Добавил:
Sekretar
kiopkiopkiop18@yandex.ru
t.me/Prokururor I Вовсе не секретарь, но почту проверяю
Опубликованный материал нарушает ваши авторские права? Сообщите нам.
Вуз:
Предмет:
Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5529_Библиотеки_им_академика_М_И_Перельмана
.pdf
184 Bioinformatics of Autoimmune Diseases
SS
between
SS
Total
is the sum of squares between groups, which measures how much group means devi-where
ate from the overall mean and
is the total sum of squares, which measures the total variation
in the data.
This statistic represents the proportion of the total variance in the dependent variable that is
explained by the grouping factor (e.g., treatment or condition). It ranges from 0 to 1, with higher
values indicating that a greater portion of the variation is explained by group differences.
In differential expression analysis, η2 indicates how much of the variability in a gene’s expression
is explained by the group categories, such as treatment type or disease subtype. A higher η2 suggests
that the group factor has a strong inuence on gene expression, making this metric useful for prioritizing genes that show consistent and meaningful differences across groups. It helps distinguish
between genes that show signicant p-values due to minor variance and those whose expression is
genuinely shaped by the grouping variable.
Tukey’s Honest Signicant Difference (HSD) test is a post hoc multiple comparison procedure used after ANOVA to determine which specic group means differ from one another. While
ANOVA tells us whether there is a statistically signicant difference somewhere among the group
means, it does not indicate which groups are different. Tukey’s HSD addresses this by performing
pairwise comparisons between all group combinations while controlling for the family-wise error
rate. It adjusts the p-values to account for multiple testing and provides condence intervals for each
comparison. In the context of gene expression, Tukey’s HSD allows researchers to identify precisely
which groups show upregulation or downregulation relative to others, making it especially valuable
when analyzing more than two biological conditions or subgroups. It enhances interpretability and
helps guide further biological validation or experimental focus.
The summarize _ anova _ results function “summarize _ anova.py” is designed to
perform comprehensive differential expression analysis for RNA-Seq data using one-way ANOVA
across multiple groups dened by a categorical variable, such as treatment condition, ethnicity,
or disease subtype. It begins by preprocessing the input data, ensuring that sample identiers in
the expression matrix match those in the metadata design table. For each gene, it collects expression values across the groups, lters out any group with insufcient sample size, and proceeds
with ANOVA to determine whether there are signicant differences in expression across the valid
groups.
Beyond basic statistical testing, the function computes key effect size metrics that provide greater
insight into the magnitude and relevance of observed differences. Eta-squared (η2) is calculated to
quantify the proportion of variance in gene expression explained by the grouping variable, offering a sense of how strongly the group classication impacts expression. In addition, when a control
group is specied, the function computes log2 fold changes and Cohen’s d for each non-control
group in comparison to the control. These values help to interpret not just whether differences exist,
but how large and biologically meaningful those differences are. Cohen’s d, in particular, provides
a standardized measure of the mean difference relative to variability, making it easier to compare
across genes.
If no control group is specied, the function defaults to comparing the top two groups with the
greatest mean expression difference for each gene. Alternatively, the function can perform post
hoc analysis using Tukey’s Honest Signicant Difference (HSD) test, which performs all pairwise
comparisons while adjusting for multiple testing. This option is useful when no control group is
dened or when the user wants to explore the full range of pairwise differences across all group
combinations.
The output of summarize _ anova _ results is a tidy DataFrame where each row cor-
responds to a gene and a group comparison. It includes the gene name, the compared group and
baseline, the log2 fold change, Cohen’s d, eta-squared, the A NOVA p-value, and an adjusted p-value
that accounts for multiple testing using the Benjamini-Hochberg method. If Tukey post hoc analysis
is enabled, the output includes all pairwise comparisons with their respective condence intervals and signicance results. This detailed output structure makes it well-suited for downstream
RNA Sequencing 185
TABLE 5.4
Differential Gene Expression Results for Ethnic Groups Relative to “White”
Gene Group Baseline Log2FC Cohen_d Eta_squared p-Value Adj-p
Gene1 Asian White 0.58 0.83 0.17 0.004 0.021
Gene1 Black White 0.12 0.23 0.17 0.004 0.021
Gene2 Asian White 1.04 1.11 0.24 0.001 0.01
Gene2 Black White 0.72 0.78 0.24 0.001 0.01
interpretation, visualization, or integration into gene prioritization workows in studies involving
complex biological groupings.
The run _ summarize _ anova.py program is a complete pipeline for performing differen-
tial gene expression analysis using one-way ANOVA on normalized RNA-Seq data. It is designed
to be modular, exible, and suitable for analyzing experiments with more than two groups. The
program begins by reading a normalized count matrix in which rows represent genes and columns
represent samples. This matrix is typically generated through preprocessing steps such as alignment, quantication, and normalization (e.g., using CPM). Alongside the expression matrix, the
program reads a study design le in CSV format that contains metadata for each sample, including
a unique identier (runID) and several categorical or numeric descriptors such as Ethnic, anti-CCP,
or pathotype.
Once the input les are loaded, the program calls the summarize _ anova _ results func-
tion, which performs ANOVA-based statistical testing and calculates additional metrics such as
log2 fold change, Cohen’s d, and eta-squared (η2). The user can specify which column from the
design le should be used as the grouping variable. For example, setting group _ column =
'Ethnic' instructs the program to test whether gene expression varies signicantly across different ethnic groups. If a control group is dened, such as “White”, the program calculates the log2
fold change and effect size for all other groups relative to that baseline. Alternatively, if no control
group is provided and post hoc testing is enabled, the function performs pairwise comparisons using
Tukey’s HSD to identify specic group differences with adjusted p-values.
The output of the program is a CSV le that can be used for further analysis or reporting. In a
typical use case where a control group is specied, the output table might look like Table 5.4.
In this example, each gene is tested against multiple ethnic groups with respect to the control
group. The table includes the effect size and the proportion of expression variance explained by
ethnicity, providing insight into both statistical and biological relevance.
In another use case, where post hoc testing is enabled and no control group is specied, the output table changes format to reect all pairwise comparisons like Table 5.5.
This output lists the estimated mean difference between groups for each gene, along with con-
dence intervals, adjusted p-values, and a ag indicating whether the difference is statistically
signicant (reject = True). Such output is particularly useful for exploring all possible group relationships when no natural control group exists.
TABLE 5.5
Tukey’s HSD Pairwise Comparisons of Gene Expression across Ethnic Groups
Gene Group1 Group2 Meandiff p-Adj Lower Upper Reject
Gene1 Asian Black 0.45 0.03 0.02 0.88 TRUE
Gene1 Asian White 0.38 0.07
Gene1 Black White
−0.07
0.89
−0.01
−0.47
0.77 FALSE
0.33 FALSE
186 Bioinformatics of Autoimmune Diseases
Overall, the run _ summarize _ anova.py program offers a structured and extensible
framework for detecting and interpreting differential gene expression in studies involving complex
groupings, and its outputs are suitable for both technical validation and biological discovery.
5.5.7.3 Two-Way ANOVA
Two-way ANOVA is a powerful statistical method used in RNA-Seq data analysis to evaluate the
inuence of two independent categorical variables on gene expression, as well as their interaction.
Unlike one-way ANOVA, which considers only a single factor, two-way ANOVA allows researchers
to explore not only the main effects of each factor individually but also whether there is a signicant
interaction effect between them. This makes it particularly useful in complex biological studies
where multiple variables may jointly inuence the transcriptional landscape.
In the context of RNA-Seq, two-way ANOVA is implemented by modeling the normalized
expression values of each gene across samples, using a linear model that includes both factors
and their interaction term. For example, in a study design involving autoimmune disease patients,
researchers might be interested in understanding how gene expression is affected by both the
patient’s ethnic background and their anti-CCP antibody status. By applying two-way ANOVA,
each gene is tested for changes in expression attributable to ethnicity, to anti-CCP status, and to the
interaction between these two factors. The analysis typically involves tting a model of the form
expression ~ factor1 + factor2 + factor1:factor2 and evaluating the signicance
of each term using F-tests.
The use of two-way ANOVA is especially valuable in autoimmune disease research because
these conditions often involve multiple contributing factors, both genetic and environmental, that
may interact in complex ways. For instance, autoimmune responses may manifest differently across
ethnic groups or may vary in severity depending on specic biomarkers like anti-CCP or rheumatoid factor status. By using two-way ANOVA, researchers can disentangle these effects and identify
genes whose expression is modulated not only by individual factors but also by the combination of
factors. This can lead to more nuanced insights into disease mechanisms, potential diagnostic markers, and therapeutic targets that are specic to patient subgroups dened by combinations of clinical
or demographic variables.
The two-way ANOVA program “rna _ pipeline _ 2wAnova.py” developed for RNA-Seq
analysis is a Python-based pipeline designed to identify genes whose expression levels are signicantly inuenced by two categorical variables and their interaction. This is especially useful
in biological studies where gene expression is modulated by multiple conditions or patient-specic
features. The pipeline begins by either reading in real RNA-Seq data or generating synthetic data
for simulation purposes. It ensures that the expression matrix is properly normalized, using CPM,
and that the sample metadata contains relevant groupings, such as ethnic background and clinical
status like anti-CCP positivity. The design and expression data are then aligned to ensure consistency between sample identiers.
At the heart of the analysis lies the two _ way _ anova function, which performs gene-wise sta-
tistical testing using a linear model. For each gene, it ts an ordinary least squares model incorporating
both main effects (e.g., ethnic group and anti-CCP status) and their interaction term. This allows the
pipeline to quantify whether a gene’s expression is affected by one factor independently, by the other,
or by a combined interaction of the two. The function collects p-values for each effect and then applies
the Benjamini-Hochberg procedure to adjust for multiple testing, yielding adjusted p-values for the
main effects and interaction term. These results are collated into a DataFrame and sorted based on the
signicance of the interaction effect, which is often of greatest interest in complex diseases.
A sample output from the two-way ANOVA is shown in Table 5.6. Each row represents a gene and
includes p-values and adjusted p-values for the ethnic group, anti-CCP status, and their interaction.
This table highlights how specic genes respond to the studied variables. For instance, Gene42
shows signicant effects for both ethnic group and interaction, suggesting its expression is inuenced not only by group membership but also by how ethnicity and anti-CCP status interact. Such
187 RNA Sequencing
TABLE 5.6
Two-Way ANOVA Results for Gene Expression across Ethnic Groups and Anti-CCP Status
Gene Ethnic_p Anti-CCP_p Interaction_p Ethnic_adj-p Anti-CCP_adj-p Interaction_adj-p
Gene42 0.0012 0.045 0.0035 0.009 0.087 0.018
Gene119 0.032 0.0021 0.089 0.045 0.017 0.135
Gene792 0.0008 0.054 0.0012 0.007 0.091 0.01
ndings are particularly valuable in autoimmune disease research, where genetic background and
immune markers often work together to shape disease progression and treatment response.
To enhance interpretability, the pipeline also includes visualizations such as volcano plots, heatmaps, and expression boxplots. The heatmap function, in particular, has been adapted to display
group annotations based on the combination of both categorical variables, with color-coded legends
that explain group identities. This visualization helps researchers quickly observe clustering patterns
and validate whether signicant genes show biologically meaningful differences across groups.
5.5.8 VISUALIZING DIFFERENTIAL GENE EXPRESSION IN RNA-SEQ DATA
Visualizing differential gene expression in RNA-Seq data is a crucial step in understanding how
gene activity varies under different biological conditions. Once raw sequencing reads are processed
and aligned to a reference genome, statistical methods are used to identify genes that show signicant changes in expression levels between groups, such as diseased versus healthy tissues or
treated versus untreated samples. However, these numerical results alone can be overwhelming and
difcult to interpret. Visualization helps transform complex datasets into intuitive and insightful
representations, allowing researchers to detect patterns, assess data quality, and draw biologically
meaningful conclusions. From heatmaps that cluster genes and samples based on expression proles
to volcano plots that highlight the magnitude and signicance of gene expression changes, each
visualization technique offers a different lens through which to explore the data. Effective visualizations not only enhance the interpretability of results but also serve as a powerful communication
tool in both scientic publications and presentations.
In the following, we discuss several visualization plots that are commonly used for illustrating
differential expression in RNA-Seq data.
5.5.8.1 PCA Plot
The PCA (principal component analysis) plot in RNA-Seq analysis is a dimensionality reduction
technique used to visualize patterns in high-dimensional gene expression data. It transforms the
original gene expression matrix, where each sample is represented by thousands of gene expression
values, into a set of uncorrelated variables called principal components. These components are calculated based on the directions of maximum variance in the data, with the rst principal component
capturing the greatest variation, followed by the second, and so on. To perform PCA, the expression
matrix is typically normalized and standardized to ensure comparability across samples. The result
is a scatter plot where each point represents a sample, positioned according to its values in the rst
two (or more) principal components. The PCA plot helps researchers assess sample clustering, identify outliers, detect batch effects, and understand the inuence of experimental conditions. Samples
that cluster closely together are considered to have similar gene expression proles, suggesting similar biological states or treatments, while separation along the principal components may indicate
signicant variation due to disease, treatment, or other experimental factors.
The PCA plot program “plot _ pc a.py” is designed to visualize RNA-Seq gene expression
data by reducing its complexity and highlighting underlying patterns among samples. It begins by
taking a gene expression matrix, where rows represent genes and columns represent samples, along
188 Bioinformatics of Autoimmune Diseases
with a metadata le containing sample-specic information such as treatment group or experimental condition. To prepare the data for PCA, the expression matrix is transposed so that each sample
becomes a row, which aligns with the expectations of most machine learning algorithms. The data
is then standardized using z-score normalization to ensure that each gene contributes equally to the
analysis, regardless of its absolute expression level.
After standardization, the program applies principal component analysis to identify directions
of maximum variance in the dataset. It reduces the high-dimensional space to two principal components, which are used to plot each sample as a point in a two-dimensional scatter plot. Each point is
colored based on a user-dened metadata grouping variable, such as condition or treatment, making
it easy to observe whether samples from the same group cluster together. Additionally, the program
labels each point with the corresponding sample name, using a smaller font to avoid visual clutter
while still providing clarity.
The expected output of the program is a high-resolution PNG le containing the PCA plot. This
plot includes clear axes labeled with the percentage of variance explained by the rst two principal
components, a legend indicating the groupings used for coloring, and a descriptive title. The visual representation helps researchers quickly identify clusters of similar samples, detect outliers, and evaluate
whether biological replicates group as expected. This kind of visualization is crucial in RNA-Seq studies for quality control and to guide further analysis, such as differential expression or batch correction.
In the PCA plot (Figure 5.2), the spatial distribution of points reects the similarity of gene expression proles across samples. Samples that cluster closely together share similar overall expression
FIGURE 5.2 PCA plot of RNA-Seq gene expression data of samples grouped by a nt i - CC P.
189 RNA Sequencing
patterns, while samples that are further apart differ signicantly in their transcriptomic proles.
The clear separation between anti-CCP-positive and anti-CCP-negative samples suggests that the
anti-CCP condition contributes signicantly to the observed variance in gene expression. This supports the hypothesis that anti-CCP status inuences transcriptional activity in RAand validates the
effectiveness of PCA as a tool for identifying biologically meaningful groupings in RNA-Seq data.
5.5.8.2 Volcano Plot
A volcano plot is a type of scatter plot that is commonly used in genomics and transcriptomics to
visualize differential expression data. It displays the relationship between statistical signicance
and the magnitude of change for each feature, such as genes or transcripts, across different experimental conditions. The x-axis of a volcano plot represents the log2 fold change (log2FC), which
quanties the magnitude of difference in expression between two groups. The y-axis represents the
negative logarithm (base 10) of the p-value or adjusted p-value, typically derived from a statistical
test like a t-test or ANOVA. This transformation ensures that features with the most statistically
signicant differences appear toward the top of the plot.
To calculate the values for a volcano plot, gene expression data is rst normalized and then
subjected to a differential expression analysis using statistical models. Each gene is tested to determine whether its expression differs signicantly between experimental groups. The fold change is
calculated as the ratio of mean expression levels between the groups, and the log2 transformation
is applied to center the distribution around zero (upregulated genes fall on the right and downregulated genes on the left). The p-values from the statistical tests are transformed using −log10 to
emphasize small values, which indicate higher signicance.
Plotting the volcano plot involves creating a scatter plot where each point corresponds to a gene.
The x-coordinate of each point is the log2 fold change, and the y-coordinate is the −log10 of the
p-value. Genes that are both highly differentially expressed and statistically signicant appear as
outliers in the upper left and upper right corners of the plot. Researchers often add threshold lines
to indicate cutoffs for fold change and signicance (e.g., log2FC greater than 1 or less than −1 and
p-value less than 0.05) highlighting genes that pass these criteria in different colors. The resulting
volcano plot provides an intuitive visual summary of the data, making it easier to identify genes of
interest for further analysis.
The volcano plot program “plot _ volc ano.p y” provides a clear and intuitive visual sum-
mary of differential gene expression results from RNA-Seq experiments. In Figure 5.3, gene
names have been anonymized (e.g., gene1, gene2) to indicate that the results are illustrative and not
intended to represent biologically validated ndings. The core of the volcano plot lies in its ability
to show both the magnitude and statistical signicance of gene expression changes. Each gene is
plotted with its log2 fold change on the x-axis and the negative log10 of the adjusted p-value on the
y-axis. This layout allows researchers to easily identify genes that are both statistically signicant
and biologically relevant.
Genes are classied into categories such as “Upregulated”, “Downregulated”, or “Not signicant” based on predetermined thresholds for fold change and p-value. These categories are then
visually represented using distinct colors (red for upregulated, blue for downregulated, and grey for
non-signicant genes) making it easy to discern expression patterns at a glance. Additional lines are
drawn to indicate the thresholds used for signicance, which helps to contextualize the position of
each gene on the plot.
To enhance interpretability, the script also identies and labels the top ten most signicant genes
based on adjusted p-values. These genes are annotated directly on the plot, aiding in the quick
identication of key candidates for further analysis or validation. The plot is styled for high-quality
output with increased font sizes for labels, legends, and axis titles, making it suitable for both publications and presentations. The nal image is saved in PNG format and is also viewable interactively.
This kind of visualization is particularly useful in understanding complex transcriptomic changes in
diseases like autoimmune diseases, where many genes may be subtly or strongly affected.
190 Bioinformatics of Autoimmune Diseases
5.5.8.3 MA Plot
The MA plot (minus-average plot) is a powerful visualization used in RNA-Seq differential expression analysis to examine the relationship between the magnitude of gene expression (A) and the
change in expression between experimental conditions (M). In the context of RNA-Seq, the A value
represents the average expression of a gene across all samples, often calculated as the log2-transformed mean of CPM. The M value corresponds to the log2 fold change (log2FC) between two
groups, such as disease versus control. Each gene is plotted as a point with its A value on the x-axis
and its M value on the y-axis. This allows researchers to quickly assess whether genes with high or
low expression levels are differentially expressed between conditions.
To generate the MA plot, normalized expression values are rst obtained for all genes. The average expression for each gene is calculated, and a small constant is added to avoid issues with log
transformation. The log2 fold change is derived from statistical testing, typically using t-tests or
generalized linear models. Genes are then categorized based on statistical signicance thresholds,
such as an adjusted p-value (FDR) cutoff and a log2FC threshold. On the plot, genes that meet these
criteria for upregulation or downregulation are highlighted in distinct colors, while non-signicant
genes are typically shown in grey. Horizontal lines are often added to indicate fold change thresholds and the y = 0 line represents no change between conditions.
MA plots are particularly useful because they highlight systematic patterns and biases in RNASeq data. For example, a skewed MA plot may suggest normalization issues or batch effects, while
a balanced distribution of signicant genes around the y = 0 line indicates consistent differential expression. Importantly, the MA plot emphasizes how differential expression varies across the
dynamic range of expression levels, something that volcano plots, which focus on p-values, may
obscure.
In the context of autoimmune diseases, MA plots can be especially benecial for visualizing
gene expression differences between patients and healthy controls. Many autoimmune conditions,
191 RNA Sequencing
such as rheumatoid arthritis or lupus, involve complex immune responses that alter gene activity. The MA plot helps identify which genes are upregulated (e.g., cytokines, chemokines, and
inammatory regulators) or downregulated in affected individuals. This visual distinction enables
researchers to focus on genes with both biologically meaningful changes and robust expression
levels. Ultimately, MA plots contribute to a deeper understanding of disease mechanisms, facilitate
biomarker discovery, and guide the identication of potential therapeutic targets by clearly presenting the transcriptional landscape associated with autoimmunity.
The Python code “plot _ MA.p y” for the MA plot is designed to visualize differential gene
expression from RNA-Seq data. It uses log2 fold change as the vertical axis (M) and log2-transformed
average gene expression as the horizontal axis (A). The input consists of a le containing differential expression statistics, including adjusted p-values and log2 fold changes, and a normalized
count matrix. The code rst ensures that gene expression values are uniquely indexed by collapsing
duplicate gene symbols via averaging. It then computes the average expression for each gene across
all samples and applies a log2 transformation with a small offset to avoid innite values.
Each gene is classied as upregulated, downregulated, or not signicant based on thresholds set
for both adjusted p-value and fold change. By default, a gene is considered signicantly upregulated
if its log2 fold change exceeds 1 and its adjusted p-value is less than 0.05, and similarly considered
downregulated if the log2 fold change is less than −1 with a signicant p-value. If neither condition
is met, the gene is labeled not signicant. These categories are visualized using different colors: red
for upregulated, blue for downregulated, and grey for non-signicant genes. The code also selects
the top most signicant genes based on their adjusted p-values and adds their gene symbols as text
labels on the plot to highlight them. Horizontal dashed lines are added to mark the fold change
thresholds, and a solid line is drawn at zero to indicate no change.
When interpreting the resulting MA plot (Figure 5.4), genes that lie far above or below the center
line with red or blue coloring indicate strong differential expression with statistical support. In the
FIGURE 5.4 MA plot showing differential gene expression in rheumatoid arthritis.
192 Bioinformatics of Autoimmune Diseases
gure, gene names have been anonymized (e.g., gene1, gene2) to indicate that the results are illustrative and not intended to represent biologically validated ndings. These genes are typically of most
biological interest. However, a common reason why many genes may appear grey, even those that
visually cross the threshold lines, is that their adjusted p-values do not meet the specied signicance threshold. This is because the code enforces a dual condition for signicance: the gene must
exceed both the fold change cutoff and the p-value cutoff to be considered up- or downregulated.
if row['adj-p'] < pval_thresh:
if row['log2FC'] > fc_thresh:
return 'Upregulated'
elif row['log2FC'] < -fc_thresh:
return 'Downregulated'
Therefore, the color coding provides not just a visual sense of expression magnitude but also
integrates statistical reliability, making the plot an effective tool for prioritizing candidate genes in
further rheumatoid arthritis research.
5.5.8.4 Heatmap
The heatmap for RNA-Seq data provides a visual summary of the expression patterns of selected
genes across multiple samples, allowing for the identication of distinct expression proles and
potential sample clustering. In this context, the heatmap displays the normalized expression levels
of the top differentially expressed genes, standardized across samples to highlight relative differences. Each row corresponds to a gene, and each column represents a sample, with color gradients
indicating the level of gene expression. Clustering algorithms are applied to both genes and samples
to group those with similar expression proles, making it easier to observe patterns that may be
associated with biological conditions such as disease status or treatment response. This visual representation is especially useful in identifying gene expression signatures that differentiate between
experimental groups.
The plot _ heatm ap.py program generates a clustered heatmap to visualize the expression
patterns of a selected set of top differentially expressed genes from RNA-Seq data. It begins by subsetting the normalized expression matrix to include only the specied genes and then standardizes
the expression values across samples to emphasize relative differences. The standardized data is
transposed so that samples become rows and genes become columns, which is a format compatible
with clustering. A hierarchical clustered heatmap is then created using seaborn’s clustermap, applying a diverging colormap to distinguish high and low expression levels. The function also ensures
that the output directory exists and saves the plot as a high-resolution PNG le. This visualization
helps in exploring how genes and samples group together based on expression patterns, which can
be indicative of underlying biological conditions.
This heatmap (Figure 5.5) displays the standardized expression levels of the top ten differentially
expressed genes across 20 samples, with 10 samples from anti-CCP positive individuals and 10 from
anti-CCP negative individuals. Rows represent samples, and columns represent genes, with color
intensity indicating the relative expression level of each gene. Clustering has been applied to both
genes and samples, revealing distinct expression patterns that differentiate between the two groups,
highlighting potential biomarkers or regulatory pathways associated with rheumatoid arthritis.
5.5.8.5 Gene Expression Plot
The plot _ gene _ expression.py program is designed to visualize the expression levels of a
specic gene across different sample groups in an RNA-Seq dataset. It takes as input a normalized
count matrix (typically containing log2-transformed CPM values) and a metadata le that contains
information about each sample, such as group labels for experimental conditions. The primary purpose of the program is to generate a boxplot overlaid with individual data points, allowing users to
clearly observe the distribution and variability of gene expression within and between dened groups.
193 RNA Sequencing
FIGURE 5.5 Heatmap of the top ten differentially expressed genes in RA RNA-Seq data.
The program begins by loading the normalized counts and metadata and then extracts expression
values for a gene specied by the user. It maps each sample to its corresponding group using the
metadata le, enabling group-wise comparison. The resulting plot includes a boxplot that summarizes the central tendency and spread of expression for each group, while a strip plot of individual
data points is layered on top to show the underlying distribution. This combination offers both statistical and granular views of the gene’s behavior across conditions.
The output of the program (Figure 5.6) is a PNG image of the expression plot, saved with a
lename that includes the gene’s name. The plot is annotated with axis labels and a title for clear
interpretation, and the style is optimized for readability with appropriate font sizes and spacing.
By examining this plot, researchers can assess whether a gene is differentially expressed between
groups, whether the variation is consistent across samples, and whether potential outliers exist. This
kind of visualization is particularly useful in biomedical research, including autoimmune diseases,
where the differential expression of certain genes may indicate immune activation, inammation, or
therapeutic response. Such expression plots serve as a vital bridge between statistical analysis and
biological interpretation.
5.5.9 FUNCTIONAL ENRICHMENT
Functional enrichment analysis is a powerful approach used to interpret large lists of genes, typically derived from differential expression studies, by identifying biological themes, pathways, or
Соседние файлы в папке Библиотека им академика М.И. Перельмана