionally, if there are overlapping data points, it could be challenging to distinguish individual data points accurately. In such cases, using interaction techniques or tooltips in Tableau could help reveal specific data points.

Tool used Tableau

[dhruvsanan]

https://public.tableau.com/app/profile/dhruv1897/viz/AntibioticPerformance/Sheet1?publish=yes

DHRUV SANAN 21F1004102

Description

The use of a bar chart makes it easy to see at a glance which antibiotics are most effective against each type of bacteria. The lower the bar, the lower the MIC, which means that less of the antibiotic is needed to kill the bacteria. The use of different colors for Gram-positive and Gram-negative bacteria helps to distinguish between the two types of bacteria. This is important because the antibiotics work differently on Gram-positive and Gram-negative bacteria. The title of the chart, "Comparing Performance of 3 Antibiotics with MIC", clearly states the purpose of the chart. The axis labels, "Antibiotic" and "Bacteria", are also clear and easy to understand. The scale of the y-axis is log base 10, which makes it easier to see the differences in MIC between the antibiotics. Different color coding is used for Gram +ve/-ve bacteria The best antibiotic for a given Bacterium is annotated using a green tick mark

Critic of my visualisation

Some aspects of the data that might be obscured or downplayed due to the visualization design include the following: The exact MIC values for each antibiotic and bacteria are not shown. This information could be useful for some users, but it would make the chart more cluttered. The data for one bacteria, Staphylococcus aureus, is missing. This could be due to a number of factors, such as the fact that Staphylococcus aureus is a common bacteria and the data for it is not as meaningful as the data for other bacteria. Proteus vulgaris has 2 best antibiotic but one is marginally better if we see actual MIC.

Here are some suggestions for how to improve the chart:

Add a table below the chart that shows the exact MIC values for each antibiotic and bacteria. Provide a link to a website where users can find more information about antibiotic resistance Users might get confused with negative MIC value with Gram Negative or vice versa

Overview

Overall, the chart created is an effective way to communicate the data on antibiotic susceptibility. The use of a heatmap, different colors, and a clear title and axis labels make the data easy to understand. The logarithmic scale of the y-axis helps to highlight the differences in MIC between the antibiotics.

[iSarthakGautam]

Minimum inhibitory concentration Visualisation

• Name: Sarthak Gautam
• Roll No: 21f1000864

Introduction

Bacteria, microscopic single-celled organisms, play a significant role in both beneficial and harmful interactions within our environment. In the medical field, understanding bacterial behaviour and identifying their susceptibility to antibiotics are essential for effectively combating infections. Two crucial aspects that aid in this understanding are Minimum Inhibitory Concentration (MIC) and Gram staining.

Minimum Inhibitory Concentration (MIC):

The Minimum Inhibitory Concentration (MIC) is a measure of the effectiveness of antibiotics against specific bacterial strains. It represents the lowest concentration of an antimicrobial agent (such as an antibiotic) required to prevent visible growth of the bacteria in a controlled laboratory environment (in vitro). Determining the MIC value helps clinicians gauge the potency of an antibiotic against a particular bacterial infection. Lower MIC values indicate that the antibiotic is highly effective in inhibiting bacterial growth at lower concentrations, while higher MIC values may indicate reduced effectiveness and the need for higher antibiotic concentrations to achieve the desired effect.

Gram Staining:

Gram staining is a fundamental microbiological technique used to categorise bacteria into two main groups: Gram-positive and Gram-negative. This staining method is based on differences in the bacterial cell wall structure. Bacteria that retain the stain, appearing purple or dark blue, are classified as Gram-positive. Conversely, bacteria that do not retain the stain, appearing pink or red, are categorised as Gram-negative. This classification is important in determining appropriate antibiotics for treatment, as Gram-positive and Gram-negative bacteria may exhibit varying levels of susceptibility to different antimicrobial agents.

Objectives

The primary goal of this visualisation is to compare the minimum inhibitory concentration (MIC) of each antibiotic for different bacterial infections and to identify patterns and differences in their effectiveness. The secondary goal is to explore how the reaction to Gram staining (Gram-positive or Gram-negative) might influence antibiotic effectiveness.

Design Decision

To achieve the goals, i decided to go with a interactive bubble chart. Apart from interactive visualisation, i tried to get a tabular form visualisation that is considered to be main visualisation for this graded activity.

link: https://public.flourish.studio/visualisation/14522699/ This decision is made to effectively communicate the antibiotic effectiveness for each bacterial infection and provide viewers with an interactive and visually engaging experience.

Data Comparison and Exploration:

The interactive bubble chart table allows viewers to compare the MIC values of multiple antibiotics for each bacterial infection side by side. This aids in identifying patterns, differences, and similarities in antibiotic effectiveness across different bacteria. The interactive nature enables users to explore the data by hovering over the bubbles for detailed MIC values, supporting a deeper understanding of antibiotic efficacy for specific infections.

Intuitive Representation:

Bubbles are a familiar and intuitive way to represent quantitative data, with the size of each bubble proportional to the MIC value. This visual encoding makes it easy for viewers to grasp the relative potency of antibiotics for each bacterial infection.

Organization and Clarity:

The table format ensures a structured and organised presentation of the data, with each bacterial infection having its dedicated row. This format minimises clutter and allows viewers to focus on individual bacteria and their corresponding antibiotic responses. By maintaining consistency across all bacterial infections, viewers can quickly comprehend the layout and navigate through the information efficiently.

Aesthetics and Engagement:

The choice of light green and light red backgrounds complements the bubble chart and creates an aesthetically pleasing and visually harmonious presentation. The interactive nature and color-coding encourage viewer engagement, fostering curiosity and interest in exploring the data further.

Consistency:

Standardising the visualisation format across different bacteria ensures consistency and ease of navigation for viewers. They can focus on interpreting the data without having to adapt to different visualisation styles for each bacteria.

Visualisation

Visual Encodings

In data visualisation, visual encodings are essential in effectively conveying information through visual representations. Carefully chosen colour semantics play a crucial role, as they can represent various motives, such as positive or negative trends. Additionally, the background colour is thoughtfully selected to ensure it complements the colors of the interactive bubbles, providing a harmonious and visually pleasing experience.

To enhance clarity and ease of interpretation, the bacteria are presented in alphabetically sorted order. This deliberate arrangement allows viewers to quickly locate specific bacterial infections and makes it simpler to compare antibiotic responses across different bacteria.

To aid viewers in understanding the visual encoding and its significance, appropriate legends are thoughtfully included. These legends provide clear explanations of color representations, bacterial names, and antibiotic MIC values. With the support of these legends, viewers can easily interpret the interactive bubble chart and gain valuable insights into antibiotic effectiveness for various bacterial infections.

shortcomings and Improvements:

• Tabular Visualisation Size: The current tabular visualisation appears too large, which may hinder the viewers' ability to grasp the data at a glance. To address this, transforming the tabular layout into a grid-like structure might help. By arranging the interactive bubble chart in a more compact grid format, viewers can easily scan through the data, making the visualisation more user-friendly and visually appealing.

• Interactive Visualisation Limitations: The limitations of the interactive visualisation tool used (Flourish) prevented the direct addition of filters using rows. While manual work was done to incorporate Gram staining encoding (colouring red and green for Gram-negative and Gram-positive, respectively), this process can be time-consuming and prone to human error. To improve this, consider exploring other visualisation tools or custom development options that allow more flexible filtering and seamless integration of the Gram staining encoding.

• Data Interactivity and Exploration: Enhance the interactive features of the visualisation to empower viewers with more control and exploration capabilities. Implementing interactive filters and zoom functionality will enable users to focus on specific subsets of the data and zoom in for more detailed views. This fosters a more engaging and insightful experience for viewers.

[deep87we]

Name:Deepanshu Mahajan Roll no:21f1006962

Title: Effectiveness of Antibiotics on Bacterial Infections - Minimum Inhibitory Concentration (MIC) Heatmap

Visualization Type: Heatmap

Rationale: To effectively communicate the data on antibiotic effectiveness against bacterial infections, I chose a heatmap as the visualization type. Heatmaps are ideal for displaying tabular data with colors, making it easy to identify patterns and trends. The heatmap uses color gradients to represent the MIC values, where higher MIC values are displayed with warmer colors (e.g., red) and lower MIC values with cooler colors (e.g., blue). This color encoding facilitates quick comprehension of the relative effectiveness of each antibiotic against different bacteria.

Visual Encodings:

Color: The color encoding accurately represents the MIC values, allowing viewers to distinguish between bacteria with different levels of antibiotic resistance. The warm colors draw attention to higher MIC values, indicating potential challenges in treating bacterial infections, while cooler colors highlight effective antibiotics. Annotating Data: Adding numeric annotations inside each cell enables precise identification of MIC values, further enhancing data comprehension.

Design Decisions:

Sorting: I have not sorted the bacteria or antibiotics to maintain their original order in the dataset. This decision ensures that the heatmap presents the data as gathered, making it easier for viewers to refer back to the original dataset if needed. Title and Axis Labels: A clear and informative title is included to convey the purpose of the visualization. The x-axis label identifies the antibiotics, and the y-axis label represents the bacteria. These labels help viewers understand the context and relationships within the heatmap. Color Palette: I used the 'coolwarm' color palette for the heatmap. This choice allows for easy differentiation between higher and lower MIC values while maintaining an aesthetically pleasing appearance. Data Transformations:

Gram Staining: To handle the qualitative data on Gram staining (positive/negative), I transformed it into numerical values. Bacteria with positive Gram staining are represented with '1', and bacteria with negative Gram staining are represented with '0'. This transformation enables the inclusion of Gram staining information in the heatmap. Communication: The heatmap effectively communicates the minimum inhibitory concentration (MIC) values of antibiotics against various bacterial infections. The color gradient quickly draws attention to antibiotics that may be less effective against certain bacteria, aiding in identifying potential antibiotic-resistant strains. Viewers can easily compare antibiotic effectiveness across bacterial species and identify patterns of susceptibility or resistance. Additionally, the inclusion of Gram staining information allows for a comprehensive view of how staining type may impact antibiotic efficacy.

Limitations: While the heatmap provides a clear overview of antibiotic effectiveness, it may not provide detailed insights into specific MIC values, especially if there are many data points to compare. In such cases, a detailed table or additional interactive elements could be beneficial. Additionally, if the dataset contains a large number of bacteria and antibiotics, the visualization might become crowded and less interpretable. However, this heatmap is designed to present a concise and effective summary of the antibiotic effectiveness data, making it an excellent tool for initial data exploration and identification of key trends.

[savindraiitm]

Antibiotic Performance Visualization for 16 Bacterial Infections

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Name: Savindra Singh Shekhawat Roll No: 21f1003973

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Purpose

The primary goal of this visualization was to effectively present the performance of three widely used antibiotics (Penicillin, Streptomycin, and Neomycin) against 16 different bacterial infections. The dataset consists of minimum inhibitory concentration (MIC) values, indicating the efficacy of each antibiotic against specific bacteria.

To achieve this, I developed a visualization using R, where each bacteria is depicted as a slice on the chart. Within each slice, the MIC values of the three antibiotics are displayed. The color and shape of the data points were utilized to signify the type of antibiotic and the Gram staining of each bacteria, respectively.

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Design Choice

I opted for this particular plot as it allows for easy comparison of multiple data points across distinct categories. By representing each bacterium as a data point, we can observe its response to different antibiotics effectively. Additionally, the size of the bars provides insight into the effectiveness of each drug.

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Visual Encodings, Scale and Color

In this visualization, the following color scheme was utilized to represent the antibiotics:

Penicillin: Blue Streptomycin: Green Neomycin: Orange Blue represents health, cleanliness, and calmness, symbolizing the soothing and healing properties of Penicillin. Green, associated with nature and growth, represents the origin of Streptomycin from soil bacteria and its ability to combat infections. Orange, symbolizing energy and potency, represents the effectiveness of Neomycin in treating infections.

Additionally, the background colors for the bacterial groups were chosen as follows:

Gram-Positive Bacteria: Light Purple/Lavender Gram-Negative Bacteria: Light Green Light purple offers a gentle and calming background, complementing the violet stain used in Gram staining for Gram-positive bacteria. Light green maintains visual clarity and complements the reddish-pink counterstain used in Gram staining for Gram-negative bacteria.

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Shortcomings

While this visualization conveys information effectively, one potential shortcoming could be the potential misinterpretation of the quantity required, as the size of bars is used for representation. Apart from that, the visualization serves its purpose well in presenting the antibiotic performance data for various bacterial infections.

[Varun-Sood-IIT]

Comparing Effectiveness of 3 Famous Drugs Against Eachother

Varun Sood (21F1003382)

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Brief Information:-

-> The MIC:

• It is the min amount of drug to stop the spread of the bacteria
• The lower the MIC is the more effective the drug is considered to be.
  • The best way to see MIC is to put 3 drugs against the same bacteria strain

    -> The Gram-Staining:

• It is a nominal scale variable where the order does not matter
• Both the Gram-Positive & Gram-Negative have their Ups & Downs

  • The best way to see Gram is to group them based on Gram category

    Visualization Goals:-

• Determining the a drug's efficiency across multiple bacteria strains and their types
• Comparing 3 of the most famous drugs against each other for the same bacteria strain

• Verifying if the type of bacteria strain (Gram Positive/Negative) affects drug efficiency or not

  Designing Logic Used:-

• Since our aim is to compare one drug against another, the grouped bar chart is the best fit for easier viewing
• To show the groups as divided into Gram Positive and Gram Negative, a transparent gradient served the best purpose
• Colors of the graph bars had to be chosen such that they were distinctively visible against the transparent hue of Gram-Stain

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  Drawable Observations:-

• Penicillin, apparently the most well-known drug in history, does not not perform well compared to Neomycin
  • Penicillin performs even poorer against Gram-Negative bacteria stains
• Streptomycin acts as a close second against most of the bacterial stains when compared to Neomycin
  • Streptomycin offers consistent performance in both strain types
• Neomycin wins amongst the three drugs for most bacterial stains by a considerable margin

  • Neomycin is a best choice against Gram-Negative stains

    The Bottom Line Verdict:-

• Penicillin works good against Gram_Positive bacterial stains
• Streptomycin is effective against both types of bacterial stains
• Neomycin is the best against the Gram_Negative bacterial stains

[21f1004666]

Logarithmic bar chart visualization for comparison of the performance of various antibiotics on bacteria.

Name: Andiboyina Mourya Chakradhar Nagesh Roll No.: 21f1004666 Source: Notebook Link

Note: Due to the size of the chart, it is recommended to open the image in a new tab.

Introduction:

Single-celled organisms like bacteria are known to be some of the deadliest organisms known to mankind. While some of the species of bacteria play a vital role in our human body, bacteria that enter the human body through external means and cause diseases are considered a threat to humanity. To fight these bacteria, many scientists started studying various chemical and biological properties of these bacteria and develop various antibiotics to fight these organisms. In the data, the bacteria are classified into two types based on the Gram-Staining method.

Gram-Staining

Gram-Staining is a method that can classify bacteria into two major types: Gram-Positive Bacteria and Gram-Negative Bacteria. Gram Positive bacteria have a thick layer of peptidoglycan (a cell wall) which gives a violet stain, whereas the Gram-Negative Bacteria have thinner peptidoglycan. Gram-Negative Bacteria have a cell membrane (not to be confused with the cell wall) that protects the inner parts of bacteria.

Minimum Inhibitory Concentration (MIC)

MIC is the minimum concentration of drug that prevents the growth of bacteria. It is often expressed in micrograms per milliliter (μg/mL) or milligrams per liter (mg/L). The lower the MIC, the better the antibiotic works against the respective bacteria.

Goals and Purpose

• Effectively communicate through the chart to compare the 3 most popular antibiotics and determine the best possible antibiotic to use against a certain type of bacteria.
• Make inferences through the chart like the performance of each antibiotic on a certain type of bacteria.

Design choices

Type of Visualization

• I've used the Logarithmic bar chart to make comparisons between the three antibiotics.
• The visualization is split into two subplots based on the type of bacteria.
• On the X-axis we have the names of various bacteria and on the Y-axis we have MIC values that were scaled on the logarithmic scale based on multiples of 10.
• On the bars, the values of MIC are written to show the actual value of MIC

Colors

Use of the Viridis colors(the midpoint and the extreme ends of the Viridis scale) to facilitate color-blind people to visually distinguish the colors. The same color is used for the same antibiotic to avoid any confusion.

• Penicillin: #fde725 (Yellow)
• Streptomycin: #21918c (Greenish Cyan)
• Neomycin: #440154 (Dark Purple)

Inference

• Due to the high values of MIC in the gram-negative chart, it seems that penicillin is less effective against gram-negative bacteria. But it is highly effective against gram-positive bacteria.
• Neomycin performs really well on most bacteria. So it can be used as a general preference as an antibiotic.
• Streptomycin is fairly consistent across most bacteria.

Tools Used

• Google Colab
• Plotly Express as plotting library.

[Khushiin]

Name- Khushee A Namdeo Roll number- 21f3001500

(Notebook link: https://colab.research.google.com/drive/13pz-kPkgW9CxKgMuEojMzPlHUx9W7RaM?usp=sharing)

AIM OF VIZUALIZATION: The purpose of this visualization is to display the minimum inhibitory concentrations (MIC) of three antibiotics (Penicillin, Streptomycin, and Neomycin) on 16 bacteria that are grouped by Gram staining. Gram staining can be used to compare antibiotic effectiveness against various bacteria and spot trends. The grouped bar chart with a logarithmic scale makes it simple to compare different antibiotics and shows how susceptible different bacteria are to various antibiotics.

DESIGN DECISIONS: Grouped Bar Chart: The comparison of three antibiotics on 16 bacteria, arranged by Gram staining, can be seen clearly by using a grouped bar chart. To make it simple to compare antibiotic efficacy within each group, each bacterium's bars are grouped based on whether they have positive or negative Gram staining. In order to accommodate the wide range of MIC values, the y-axis is set to a logarithmic scale. This keeps the chart from being skewed and guarantees that both high and low MIC values are evident and comparable. Each antibiotic (Penicillin, Streptomycin, and Neomycin) is consistently color-coded throughout the chart to make identification and differentiation simple. Sorting: The bacteria are arranged according to their Gram staining and the average MIC values for the three antibiotics. This makes it easier to spot patterns and variations in how Gram-positive and Gram-negative bacteria respond to antibiotics. Clear Axis Labels and a Descriptive Title: The chart includes clear axis labels, a descriptive title, and a legend to help readers understand the data being displayed.

SCALE, VISUAL ENCODING AND COLOR: To handle the wide range of MIC values effectively, the y-axis scale is set to a logarithmic scale. By doing this, the chart is kept balanced while making both high and low MIC values visible. Visual Encoding: The data is visualized using a grouped bar chart. Three bars, one for each antibiotic, are used to represent each bacterium, which are grouped according to the Gram staining method. This encoding makes the comparison of antibiotic efficacy within each bacterial group possible. Penicillin, Streptomycin, and Neomycin are the three antibiotics that are consistently color-coded in the chart. The easy identification and distinction of each antibiotic using color improves the readability and comprehension of the data.

The particular graph (grouped bar chart) is appropriate for this problem statement because it clearly communicates the relationships and variations between the three antibiotics (Penicillin, Streptomycin, and Neomycin) and their minimum inhibitory concentrations (MIC) values on 16 different bacteria when taking into account the Gram staining data.

WHY THIS PARTICULAR ILLUSTRATION? (CHOICE OF GRAPH): Comparison: The grouped bar chart enables a simple visual comparison of the three antibiotics' MIC values for each bacterium. Viewers can quickly determine which antibiotic works better or worse against a particular bacterium, helping to spot any patterns or trends. Gram staining is used to categorize the bars of the graph into Gram-positive and Gram-negative bacteria. This makes it simple for viewers to compare how each antibiotic works on the two different types of bacteria separately, providing insights into how Gram staining affects antibiotic efficacy. Wide Range of Data: The logarithmic scale on the y-axis efficiently handles the wide range of MIC values, which can differ noticeably between various bacteria and antibiotics. With the help of this scale, it is possible to see both small and large MIC values on the same chart without one overpowering the other. Clarity and Readability: The color-coding, sorting, and rotating of the x-axis labels are design choices that improve the clarity and readability of the chart. The names of the bacteria, data points, and antibiotic effectiveness are easily recognized and interpreted by viewers. Concise Representation: The grouped bar chart distills a lot of data into a format that is easy to understand. It offers a comprehensive analysis of the efficacy of antibiotics against a variety of bacteria, making comparisons and conclusions simple. The grouped bar chart is an efficient and appropriate solution for this problem statement when taking these benefits into account. It enables decision-makers in the medical field, including researchers, practitioners, and stakeholders, to quickly comprehend the data and decide how to use antibiotics and determine bacterial susceptibility.

EFFECTIVE COMMUNICATION: Comparing the MIC values of the three antibiotics for each bacterium is simple thanks to the grouped bar chart. Effective comparisons are made possible by the structured presentation of the data, which enables viewers to recognize which antibiotic works better or worse against particular bacteria. Gram staining-based grouping of the bars offers important information about how each antibiotic behaves toward both Gram-positive and Gram-negative bacteria. This distinction makes it easier for viewers to comprehend how bacterial traits affect antibiotic effectiveness. Handling Wide Range of Data: The y-axis's logarithmic scale efficiently handles the MIC values' wide range, preventing one set of data from predominating the chart. A thorough understanding of antibiotic performance is made possible by this scale, which ensures that both small and large MIC values are visible. Easy Identification of Antibiotics: Color-coding the bars consistently for each antibiotic allows viewers to easily distinguish between Penicillin, Streptomycin, and Neomycin, enhancing the readability of the chart. Clear Axis Labels, a Descriptive Title, and a Legend: These elements give the data being presented context and understanding. The chart's purpose is clearly understood by viewers, who can correctly interpret the data. _Effective Data Presentati_on: The chart reveals trends and patterns, making it simpler for viewers to interpret the data by grouping the bacteria according to Gram staining and average MIC values. Concise Visualization: A significant amount of data is condensed into a clear and understandable format in the grouped bar chart. Without being overwhelmed by unnecessary detail, it enables viewers to understand the important information. Overall, by enabling viewers to gather insightful information, draw conclusions, and make defensible choices about the efficacy of antibiotics against various bacteria, these design choices support effective communication. For researchers, medical professionals, and anyone else interested in comprehending antibiotic performance, the chart effectively and easily communicates complex data.

[faizanxmulla]

Name : Faizan Mulla Roll No. : 21f1003885

DESCRIPTION

The chosen visualization is a pair of grouped bar plots, representing the minimum inhibitory concentration (MIC) of three antibiotics across different types of bacteria, segregated by their Gram staining property into two distinct subplots: one for Gram-positive bacteria and one for Gram-negative bacteria.

The x-axis represents different types of bacteria and the y-axis denotes the MIC on a logarithmic scale. Each antibiotic is differentiated by color, as indicated in the legend.

CHOICE of visualization

The grouped bar plots were chosen to allow direct comparison of the antibiotics' effectiveness on each bacteria type. The logarithmic scale for MIC values accommodates the wide range of MIC values, allowing for meaningful comparison. The division of bacteria into two categories (Gram-positive and Gram-negative) provides a cleaner representation of the Gram staining property and reduces clutter in the visualization.

INSIGHTS gathered / Observations:

1. Variability of Antibiotic Effectiveness: The effectiveness of the antibiotics varies greatly across different bacteria, indicating that the choice of antibiotic should be tailored to the specific type of bacterial infection.

2. Penicillin's Effectiveness on Gram-positive Bacteria: Penicillin shows a particularly high effectiveness (low MIC values) on some Gram-positive bacteria such as 'Brucella anthracis', 'Diplococcus pneumoniae', and 'Streptococcus pyogenes'.

3. Effectiveness on Gram-negative Bacteria: None of the antibiotics are uniformly effective against all Gram-negative bacteria. The MIC values are quite high for some bacteria like 'Aerobacter aerogenes' and 'Escherichia coli' indicating less effectiveness.

4. Streptomycin's Wide-ranging Effectiveness: Streptomycin seems to be fairly effective (low MIC values) across a wide range of both Gram-positive and Gram-negative bacteria.

5. Neomycin's Effectiveness: Neomycin appears to be less effective (higher MIC values) compared to Penicillin and Streptomycin on several bacteria such as 'Brucella anthracis', 'Streptococcus pyogenes', and 'Escherichia coli'.

CRITIC of visualization :

• The visualization primarily emphasizes the comparison of antibiotic effectiveness on each bacterium, potentially downplaying the differences in effectiveness between different bacteria for the same antibiotic.

• The division into two plots based on Gram staining might also lessen the direct comparison of MIC values between Gram-positive and Gram-negative bacteria.

• Color coding was used to differentiate the three antibiotics, and the division into two subplots based on Gram staining provided an additional layer of information.

• The bacteria names were sorted based on their order in the dataset, which doesn't emphasize any specific aspect of the data, but keeps the visualization clean and easy to interpret.

SUGGESTIONS for the visualization :

1. Interactive Visualization: Given the number of bacteria types and the three different antibiotics, the chart can become cluttered and overwhelming. An interactive visualization where the user can select or hover over a bar to get exact MIC values or to highlight a specific antibiotic across all bacteria could enhance readability.

2. Different Chart Types: While the grouped bar chart allows for direct comparison between antibiotics for each bacterium, it might not be the best for comparing the effectiveness of a specific antibiotic across different bacteria. A line chart for each antibiotic could give a clearer view of how their effectiveness changes across different bacteria.

SUMMARY / OVERVIEW :

In summary, the visualization design choices were driven by the goal of effectively comparing the antibiotics' effectiveness on different bacteria, while also cleanly representing the Gram staining property. The decisions regarding visualization type, scale, color, and data transformation were taken to facilitate a clear, uncluttered, and informative visualization of the dataset.

[SrivinaySridhar]

Name: Srivinay Sridhar Roll number: 21f1006569

Visualization type: Grouped Bar charts

Note: Due to the size of the image, it is better to view it in a separate tab.

Visualization of the concentrate effectiveness of Antibiotics on Gram-positive and negative bacteria

Introduction

Antibiotics are a class of powerful medications used to treat bacterial infections in both humans and animals. Different antibiotics target specific types of bacteria, and their effectiveness can vary depending on whether the bacteria are Gram-positive or Gram-negative. Bacteria that are stained dark blue or violet are Gram-positive. Otherwise, they are Gram-negative. The Minimum Inhibitory Concentration (MIC) is the smallest amount of an antibiotic needed to stop the growth of a specific germ under specific conditions.

The data and purpose

The Minimum Inhibitory Concentration (MIC) of Penicillin, Streptomycin, and Neomycin is available for 16 bacteria; 9 of which are Gram-negative and the remaining 7 are Gram-positive. The purpose of visualizing this dataset is to compare the MIC values of the 3 antibiotics for Gram-positive and Gram-negative bacteria while also understanding how different bacteria of same type have different values of MIC. Since MIC is inversly proportional to the effectiveness of an antibiotic on bacteria, I felt the need to derive the Concentrate effectiveness (in %) for the antibiotics which is directly proportional to the effectivess.

Visualization choice

The choice of using grouped bar charts was a rational one as it makes it easier to compare the values across various bacteria types while referring to the same antibiotic on the same scale with colour coded bars. The split of the visualization between Gram-positive and negative is because we can see a stark difference in the concentration values if any and it makes it easier to compare across the two groups.

The x-axis marks the 3 antibiotics while the y-axis marks the Concentration effectiveness (in %).

Derived metric: Concentrate effectiveness = 1 - (MIC of antibiotic for the bacteria/MIC of all 3 antibiotic for the bacteria) * 100 Note: More the concentrate effectiveness, the better is the effect of the antibiotic for the bacteria Reason for deriving this value instead of just plotting the MIC values: The range of values varies over different scales so it felt better to normalize them. And since it is percieved that more is good, I felt the need to derive a value that has a positive relationship with the effectiveness.

The colour palatte used is pleasing to the eye and intentionally chosen so that the colours do not appear too bright and convey any wrong messages. They also belong to different spectrums and thus are easy to distinguish.

Though the height of the bar only marks the concentrate effectiveness, the actual MIC values have been annotated on top of the bars to make sure the visualization is not only good looking but also contains all the information that is important.

The visualization has also been sorted by the concentrate effectiveness of Penicillin (pink colour) which also happens to be the 1st index in the x-axis for better readability. This was also a conscious decision as the variance of MIC of Penicillin is the highest among the 3 antibiotics.

Insights

• For Penicillin, its MIC values for Gram-positive bacteria are low, indicating higher susceptibility, while for Gram-negative bacteria, the MIC values are high, indicating reduced susceptibility.
• Neomycin's MIC values for both Gram-positive and Gram-negative bacteria are low, indicating high effectiveness for any type of bacteria.
• Streptomycin has a good effectiveness against most of the Gram-negative bacteria but only average against the Gram-positive bacteria.

Tools used

• Google Colab - Python
• Seaborn
• Figma

[21f1005173]

Name : M.S.Srinivass Roll No. : 21f1005173

Data

The given data contains the MIC values of 3 antibiotics on 16 different types of bacteria, out of which a few are gram positive and the rest are gram negative. The antibiotics are penicillin , streptomycin and neomycin. MIC or Minimum Inhibitory Concentration is the is the lowest concentration of a chemical, usually a drug, which prevent the growth of bacteria or fungi, and in this case we are given the MIC of the three antibiotics with respective to 16 types of bacteria. Lower the MIC values, the more effective the antibiotic is against the bacteria.

Choice of Visualization

A grouped column chart was chosen and the data was split into 2 graphs, one having the gram positive bacteria and the other having the gram negative bacteria. All MIC values were also converted to the logarithmic scale ( natural log ) so as to scale down some high values and to visualize it better, This way the reader can see that a very well performing antibiotic for a particular bacteria would have its column on the negative Y-axis and a relatively poorly performing antibiotic would have a column on the positive Y-axis. A dark background with white text and light shades of red, green and blue were chosen for the columns representing the antibiotics. This makes the graph legible and pleasing to look at. X-axis represents the bacteria and Y-axis represents the MIC values.

Insights

From the visual we can see that :

1. Penicillin performs really well against the gram positive bacteria but performs the worst against the gram negative bacteria.
2. Streptomycin performs average against both the gram positive and negative bacteria.
3. Neomycin performs the best against gram negative bacteria and also really well against a few gram positive bacteria except 3 of them.
4. Overall best Antibiotic choice against the given bacteria seem to be Neomycin

Tools Used

1. https://chart-studio.plotly.com/ to plot the 2 graphs separately
2. An image editor to combine them both into one picture

[BarunSinha]

Name - Barun Kumar Sinha Roll No - 21f1002021

Performance of 3 most popular Antibiotics minimum inhibitory concentration (MIC) on 16 Bacteria

Goal

Determining which antibiotics (and how much concentration) is required to prevent growth in vitro.

Design choice

I choose bubble chart because of the two variables (i.e., minimum inhibitory concentration, gram staining) used in determining the efficiency of the antibiotics on each bacteria . We represent the efficiency as a bubble, bubble color will determine the gram stain and size of the bubble will represent the concentration of the antibiotic .

Visual Encodings

• There are three sections of the graph for each of the antibiotics .
• Y-axis represents the minimum inhibitory concentration (MIC) , The values on the y-axis are scaled to the natural logarithmic scale.
• On X-axis 16 bacteria name are present
• Bubble size represents the amount of the concentration required to prevent growth in vitro, and the bubble color represents the effect of gram stain .

Observation

• Out of 16 bacteria, 9 bacteria have a negative staining effect.
• Out of 7 bacteria which have positive stain effect, 5 bacteria (Brucella anthracis,Diplococcus pneumoniae, Staphylococcus albus, Streptococcus viridans, Streptococcus hemolyticus) require only a small concentration of Penicillin to prevent growth in vitro .
• Brucella anthracis, Staphylococcus albus, Staphylococcus aureus require only a small concentration of Neomycin and Streptomycin to prevent growth in vitro.

Tools used

• PowerBI
• Excel

[ghost]

Antibiotic Effectiveness on Bacteria

Name: Prateek Ganguli Roll: 21f1004044

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URL: https://public.tableau.com/views/BacteriavsAntibiotic/Bacteria_vs_Antibiotic Tool used: Tableau Public

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Overview

For this task, I have chosen to create a grouped bar chart to effectively communicate the data. The chart will display the minimum inhibitory concentration (MIC) values for the three most popular antibiotics on 16 different bacteria. Each antibiotic is represented by a distinct grouped row, and the bacteria are coloured based on their Gram staining reaction (Gram-positive and Gram-negative). The chart aims to showcase the varying effectiveness of antibiotics against different bacteria and highlight any patterns or trends based on Gram staining.

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Design Rationale

• Visualization Type: A grouped bar chart is chosen as it allows a clear comparison of MIC values across antibiotics for each bacterial infection. The grouping by most effective antibiotic reaction will enable viewers to identify any differences in antibiotic performance based on Gram-positive or Gram-negative nature of bacteria.
• Grouping of rows: Each antibiotic will be represented by a unique grouped row throughout the chart. This visual boundary is an effective visual cue, making it easier for viewers to differentiate between the antibiotics and identify trends.
• Colour Encoding: Gram-positive or Gram-negative is indicated by contrasting colours, that makes it easy for viewers to quickly identify Gram-sensitive trends based on antibiotic performance.
• Axis Labels and Legends: The chart includes clear and informative axis labels for both the x-axis (bacteria) and the y-axis (MIC values grouped by antibiotic). A legend is also provided to link the Gram-sensitivity colors to their respective classes.
• Data Transformation: To facilitate effective comparison, the MIC values will be displayed on a logarithmic scale on the y-axis. This transformation helps in reducing the visual skewness caused by varying MIC ranges and ensures better visibility of differences among the antibiotics.
• Sorting: Bacterial infections will be sorted horizontally based on their reaction to antibiotics. This will assist in identifying the most effective antibiotic for a given set of bacteria, aiding in easy comparison across both Gram staining categories.

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Data Communicated

The grouped bar chart will effectively convey the following aspects of the data:

• Comparison of antibiotic effectiveness against different bacterial infections.
• Identification of trends and patterns based on Gram staining (Gram-positive vs. Gram-negative).
• Easy recognition of the most effective antibiotics for specific infections.
• Highlighting any variations or inconsistencies in antibiotic performance across bacteria.

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Potential Limitations:

While the grouped bar chart is a suitable choice for this dataset, it may have some limitations:

• Overlapping labels if there are many bacterial infections, which could reduce readability.
• The chosen color palette must be accessible to all viewers, considering any potential colorblindness.
• The logarithmic scale might obscure small differences in MIC values between antibiotics.
• Inherent limitations in bar charts regarding displaying the shape of the entire distribution of MIC values.

[priyanka-maz]

Name - Priyanka Mazumdar Roll No. - 21f1000367

Data Visualization Regarding Butin Antibiotic Data

Link to Visualization

Information About the Data & Visualization

Antibiotics are a class of medications that are used to treat bacterial infections. The data provided to us has various parameters which are significant:

1. Bacteria - This column stores the names of the bacteria we have taken under consideration for this visualization.
2. Penicillin - This is the name of an antibiotic used and the MIC value for this antibiotic over the bacteria given. The effectiveness of antibiotics depends on the minimum inhibitory capacity because it refers to the minimum amt. of antibiotic required to inhibit the growth of this bacteria.
3. Neomycin - This is the name of an antibiotic used and the MIC value for this antibiotic over the bacteria given.
4. Streptomycin - This is the name of an antibiotic used and the MIC value for this antibiotic over the bacteria given.
5. Gram Staining - Gram stain, is a method of staining used to classify bacterial species into two large groups: gram-positive bacteria and gram-negative bacteria.

Values that the visualization tries to show is the lowest MIC value for a particular bacteria with the use of a specific antibiotic because then it refers to that antibiotic being most appropriate for treatment. The visualization also aims to represent the gram class a particular bacteria falls under and the overall performance of antibiotics on these bacteria. It aims to show whether a certain antibiotic is more effective for certain gram classes or not.

Design Principles used & their Benefits

1. Type of visualization

• A bar chart has been used for showing the performance of each antibiotic over the entire set of bacteria given.
• A scatter plot has been used to show the overall flow of MIC values between different bacteria classes treated by the same antibiotic - the assembly follows the Gestalt principle of continuity.

2. Gridlines and Axes used

• Even though the gridlines may cause visual clutter, they are necessary here so as to compare the values of performance of three antibiotics over one bacteria as MIC values range quite a bit. One may also use the relativity of perception in an aligned frame to compare the values.
• The opacity of the gridlines is low to remove the problem of visual clutter.
• The exact MIC values are provided for reference.

3. Colours used

• In the bar chart, ranking (lowest MIC value as 1 and highest as 3) is used for marking the hue of the bars. The colour scheme used shows the lowest MIC values in a green colour since they are more favourable and the highest MIC values are in a red colour as they are least favourable. This has been chosen to show a semantically resonant colour scheme so that the best choice is visually apparent to the user.
• In the scatter plot, the gram-negative is shown using a red marker and the positive using a blue marker since bacteria that are stained dark blue or violet are Gram-positive.

5. Markers used

• Hollowed-out markers have been used to reduce visual clutter and show a flow-like pattern.
• The shapes use Gestalt's principle of similarity as well to group gram-stained bacteria.

6. Sorting used

• The method of sorting is the SUM of MIC values for a particular bacteria. This pattern aims to show bacteria that usually have low MIC values and are thus easier to treat.

Insights obtained from the Visualization

1. From the visualization we can see that Neomycin has most of the lowest MIC values overall (concentration of green bars) and therefore is the most efficient one, then streptomycin and then Penicillin. Thus, streptomycin is more or less an average antibiotic for most bacteria and their gram staining classes.
2. Most of the green bars treated by Neomycin are gram-negative bacteria. Most of the green bars treated by Penicillin are gram-positive.
3. Bacteria like Brucella Anthrasis and Staphylococcus Aureus do not require much concentration from any of the antibiotics.
4. Bacteria like Aerobacter Aerogenes and Pseudomonas aeruginoas are not much affected by Penicillin but Neomycin and Streptomycin work excellently on them.

Limitations of the Design

1. Stacking the three antibiotics as three columns may confuse the reader for comparing values, more so for colour-blind readers as the colour encoding is strong here.
2. The logarithm scale may skew the exact values for MIC and affect interpretability.
3. If the number of bacteria to represent increase, the same visualization would not be effective because it would cause visual clutter.

Tools used

• Colab - used to reorder the data, such that each bacteria is repeated in 3 rows, (required redundancy) with the 3 different antibiotics, to facilitate the visualization. Also used to create the ranking column.
• Tableau - Used to visualise the .csv file after preprocessing on colab.

[Invincyble]

Vineeth Reddy Donthi 21f1004514

Antibiotic Effectiveness Across Bacterial Strains

Introduction-

Minimum Inhibitory Concentration (MIC) is a crucial measure in microbiology, determining the lowest concentration of an antimicrobial needed to inhibit bacterial growth. It guides antibiotic selection for infections, and monitoring MIC changes aids in tracking antibiotic resistance. Gram staining classifies bacteria into Gram-positive and Gram-negative groups based on cell wall characteristics. This rapid technique aids in diagnosing infections, selecting appropriate antibiotics, and providing valuable insights into bacterial identification. The dataset provides information about the Minimum Inhibitory Concentration (MIC) values of three different antibiotics - Penicillin, Streptomycin, and Neomycin. The dataset also includes the Gram Staining reaction for each bacterium, indicating whether they are Gram-positive or Gram-negative.

Design Choices-

1. Type of visualization

The purpose of using a logarithmic horizontal bar chart is to visualize data that spans a wide range of values, particularly when there is a significant difference between the magnitudes of the data points. The logarithmic scale compresses the data, making it easier to compare values that vary greatly in size on the same chart. The data is separated to positive and negative gram-staining bacteria and visualized accordingly. In the above charts, the y-axis denotes the various types of bacteria and the x-axis denotes the MIC Values of various antibiotics. The tick lines in bar charts serve to mark axis points, represent data values, aid interpretation, measure scale, and align gridlines. They enhance chart readability and enable viewers to understand data differences, identify specific points, and grasp data distribution and trends effectively. Each bacteria have a bar denoting the MIC Value by three different antibiotics.

2. Colors, size

Penicillin is denoted by blue color, Streptomycin by orange color and Neomycin by grey color. Axis labels have a font size of 12 to be easily readable without being too distracting. Data labels have a font size of 14 to stand out.

Inference-

The antibiotic with the lowest MIC value against a specific bacterial strain is generally considered the most effective. A lower MIC indicates that a smaller concentration of the antibiotic is required to inhibit bacterial growth, making it more potent against that particular strain. Penicilin worked best against most of the gram-positive bacteria while Neomycin worked best against gram-negative bacteria.

Tools Used-

Google Sheets Google Colab

[VarnikaB]

Name : Varnika Bagaria Roll No. : 21f1007039

Overview:

In this task, I have used Google Colab and Google Sheets to obtain the chart. I have chosen to use a scatter chart. The given data has MIC values of 3 antibiotics on 16 types of bacteria. I first went about normalizing the values and then plotting it.

Choice of visualization

1. Type of visualisation: I felt that scatter plot gives me more insights. I felt that here pinpointing of mic values rather then its decreasing and increase in its value is better.

2. Color coding: I have used different set of marker and color to plot Antibiotic vs Gram Staining Graph. Pencilin: Blue Circle Streptomycin: Green Square Neomycin: Red Triangle I also chose to have markers because it made identifications clearer. The colors are used so that there is mix up between shades For Antibiotic vs bacteria I have used only color coding to differentiate. Here, I chose not to have markers as there 16 bacteria an it would the graph loo more complex. Axis labels have a font size of 12 to be easily readable without being too distracting.

Chart

Shortcomings

• It is difficult to represent in 1 chart
• With this graph it is hard to determine which antibiotic is more suitable for which bacteria.
• The small size of markers might make identifiaction a bit difficult

[harshadpaikrao]

Name : Harshad Shahu Paikrao Roll Number : 21f1002085

Link to the visualization : https://public.tableau.com/app/profile/harshad.paikrao/viz/DatavizGA3/Sheet1?publish=yes

Overview:

The given dataset contains the minimum inhibitory concentration (MIC) values of the three most popular antibiotics on 16 different types of bacteria. Along with the gram staining test effect of each bacteria. The purpose of the visualization is to effectively highlight the most effective antibiotic for each of the 16 types of bacteria. The target audience is medical professionals.

Design Decision:

Type of visualization:

• Considering that the purpose of this visualization is to highlight the most effecting antibiotic among the 3, Its best to compare their efficiency side by side using bar chart.
• Based on the gram staining test, the bacteria are divided in two group i.e positive and negative.
• Considering the large difference in MIC values, the data is transformed using log with base 10 that allowed better visualization.

Color and labels:

• To pop out the most effective antibiotic, the one with lowest MIC is colored in green while others are kept in grey color.
• The MIC values are labeled on every bar to allow comparison.

Tools used

• Tablue desktop

Inference:

• Penicilin, being the oldest antibiotic, needs very high concentration and also not very effective against most of the bacteria. However it is still the best choice for few gram positive bacteria.
• Neomycin is the latest one and works best against most of the gram negative bacteria.
• Streptomycin close to but slightly less that Neomycin

Shortcomings

• Due to log transformation the comparison of MIC values is not very accurate.
• Information about gram negative (harder to treat) and gram positive (comparatively easier to treat) is captured.

[afnan-ahmad]

Visualisation on effectiveness of antibiotics

Created by: Afnan Ahmad | 21F1003730

Objective

The aim of this visualization is to help in comparing the effectiveness of three antibiotics, namely, Penicillin, Streptomycin, and Neomycin against 16 different bacteria. Minimum Inhibitory Concentration (MIC) is used as the measure of effectiveness.

Design Decisions

The design choices have been made, keeping in the mind the following principles:

• The visualization should be easy to interpret and be understandable without requiring additional details. However, note that the design assumes the viewer to be familiar with the terms used in the visualization as well as the measure used herein, i.e. minimum inhibitory concentration.
• The visualization should be cohesive, i.e. a single visualization should be able to convey the needed information without requiring the viewer to refer to additional visuals.

Visual Encodings

Type of Visualization: A stacked bar chart has been used for this visualization, because it allows us to easily compare the concentration values across the different antibiotics. Furthermore, distinct colors have been used to represent the antibiotics, it allows us to quickly see the most ineffective (high concentration) and effective (low concentration) antibiotics for a particular bacterium.

Colors: Bright yet low saturation colors have been used as they are easier on the eyes, while allowing us to easily differentiate between them.

Grouping: One of the unique key features of this visualization is that colors have been used to group the labels (on the y-axis) representing the names of bacteria. The labels have been sorted and grouped according to the gram-staining characteristic, i.e. gram-positive or gram-negative. Furthermore, legends have been added accordingly to convey the meaning behind the groups.

Data Transformation

The minimum inhibitory concentration (MIC) is represented on the chart in a logarithmic scale so that the numbers are normalized and are on the same scale, allowing us to compare between them.

Shortcomings

Primarily, the fact that lower concentration is better might not be apparent at first glance, leading possibly to wrong conclusions. Although, this issue has been mitigated to some extent by mentioning on the chart that lower is better, a different encoding may possibly be used to convey this more clearly.

It should be noted however that, as mentioned in design decisions, the design assumes basic familiarity with the objective of this visualization as well as the measure used herein. Therefore, it is expected that the intended audience would be able to interpret it properly.

Tools Used

Microsoft Excel

[Vishvam10]

Name : Vishvam Sundararajan S Roll No : 21f1005939

Visualization on Effectiveness of Antibiotics

Note that the MIC column is normalized (between 0 and 1) for better visualization

Aim of the Visualization

The aim of this visualization is to help in comparing the effectiveness of three antibiotics, namely, Penicillin, Streptomycin, and Neomycin against 16 different bacteria. Minimum Inhibitory Concentration (MIC) is used as the measure of effectiveness.

Type of Visualization Used

Heatmap

Why I Went With This

• A heatmap is an excellent choice when dealing with two categorical variables (bacteria and antibiotics) and one numerical variable (MIC).
• It allows us to visualize the data in a compact and visually appealing manner, highlighting patterns and trends effectively.

Design Decisions

• The decision to use a heat map chart was driven by its ability to compactly display antibiotic efficacy against bacterial infections.
• Color intensity represents MIC values, aiding quick comprehension.
• A vibrant blue to green color map enhances visual appeal, and added padding between axis labels, title, and the chart improves readability and spacing.

Tools Used

Python, Pandas, Matplotlib, Seaborn and Jupyter Notebook

Code Used To Generate The Visualization :

  import pandas as pd
  import matplotlib.pyplot as plt
  import seaborn as sns

  # Renamed the given file to data.xlsx
  df = pd.read_excel('data.xlsx')

  # Normalize the MIC column
  min_mic = df[['Penicilin', 'Streptomycin', 'Neomycin']].min().min()
  max_mic = df[['Penicilin', 'Streptomycin', 'Neomycin']].max().max()
  df[['Penicilin', 'Streptomycin', 'Neomycin']] = (
      df[['Penicilin', 'Streptomycin', 'Neomycin']] - min_mic) / (max_mic - min_mic)
   )

  # Pivot the dataframe to have antibiotics as columns, 
  # bacteria as rows, and normalized MIC values as values
  pivot_df = df.pivot(
     index='Bacteria', columns='Gram Staining', 
     values=['Penicilin', 'Streptomycin', 'Neomycin']
   )

  plt.figure(figsize=(10, 16))

  sns.heatmap(
     pivot_df, cmap='mako_r', annot=True, 
     fmt=".2f", linewidths=0.5, 
     cbar_kws={'label': 'Normalized MIC'}
  )

  plt.title(
    'Effectiveness of Antibiotics Against Bacterial Infections', pad=40
  ) 
  plt.xlabel('Gram Staining', labelpad=24)  
  plt.ylabel('Bacteria', labelpad=24)  
  plt.show()

[hitansh1299]

Roll No: 21F1001178, Hitansh Shah Title: Antibiotic Efficacy of Antibiotics on Various Bacterial Infections

Chart Design Rationale: To effectively communicate the data on antibiotic effectiveness for different bacterial infections, I have chosen to use a grouped bar chart with a logarithmic scale for the inverted MIC values. This design decision facilitates easy comparison of the three most popular antibiotics on each bacterial infection and allows viewers to discern trends and patterns in antibiotic efficacy. Usually with MIC, the lower is better, this is counterintuitive. In this visualization, the higher is better because of inversion of MIC values before logarithmic scaling.

Visual Encodings:

X-axis: The X-axis represents the 16 bacterial infections, labeled with their names for easy identification. Y-axis: The Y-axis represents the Minimum Inhibitory Concentration (MIC), indicating the effectiveness of the antibiotics. It is scaled logarithmically to accommodate a wide range of MIC values and prevent data compression. Grouped Bars: Each antibiotic's efficacy against various infections is represented by 15 grouped bars, one for each bacteria. The height of each bar corresponds to the Inverted Logarithmic MIC value for that specific antibiotic-bacteria combination.

Grouped bar chart: This chart type facilitates easy comparison of multiple categories (bacteria) across different subgroups (antibiotics) at once, enabling viewers to identify the most effective antibiotic for each bacterial infection quickly. Logarithmic scaling: As antibiotic effectiveness can vary significantly, using a logarithmic scale ensures that both high and low MIC values are visually distinguishable, preventing small values from being overwhelmed by larger ones. Inversion: To help the chart be more intuitive.

[dipak-patil-iitm]

Data Viz of Antibiotic Effectiveness on Bacteria

Name : Dipak Patil Roll No : 21f1004451

Objective:

The objective of this data visualization is to effectively communicate the antibiotic effectiveness (minimum inhibitory concentration - MIC) of the three most popular antibiotics (Penicillin, Streptomycin, and Neomycin) against 16 bacterial infections. The visualization should allow an easy comparison of antibiotic efficacy for each bacterium and highlight any patterns related to Gram staining (Gram-positive or Gram-negative).

Design Decisions:

1. Type of Visualization: A stacked bar plot with rings as the mark was chosen for its ability to show the comparative effectiveness of the three antibiotics for each bacterial infection while accommodating multiple data points.

2. Scale: To address the wide range of MIC values, a logarithmic scale centered on 1 MIC unit was used. This helps in distinguishing closely located MIC values near 1 and avoids clustering of lower MIC values.

3. Visual Encodings: a. Colors: A color scheme with green, orange, and red was chosen to represent Penicillin, Streptomycin, and Neomycin, respectively. This color encoding provides good contrast, making it easier for viewers to differentiate between the antibiotics. b. Rings: The use of rings as marks creates a distinct visual representation for each antibiotic, enhancing visibility and improving the chart's aesthetic appeal.

4. Sorting: The data can be sorted based on the MIC values of a specific antibiotic to highlight the most effective and least effective antibiotics for each bacterial infection.

Tools Used:

Tableau Public was utilized to create the data visualization. Tableau is a powerful data visualization tool that allows for easy exploration and representation of data through interactive charts and graphs.

Conclusion:

In conclusion, the stacked bar plot with rings, using a logarithmic scale and distinct color scheme, created using Tableau Public, effectively communicates the antibiotic effectiveness data. The visualization enables viewers to compare the efficacy of Penicillin, Streptomycin, and Neomycin against different bacterial infections and easily identify patterns related to Gram staining. The use of rings as marks and appropriate color choices enhances visibility and aids in the quick understanding of the data. While this design presents the data well, it's essential to consider potential limitations related to clutter and perception of size. Overall, the visualization serves its objective by providing valuable insights into antibiotic efficacy and its relationship with bacterial infections and Gram staining.

[mb1AtGithub]

Manisha Bapat 21f1000449

Brief Information:- MIC: The MIC, or minimum inhibitory concentration, is the lowest concentration (in μg/mL) of an antibiotic that inhibits the growth of a given strain of bacteria. Minimum inhibitory concentration (MIC) can be determined by culturing microorganisms in liquid media or on plates of solid growth medium. A lower MIC value indicates that less drug is required for inhibiting growth of the organism; therefore, drugs with lower MIC scores are more effective antimicrobial agents.

Gram-Staining: Gram staining is a common technique used to differentiate two large groups of bacteria based on their different cell wall constituents. Based on it the bacteria are classified into 2 types: Gram positive and gram negative

Objective: To compare the 3 given antibiotics for different gram positive and gram negative bacteria.

Observations:

1. Overall, Penicilin is more effective on gram positive than gram negative bacteria.
2. Neomycin is more effective on gram negative than gram positive bacteria
3. Streptomycin seems equally efficient on both gram positive and gram negative bacteria.
4. In general, Neomycin is the more effective than the others.

Rationale to choose Area chart

1. Easy to compare
2. Colors have been chosen as per the overall performance of the drugs

Shortcomings:

1. MIC: lower the better is not evident from the chart.
2. One needs to hover over the dot to get the exact value

For Gram Positive :

For Gram Negative:

[S-D-P]

Name: Siddhi Dhirajkumar Pandirkar RollNo: 21f1001177

Tool: MS Excel

Design Decisions The choice of a panel bar chart was influenced by an exploration of various visualization options, including Burtin's antibiotics visualization and submissions from a Chance magazine contest. Burtin's visualization was visually appealing and unique, but it was noted that it might be confusing at first glance, requiring viewers to read in detail to understand its full meaning. On the other hand, the submissions for the Chance magazine contest demonstrated the effectiveness of a bar chart in conveying the information clearly and remaining pleasing to the eye. The panel bar chart is chosen as the visualization type to present the data in separate panels for Penicillin, Streptomycin, and Neomycin. This decision allows for clear and focused comparisons between the three antibiotics and their effectiveness across different bacterial species. Each panel acts as a standalone bar chart, reducing clutter and aiding viewers in interpreting the data without visual interference from other antibiotics. The panel bar chart presents the data without scaling, allowing for a raw comparison of antibiotic effectiveness. Bright and different colors are used for each antibiotic to ensure clear visibility and easy differentiation. Bacteria are sorted based on Gram Staining, aiding viewers in identifying Gram-positive and Gram-negative species promptly. These design choices create a visually engaging chart that maintains data clarity, facilitating direct assessments of antibiotic performance.

Downplayed Information In the visualization I created, the effectiveness of a particular antibiotic for each bacteria compared to others was not shown very effectively since I did not scale the data. This lack of scaling made it challenging to assess how each antibiotic performed across different bacteria at a glance. The differences in MIC values might not have been immediately apparent, and some data points could have been visually clustered or indistinguishable. One aspect that got downplayed or obscured in the visualization was the relative effectiveness of each antibiotic against specific bacterial species. Without scaling the data or providing interactive elements for filtering, it may be difficult for viewers to discern how well an antibiotic worked on different bacteria without detailed examination.

Improvements To improve the visualization's effectiveness, incorporating interactive elements, like data filtering or highlighting specific antibiotics, would be beneficial. Allowing viewers to interactively select a particular antibiotic could dynamically update the chart to show only the relevant data, providing a clearer view of antibiotic performance against different bacterial species and enabling more focused comparisons. Additionally, appropriate data scaling, such as using logarithmic scales or normalizing the MIC values, could enhance the chart's readability. Scaling the data would help evenly distribute data points along the y-axis, making it easier to identify differences in antibiotic effectiveness. But as I mentioned, I did not want to scale the data to show the raw difference in effectiveness.

References:

• https://healthdataviz.com/2013/12/20/nightmare-bacteria-antibiotics-and-the-remote-control/
• https://peltiertech.com/antibiotic-effectiveness-charting-revisited/
• https://peltiertech.com/antibiotic-effectiveness-a-study-of-chart-types/
• https://xcelanz.com/panel-bar-chart-displaying-3-sets-of-data/

[SrijanShukla]

Srijan Shukla 21f1000671

Clustered Bar Chart: In this clustered bar chart, each bacteria is represented on the x-axis, and the minimum inhibitory concentration (MIC) values of the three antibiotics are shown as grouped bars for each bacteria. The y-axis represents the MIC values. The three antibiotics (Penicillin, Streptomycin, and Neomycin) are color-coded and distinguished using the legend.

To add the information about the Gram staining reaction, we used text annotations below each bar group. Gram-positive bacteria are labeled as "Gram-positive," and Gram-negative bacteria are labeled as "Gram-negative."

The choice of a clustered bar chart is appropriate because it allows us to compare the MIC values of multiple antibiotics for each bacteria while also considering the Gram staining information. The grouping of bars for each bacteria helps in easy visual comparison of the antibiotic effectiveness, and the text annotations provide additional insight into the Gram staining reaction of each bacteria. The chart's title, axis labels, and legend provide context and improve the chart's interpretability.

Pie Chart: Each pie chart represents one antibiotic, and it is divided into two segments: one for Gram-negative bacteria and another for Gram-positive bacteria. The size of each segment corresponds to the percentage of bacteria with that particular Gram Staining reaction relative to the total number of bacteria affected by the antibiotic.

By using pie charts for each antibiotic, we can quickly compare the Gram Staining distributions across different antibiotics and identify any patterns or trends. This visualization helps us understand how the effectiveness of each antibiotic relates to the Gram Staining reaction of the bacteria it targets.

Stackplot: The purpose of the stackplot is to visualize the contribution of each antibiotic to the overall effectiveness for each bacterium. It allows us to observe how the cumulative effectiveness of the three antibiotics varies across different bacteria. Additionally, the stackplot helps in identifying the dominant antibiotics that contribute the most to the overall effectiveness for each bacterium. This visualization is useful for comparing the combined effects of multiple antibiotics and gaining insights into which bacteria are more susceptible to the combined treatment of these antibiotics.

Line Chart: The line chart allows us to observe how the MIC values change for each antibiotic as we move from one bacterium to another. It helps in identifying patterns and trends in the data, such as which antibiotics tend to be more effective against certain bacteria.

This type of visualization is useful when we want to focus on the trend of MIC values and how they vary across the different bacteria in the dataset.

[upatil98]

Uday Patil 21f1003481

Findings: The chart illustrates the performance of three antibiotics on 16 different bacteria strains, highlighting the MIC values for each combination. The horizontal axis represents the MIC values which are color-coded, while the vertical axis shows the bacteria.

Analysis: The chart reveals intriguing patterns regarding antibiotic efficacy against specific bacterial strains. Penicilin demonstrates remarkable effectiveness against Gram-positive bacteria, as evidenced by consistently lower MIC values for these strains. However, its performance against Gram-negative bacteria is comparatively weaker, necessitating higher concentrations to inhibit growth. In contrast, Neomycin appears to have a more balanced performance, showing moderate MIC values for both Gram-positive and Gram-negative bacteria. This characteristic could make it a versatile choice for treating a broader range of infections. Streptomycin, on the other hand, exhibits a significant advantage in tackling Gram-negative bacteria, with notably lower MIC values in comparison to the other antibiotics. Nevertheless, its potency against Gram-positive bacteria is less impressive.

For Gram Positive :

For Gram Negative:

[blackpearl006]

-- Name: Ninad Aithal
Roll No: 21f1006030

Objective

Compare the effect of 3 antibodies (Penicillin, Streptomycin, and Neomycin) on different types of bacteria and find which antibody is most effective against a bacteria. Also, identify any gross patterns, such as a strain of bacteria more susceptible to an antibody.

Information

MIC, or minimum inhibitory concentration, is a measure used in microbiology and pharmacology to assess the effectiveness of antimicrobial agents, such as antibiotics or antifungal drugs. It refers to the lowest concentration of a drug required to inhibit the visible growth of a microorganism, specifically a bacterium or fungus, in a controlled environment in vitro (in a laboratory setting). Lower concentration indicates better effectiveness.

Gram-positive bacteria retain the purple stain, while Gram-negative bacteria appear pink. This difference is due to the variation in their cell wall structures. Gram-positive bacteria have a thick peptidoglycan layer, while Gram-negative bacteria have a thinner peptidoglycan layer between two membranes. This distinction influences their susceptibility to antibiotics and their ability to cause infections.

Design Decision

I wanted an overall view of all the bacteria reacting to the antibiotics and also wanted to compare their effectiveness between the strains. I have created a Grouped Scatter Plot and color-coded the 3 antibiotics as follows: Penicillin - Orange, Streptomycin - Blue, and Neomycin - Red. The below 2 charts also follow the same color coding. All the charts' axes have been scaled to logarithmic values ranging from 0.001 to 1000 to compare the effectiveness of all antibodies. The bacteria have been sorted based on descending values of MIC.

Tools Used

I used Tableau for creating this dashboard.

Findings

• Penicillin requires very high concentration to kill Gram-Negative samples but needs very less concentration to kill Gram-Positive bacterias.
• Streptomycin has very little variance and works on nearly all bacterias in a very small range of concentration.

Conclusion

The visualization effectively conveys the information encoded in the data. The logarithmic scaling helps the patterns come out, and the comparison within gram strains shows the effectiveness of each antibody within a class of bacteria. The Grouped Scatter Plot with color-coded circle marks for different antibodies is aesthetically pleasing and also shows the trend of the data.

[Shreyays]

Shreya Y
21f1002768

A brief overview:

Discovered in the early 20th century, antibiotics have revolutionized modern medicine and have become an essential tool in combating bacterial infections. They work by targeting specific structures or processes within bacteria, disrupting their growth and replication. One common measure used to assess antibiotic effectiveness is the Minimum Inhibitory Concentration (MIC). MIC is the lowest concentration of an antibiotic that inhibits the visible growth of the microorganism in vitro (in a laboratory setting). The lower the MIC value, the more effective the antibiotic is at inhibiting the growth of the bacteria. The reaction of the bacteria to Gram staining is described by the covariate “gram staining”. Bacteria that are stained dark blue or violet are Gram-positive. Otherwise, they are Gram-negative.

Aim of the visualisation

The aim of the visualisation is to understand the absolute and relative effectiveness of the three types of antibiotics - Penicilin, Streptomycin and Neomycin in combating bacterial infection, measured using the MIC value. Further, we also aim to identify these trends within different categories of bacteria based on their reaction to gram straining. This would help in choosing the most effective treatment depending on the case and would help in detecting antibiotic resistance among others.

Design decisions and supporting scaling and encoding

A bubble chart has been used to convey the relative effectiveness across categories of antibiotics for each disease.

1. Distinguishing and comparison between gram straining categories and antibiotic types: While designing the chart, the main idea was to facilitate understanding the effectiveness in each gram straining category, and how each antibiotic fares in comparison with the others. Hence one chart for each strain has been used (with separate colour schemes) and separate colours have been used for each antiobiotic.
2. Data converted to display effectiveness: The MIC is an indication of the lowest concentration of an antibiotic to inhibit the growth of the microorganism. However, for better presentation, the inverse of the MIC has been used to demonstrate the effectiveness i.e lower the MIC, higher the inverse and visibly greater effectiveness.
3. Scale represented by size of the dots: The size of the dots is based on the effectiveness calculated in point 2, which facilitates the chart in displaying the effectiveness at a glance.
4. Colour schemes: Suited to the natural perception of positive and negative in the two charts.

Observations

1. Neomycin appears to be effective drug, across many bacteria, irrespective of the gram strain.
2. Streptomycin is more effective against negative gram strain as compared to positive gram strain.
3. Penicillin has a high MIC across all bacteria, and it's maximum effectiveness is against Streptococcus fecalis.
4. Bacteria which can be attacked with lower doses of any antibiotics (Neomycin) are Salmonella(Eberthella) typhosa and Streptococcus viridans.
5. In the positive straining category, Streptomycin is most effective against Proteus Vulgaris.

Tools used

The visualisation charts have been prepared using Flourish.

[faridkhan5]

Farid Khan 21f1002045

Understanding of the Problem

• Here, we have been given a list of bacteria and 3 antibiotics that are tested on them
• The effectiveness of the antibiotic is measured using a MIC, which represents the concentration of antibiotic required to prevent the growth of the bacteria
• Then the gram straining for each bacteria is given and this is irrespective of the type of antibiotic used. So, a positive result tells us that the bacteria is clearly observed under a microscope which proves its presence and a negative result means that the bacteria is not clearly visible under a microscope, which makes us difficult to prove its presence

Purpose

• To make use of the dataset to understand the effectiveness of antibiotics in inhibiting the growth of the bacteria
• Identify which antibiotic works the best for which bacteria
• This will help us find out the most effective antibiotics that prevent their respective cause

Design Decisions

• A Radial Tree is used to figure out the most efficient antibiotics
• This type of visualization was mainly used because it clearly depicted the contribution of each category (antibiotics) among all instances, which gave an overall view of the whole dataset
• To display the categories in a tree like format, the dataset was converted into a long format, such that the 3 categories branch out from an instance node
• Since a lower MIC leads to a more efficient antibiotic, the inverse of MIC was calculated such that the longest bars represent the most effective antibiotics for the respective bacteria
• 2 charts were plotted to segregate the postive and negative gram-straining bacteria respectively

Color Encoding

• solid colors were used for the bars in positive gram-straining which portray the fact that they are clearly visible
• faded colors were used for the bars in negative gram-straining which portray the fact that they are not clear

Observations

The 3 antibiotics that prevent the bacteria with highest effectiveness are given below:

Positive Gram Straining

• Penicilin -> Brucella anthracis and Streptococcus hemolyticus
• Neomycin -> Staphylococcus albus and Staphylococcus aureus
• Streptomycin -> Not as effective as the other two antibiotics

Negative Gram Straining

• Penicilin -> Not effective
• Neomycin -> Salmonella (Eberthella) typhosa and Brucella abortus
• Streptomycin -> Not effective

To conclude Neomycin is the most effective antibiotic, as it prevents both the Gram Straining bacteria with the least concentration

Tools used

The Radial Tree visualization from Flourish has been used. https://public.flourish.studio/visualisation/14547520/