测序之后的差异基因分析和分组展示是一个必须的过程，好的分析可以把结果展示的美观具有极强的可读性。但是在实际的操作中经过反复的转换也是一个麻烦事，关键是要达到很好对大多数人来说还是需要下一番功夫。好吧，就交给今天的主角来干这件事吧。只能说翻译也是一个体力活，请自选————————

摘要

差异基因表达 (DGE) 是 RNA 测序 (RNA-seq) 数据最常见的应用之一。该过程允许在两个或多个条件下阐明差异表达的基因 (DEG)。由于基于所选工具的各种格式以及这些结果文件中提供的大量信息，对 DGE 结果的解释可能不直观且耗时。在这里，我们展示了一个 R 包 ViDGER（使用 R 可视化差异基因表达结果），其中包含九个函数，这些函数生成信息丰富的可视化，用于解释来自三个广泛使用的工具 Cuffdiff、DESeq2 和 edgeR 的 DGE 结果。

R语言入群

R语言群，不知怎么的入群的人很少啊，请大家支持呐--

示例 S1：安装和数据示例

此软件包的稳定版本可在 Bioconductor上获得。您可以通过运行以下命令来安装它：

if (!requireNamespace("BiocManager", quietly=TRUE))
    install.packages("BiocManager")
BiocManager::install("vidger")

可以使用 devtools 包通过 GitHub 安装 ViDGER 的最新开发版本

if (!require("devtools")) install.packages("devtools")
devtools::install_github("btmonier/vidger", ref = "devel")

Onc安装后，您将可以访问以下功能：

vsBoxplot()
vsScatterPlot()
vsScatterMatrix()
vsDEGMatrix()
vsMAPlot()
vsMAMatrix()
vsVolcano()
vsVolcanoMatrix()
vsFourWay()

在下面的示例中，将使用三个测试数据集：df.cuff、df.deseq 和 df.edger。这些数据集中的每一个都反映了该软件包涵盖的三个 RNA-seq 分析。这些可以使用以下命令加载到 R 工作区中：

data(<data_set>)

其中<data_set> 是前面提到的数据集之一。在这些函数中的每一个中找到的一些重复元素是 type 和 d.factor 参数。类型参数告诉函数如何处理每种分析类型的数据（即“cuffdiff”、“deseq”或“edge”）。 d.factor 参数专门用于 DESeq2 对象，我们将在 DESeq2 部分讨论这些对象。通过查看每个函数的相应帮助文件（比如？vsScatterPlot），将进一步详细讨论所有其他参数。

所用数据概览

如前所述，此包中包含三个玩具数据集。除了这些数据集之外，还使用了 5 个“真实世界”数据集。目前使用的所有真实世界数据都未从正在进行的合作中发布。这些数据的摘要可以在下表中找到：

表 1：此包中包含的测试数据集概述。在此表中，根据使用的分析软件、生物体 ID、实验布局（重复和处理）、转录本 (ID) 的数量以及以兆字节 (MB) 为单位的数据对象大小对每个数据集进行了总结。

数据	软件	物种	重复	处理.	IDs	大小(MB)
`df.cuff`	CuffDiff	H	2	3	1200	0.2
		sapiens
`df.deseq`	DESeq2	D.	2	3	29391	2.3
		melanogaster
`df.deseq`	edgeR	A.	2	3	724	0.1
		thaliana

表 2：“真实世界”（RW）数据集统计数据。为了测试我们包装的可靠性，我们使用了来自人类收藏和几个植物样本的真实数据。每个数据集都根据生物体 ID、实验样本数 (n)、实验条件和转录本 (ID) 数进行总结。

数据	物种	个数	实验.条件	IDs
RW-1	H.	10	Two treatment dosages taken at two	198002
	sapiens		time points and one control sample
			taken at one time point
RW-2	M.	24	Two phenotypes taken at four time	63517
	domestia		points (three replicates each)
RW-3	V.	6	Two conditions (three replicates	59262
	ripria:		each).
	bud
RW-4	V.	6	Two conditions (three replicates	17962
	ripria:		each).
	shoot-tip
	(7 days)
RW-5	V.	6	Two conditions (three replicates	19064
	ripria:		each).
	shoot-tip
	(21 days)

示例 S2：创建箱线图

箱线图是一种确定数据分布的有用方法。在这种情况下，我们可以使用 vsBoxPlot() 函数来确定 FPKM 或 CPM 值的分布。此功能允许您从分析对象中提取必要的基于结果的数据，以创建比较实验处理的 log10log10（FPKM 或 CPM）分布的箱线图。

使用Cuffdiff

vsBoxPlot(
    data = df.cuff, d.factor = NULL, type = 'cuffdiff', title = TRUE, 
    legend = TRUE, grid = TRUE
)

图 1：使用 vsBoxPlot() 函数的箱线图示例 cuffdiff 数据。在此示例中，实验中每个处理的 FPKM 分布以箱线图的形式显示。

使用DESeq2

vsBoxPlot(
    data = df.deseq, d.factor = 'condition', type = 'deseq', 
    title = TRUE, legend = TRUE, grid = TRUE
)

图 2：使用 vsBoxPlot() 函数的箱线图示例 DESeq2 数据。在此示例中，实验中每个处理的 FPKM 分布以箱线图的形式显示。

使用edgeR

vsBoxPlot(
    data = df.edger, d.factor = NULL, type = 'edger', 
    title = TRUE, legend = TRUE, grid = TRUE
)

F图 3：使用 vsBoxPlot() 函数和 edgeR 的箱线图示例数据。在此示例中，实验中每个处理的 CPM 分布以箱线图的形式显示

箱形图的美学优化

vsBoxPlot() 可以允许不同的迭代来展示数据分布。这些更改可以使用 aes 参数实现。目前，有 6 种不同的变体：

box: standard box plot
violin: violin plot
boxdot: box plot with dot plot overlay
viodot: violin plot with dot plot overlay
viosumm: violin plot with summary stats overlay
notch: box plot with notch

`box` variant

data("df.edger")
vsBoxPlot(
   data = df.edger, d.factor = NULL, type = "edger", title = TRUE,
   legend = TRUE, grid = TRUE, aes = "box"
)

图 4：使用 aes 和box参数的箱线图示例

`violin` variant

data("df.edger")
vsBoxPlot(
   data = df.edger, d.factor = NULL, type = "edger", title = TRUE,
   legend = TRUE, grid = TRUE, aes = "violin"
)

图 5：使用 aes 和violin参数的箱线图示例

`boxdot` variant

data("df.edger")
vsBoxPlot(
   data = df.edger, d.factor = NULL, type = "edger", title = TRUE,
   legend = TRUE, grid = TRUE, aes = "boxdot"
)

图 6：使用 aes 和boxdot参数的箱线图示例：

`viodot` variant

data("df.edger")
vsBoxPlot(
   data = df.edger, d.factor = NULL, type = "edger", title = TRUE,
   legend = TRUE, grid = TRUE, aes = "viodot"
)

图 7：使用 aes 和viodot参数的箱线图示例：

`viosumm` variant

data("df.edger")
vsBoxPlot(
   data = df.edger, d.factor = NULL, type = "edger", title = TRUE,
   legend = TRUE, grid = TRUE, aes = "viosumm"
)

图 8：使用 aes 参数的箱线图示例：viosumm

`notch` variant

data("df.edger")
vsBoxPlot(
   data = df.edger, d.factor = NULL, type = "edger", title = TRUE,
   legend = TRUE, grid = TRUE, aes = "notch"
)

图 9：使用 aes 和notch参数的箱线图示例：缺口

箱形图的调色板变体

除了美学上的变化，每个变体的填充颜色也可以改变。这可以通过修改 fill.color 参数来实现。

The palettes that can be used for this parameter are based off of the palettes found in the RColorBrewer A visual list of all the palettes can be found . 可用于此参数的调色板基于 RColorBrewer package包中的调色板。可以在此处here找到所有调色板的可视化列表。

颜色变体示例 1

data("df.edger")
vsBoxPlot(
   data = df.edger, d.factor = NULL, type = "edger", title = TRUE,
   legend = TRUE, grid = TRUE, aes = "box", fill.color = "RdGy"
)

Figure 10: Color variant 1
A box plot example using the fill.color parameter: RdGy.

Color variant example 2

data("df.edger")
vsBoxPlot(
   data = df.edger, d.factor = NULL, type = "edger", title = TRUE,
   legend = TRUE, grid = TRUE, aes = "viosumm", fill.color = "Paired"
)

Figure 11: Color variant 2
A violin plot example using the fill.color parameter: Paired with the aes parameter: viosumm.

Color variant example 3

data("df.edger")
vsBoxPlot(
   data = df.edger, d.factor = NULL, type = "edger", title = TRUE,
   legend = TRUE, grid = TRUE, aes = "notch", fill.color = "Greys"
)

Figure 12: Color variant 3
A notched box plot example using the fill.color parameter: Greys with the aes parameter: notch. Using these parameters, we can also generate grey-scale plots.

Example S3: Creating scatter plots

This example will look at a basic scatter plot function, vsScatterPlot(). This function allows you to visualize comparisons of log10log10 values of either FPKM or CPM measurements of two treatments depending on analytical type.

With Cuffdiff

vsScatterPlot(
    x = 'hESC', y = 'iPS', data = df.cuff, type = 'cuffdiff',
    d.factor = NULL, title = TRUE, grid = TRUE
)

$A scatterplot example using the `vsScatterPlot()` function with `Cuffdiff` data. In this visualization, $log_{10}$ comparisons are made of fragments per kilobase of transcript per million mapped reads (FPKM) measurments. The dashed line represents regression line for the comparison.$

Figure 13: A scatterplot example using the vsScatterPlot() function with
Cuffdiff data. In this visualization, log10log10 comparisons are made of fragments per kilobase of transcript per million mapped reads (FPKM) measurments. The dashed line represents regression line for the comparison.

With DESeq2

vsScatterPlot(
    x = 'treated_paired.end', y = 'untreated_paired.end', 
    data = df.deseq, type = 'deseq', d.factor = 'condition', 
    title = TRUE, grid = TRUE
)

$A scatterplot example using the `vsScatterPlot()` function with `DESeq2` data. In this visualization, $log_{10}$ comparisons are made of fragments per kilobase of transcript per million mapped reads (FPKM) measurments. The dashed line represents regression line for the comparison.$

Figure 14: A scatterplot example using the vsScatterPlot() function with
DESeq2 data. In this visualization, log10log10 comparisons are made of fragments per kilobase of transcript per million mapped reads (FPKM) measurments. The dashed line represents regression line for the comparison.

With edgeR

vsScatterPlot(
    x = 'WM', y = 'MM', data = df.edger, type = 'edger',
    d.factor = NULL, title = TRUE, grid = TRUE
)

$A scatterplot example using the `vsScatterPlot()` function with `edgeR` data. In this visualization, $log_{10}$ comparisons are made of fragments per kilobase of transcript per million mapped reads (FPKM) measurments. The dashed line represents regression line for the comparison.$

Figure 15: A scatterplot example using the vsScatterPlot() function with
edgeR data. In this visualization, log10log10 comparisons are made of fragments per kilobase of transcript per million mapped reads (FPKM) measurments. The dashed line represents regression line for the comparison.

Example S4: Creating scatter plot matrices

This example will look at an extension of the vsScatterPlot() function which is vsScatterMatrix(). This function will create a matrix of all possible comparisons of treatments within an experiment with additional info.

With Cuffdiff

vsScatterMatrix(
    data = df.cuff, d.factor = NULL, type = 'cuffdiff', 
    comp = NULL, title = TRUE, grid = TRUE, man.title = NULL
)

Figure 16: A scatterplot matrix example using the vsScatterMatrix()
function with Cuffdiff data. Similar to the scatterplot function, this visualization allows for all comparisons to be made within an experiment. In addition to the scatterplot visuals, FPKM distributions (histograms) and correlation (Corr) values are generated.

With DESeq2

vsScatterMatrix(
    data = df.deseq, d.factor = 'condition', type = 'deseq',
    comp = NULL, title = TRUE, grid = TRUE, man.title = NULL
)

Figure 17: A scatterplot matrix example using the vsScatterMatrix()
function with DESeq2 data. Similar to the scatterplot function, this visualization allows for all comparisons to be made within an experiment. In addition to the scatterplot visuals, FPKM distributions (histograms) and correlation (Corr) values are generated.

With edgeR

vsScatterMatrix(
    data = df.edger, d.factor = NULL, type = 'edger', comp = NULL,
    title = TRUE, grid = TRUE, man.title = NULL
)

Figure 18: A scatterplot matrix example using the vsScatterMatrix()
function with edgeR data. Similar to the scatterplot function, this visualization allows for all comparisons to be made within an experiment. In addition to the scatterplot visuals, FPKM distributions (histograms) and correlation (Corr) values are generated.

Example S5: Creating differential gene expression matrices

Using the vsDEGMatrix() function allows the user to visualize the number of differentially expressed genes (DEGs) at a given adjusted p-value (padj = ) for each experimental treatment level. Higher color intensity correlates to a higher number of DEGs.

With Cuffdiff

vsDEGMatrix(
    data = df.cuff, padj = 0.05, d.factor = NULL, type = 'cuffdiff', 
    title = TRUE, legend = TRUE, grid = TRUE
)

Figure 19: A matrix of differentially expressed genes (DEGs) at a given
p-value using the vsDEGMatrix() function with Cuffdiff data. With this function, the user is able to visualize the number of DEGs ata given adjusted p-value for each experimental treatment level. Higher color intensity correlates to a higher number of DEGs.

With DESeq2

vsDEGMatrix(
    data = df.deseq, padj = 0.05, d.factor = 'condition', 
    type = 'deseq', title = TRUE, legend = TRUE, grid = TRUE
)

Figure 20: A matrix of differentially expressed genes (DEGs) at a given
p-value using the vsDEGMatrix() function with DESeq2 data. With this function, the user is able to visualize the number of DEGs ata given adjusted p-value for each experimental treatment level. Higher color intensity correlates to a higher number of DEGs.

With edgeR

vsDEGMatrix(
    data = df.edger, padj = 0.05, d.factor = NULL, type = 'edger', 
    title = TRUE, legend = TRUE, grid = TRUE
)

Figure 21: A matrix of differentially expressed genes (DEGs) at a given
p-value using the vsDEGMatrix() function with edgeR data. With this function, the user is able to visualize the number of DEGs ata given adjusted p-value for each experimental treatment level. Higher color intensity correlates to a higher number of DEGs.

Grey-scale DEG matrices

A grey-scale option is available for this function if you wish to use a grey-to-white gradient instead of the classic blue-to-white gradient. This can be invoked by setting the grey.scale parameter to TRUE.

vsDEGMatrix(data = df.deseq, d.factor = "condition", type = "deseq",
    grey.scale = TRUE
)

Example S6: Creating MA plots

vsMAPlot() visualizes the variance between two samples in terms of gene expression values where logarithmic fold changes of count data are plotted against mean counts. For more information on how each of the aesthetics are plotted, please refer to the figure captions and Method S1.

With Cuffdiff

vsMAPlot(
    x = 'iPS', y = 'hESC', data = df.cuff, d.factor = NULL, 
    type = 'cuffdiff', padj = 0.05, y.lim = NULL, lfc = NULL, 
    title = TRUE, legend = TRUE, grid = TRUE
)

Figure 22: MA plot visualization using the vsMAPLot() function with
Cuffdiff data. LFCs are plotted mean counts to determine the variance between two treatments in terms of gene expression. Blue nodes on the graph represent statistically significant LFCs which are greater than a given value than a user-defined LFC parameter. Green nodes indicate statistically significant LFCs which are less than the user-defined LFC parameter. Gray nodes are data points that are not statistically significant. Numerical values in parantheses for each legend color indicate the number of transcripts that meet the prior conditions. Triangular shapes represent values that exceed the viewing area of the graph. Node size changes represent the magnitude of the LFC values (i.e. larger shapes reflect larger LFC values). Dashed lines indicate user-defined LFC values.

With DESeq2

vsMAPlot(
    x = 'treated_paired.end', y = 'untreated_paired.end', 
    data = df.deseq, d.factor = 'condition', type = 'deseq', 
    padj = 0.05, y.lim = NULL, lfc = NULL, title = TRUE, 
    legend = TRUE, grid = TRUE
)

Figure 23: MA plot visualization using the vsMAPLot() function with
DESeq2 data. LFCs are plotted mean counts to determine the variance between two treatments in terms of gene expression. Blue nodes on the graph represent statistically significant LFCs which are greater than a given value than a user-defined LFC parameter. Green nodes indicate statistically significant LFCs which are less than the user-defined LFC parameter. Gray nodes are data points that are not statistically significant. Numerical values in parantheses for each legend color indicate the number of transcripts that meet the prior conditions. Triangular shapes represent values that exceed the viewing area of the graph. Node size changes represent the magnitude of the LFC values (i.e. larger shapes reflect larger LFC values). Dashed lines indicate user-defined LFC values.

With edgeR

vsMAPlot(
    x = 'WW', y = 'MM', data = df.edger, d.factor = NULL, 
    type = 'edger', padj = 0.05, y.lim = NULL, lfc = NULL, 
    title = TRUE, legend = TRUE, grid = TRUE
)

Figure 24: MA plot visualization using the vsMAPLot() function with
edgeR data. LFCs are plotted mean counts to determine the variance between two treatments in terms of gene expression. Blue nodes on the graph represent statistically significant LFCs which are greater than a given value than a user-defined LFC parameter. Green nodes indicate statistically significant LFCs which are less than the user-defined LFC parameter. Gray nodes are data points that are not statistically significant. Numerical values in parantheses for each legend color indicate the number of transcripts that meet the prior conditions. Triangular shapes represent values that exceed the viewing area of the graph. Node size changes represent the magnitude of the LFC values (i.e. larger shapes reflect larger LFC values). Dashed lines indicate user-defined LFC values.

Example S7: Creating MA plot matrices

Similar to a scatter plot matrix, vsMAMatrix() will produce visualizations for all comparisons within your data set. For more information on how the aesthetics are plotted in these visualizations, please refer to the figure caption and Method S1.

With Cuffdiff

 vsMAMatrix(
    data = df.cuff, d.factor = NULL, type = 'cuffdiff', 
    padj = 0.05, y.lim = NULL, lfc = 1, title = TRUE, 
    grid = TRUE, counts = TRUE, data.return = FALSE
)

Figure 25: A MA plot matrix using the vsMAMatrix() function with Cuffdiff
data. Similar to the vsMAPlot() function, vsMAMatrix() will generate a matrix of MA plots for all comparisons within an experiment. LFCs are plotted mean counts to determine the variance between two treatments in terms of gene expression. Blue nodes on the graph represent statistically significant LFCs which are greater than a given value than a user-defined LFC parameter. Green nodes indicate statistically significant LFCs which are less than the user-defined LFC parameter. Gray nodes are data points that are not statistically significant. Numerical values in parantheses for each legend color indicate the number of transcripts that meet the prior conditions. Triangular shapes represent values that exceed the viewing area of the graph. Node size changes represent the magnitude of the LFC values (i.e. larger shapes reflect larger LFC values). Dashed lines indicate user-defined LFC values.

With DESeq2

vsMAMatrix(
    data = df.deseq, d.factor = 'condition', type = 'deseq', 
    padj = 0.05, y.lim = NULL, lfc = 1, title = TRUE, 
    grid = TRUE, counts = TRUE, data.return = FALSE
)

Figure 26: A MA plot matrix using the vsMAMatrix() function with DESeq2
data. Similar to the vsMAPlot() function, vsMAMatrix() will generate a matrix of MA plots for all comparisons within an experiment. LFCs are plotted mean counts to determine the variance between two treatments in terms of gene expression. Blue nodes on the graph represent statistically significant LFCs which are greater than a given value than a user-defined LFC parameter. Green nodes indicate statistically significant LFCs which are less than the user-defined LFC parameter. Gray nodes are data points that are not statistically significant. Numerical values in parantheses for each legend color indicate the number of transcripts that meet the prior conditions. Triangular shapes represent values that exceed the viewing area of the graph. Node size changes represent the magnitude of the LFC values (i.e. larger shapes reflect larger LFC values). Dashed lines indicate user-defined LFC values.

With edgeR

vsMAMatrix(
    data = df.edger, d.factor = NULL, type = 'edger', 
    padj = 0.05, y.lim = NULL, lfc = 1, title = TRUE, 
    grid = TRUE, counts = TRUE, data.return = FALSE
)

Figure 27: A MA plot matrix using the vsMAMatrix() function with edgeR
data. Similar to the vsMAPlot() function, vsMAMatrix() will generate a matrix of MA plots for all comparisons within an experiment. LFCs are plotted mean counts to determine the variance between two treatments in terms of gene expression. Blue nodes on the graph represent statistically significant LFCs which are greater than a given value than a user-defined LFC parameter. Green nodes indicate statistically significant LFCs which are less than the user-defined LFC parameter. Gray nodes are data points that are not statistically significant. Numerical values in parantheses for each legend color indicate the number of transcripts that meet the prior conditions. Triangular shapes represent values that exceed the viewing area of the graph. Node size changes represent the magnitude of the LFC values (i.e. larger shapes reflect larger LFC values). Dashed lines indicate user-defined LFC values.

Example S8: Creating volcano plots

The next few visualizations will focus on ways to display differential gene expression between two or more treatments. Volcano plots visualize the variance between two samples in terms of gene expression values where the −log10−log10 of calculated p-values (y-axis) are a plotted against the log2log2 changes (x-axis). These plots can be visualized with the vsVolcano() function. For more information on how each of the aesthetics are plotted, please refer to the figure captions and Method S1.

With Cuffdiff

vsVolcano(
    x = 'iPS', y = 'hESC', data = df.cuff, d.factor = NULL, 
    type = 'cuffdiff', padj = 0.05, x.lim = NULL, lfc = NULL, 
    title = TRUE, legend = TRUE, grid = TRUE, data.return = FALSE
)

$A volcano plot example using the `vsVolcano()` function with `Cuffdiff` data. In this visualization, comparisons are made between the $-log_{10}$ *p*-value versus the $log_2$ fold change (LFC) between two treatments. Blue nodes on the graph represent statistically significant LFCs which are greater than a given value than a user-defined LFC parameter. Green nodes indicate statistically significant LFCs which are less than the user-defined LFC parameter. Gray nodes are data points that are not statistically significant. Numerical values in parantheses for each legend color indicate the number of transcripts that meet the prior conditions. Left and right brackets (< and >) represent values that exceed the viewing area of the graph. Node size changes represent the magnitude of the LFC values (i.e. larger shapes reflect larger LFC values). Vertical and horizontal lines indicate user-defined LFC and adjusted *p*-values, respectively.” width=”100%”></p> <p>Figure 28: <strong>A volcano plot example using the <code>vsVolcano()</code> function with</strong><br><code>Cuffdiff</code> data. In this visualization, comparisons are made between the −log10−log10 <em>p</em>-value versus the log2log2 fold change (LFC) between two treatments. Blue nodes on the graph represent statistically significant LFCs which are greater than a given value than a user-defined LFC parameter. Green nodes indicate statistically significant LFCs which are less than the user-defined LFC parameter. Gray nodes are data points that are not statistically significant. Numerical values in parantheses for each legend color indicate the number of transcripts that meet the prior conditions. Left and right brackets (< and >) represent values that exceed the viewing area of the graph. Node size changes represent the magnitude of the LFC values (i.e. larger shapes reflect larger LFC values). Vertical and horizontal lines indicate user-defined LFC and adjusted <em>p</em>-values, respectively.</p> <h2>With DESeq2</h2> <pre class=$ vsVolcano( x = 'treated_paired.end', y = 'untreated_paired.end', data = df.deseq, d.factor = 'condition', type = 'deseq', padj = 0.05, x.lim = NULL, lfc = NULL, title = TRUE, legend = TRUE, grid = TRUE, data.return = FALSE )

$A volcano plot example using the `vsVolcano()` function with `DESeq2` data. In this visualization, comparisons are made between the $-log_{10}$ *p*-value versus the $log_2$ fold change (LFC) between two treatments. Blue nodes on the graph represent statistically significant LFCs which are greater than a given value than a user-defined LFC parameter. Green nodes indicate statistically significant LFCs which are less than the user-defined LFC parameter. Gray nodes are data points that are not statistically significant. Numerical values in parantheses for each legend color indicate the number of transcripts that meet the prior conditions. Left and right brackets (< and >) represent values that exceed the viewing area of the graph. Node size changes represent the magnitude of the LFC values (i.e. larger shapes reflect larger LFC values). Vertical and horizontal lines indicate user-defined LFC and adjusted *p*-values, respectively.” width=”100%”></p> <p>Figure 29: <strong>A volcano plot example using the <code>vsVolcano()</code> function with</strong><br><code>DESeq2</code> data. In this visualization, comparisons are made between the −log10−log10 <em>p</em>-value versus the log2log2 fold change (LFC) between two treatments. Blue nodes on the graph represent statistically significant LFCs which are greater than a given value than a user-defined LFC parameter. Green nodes indicate statistically significant LFCs which are less than the user-defined LFC parameter. Gray nodes are data points that are not statistically significant. Numerical values in parantheses for each legend color indicate the number of transcripts that meet the prior conditions. Left and right brackets (< and >) represent values that exceed the viewing area of the graph. Node size changes represent the magnitude of the LFC values (i.e. larger shapes reflect larger LFC values). Vertical and horizontal lines indicate user-defined LFC and adjusted <em>p</em>-values, respectively.</p> <h2>With edgeR</h2> <pre class=$ vsVolcano( x = 'WW', y = 'MM', data = df.edger, d.factor = NULL, type = 'edger', padj = 0.05, x.lim = NULL, lfc = NULL, title = TRUE, legend = TRUE, grid = TRUE, data.return = FALSE )

$A volcano plot example using the `vsVolcano()` function with `edgeR` data. In this visualization, comparisons are made between the $-log_{10}$ *p*-value versus the $log_2$ fold change (LFC) between two treatments. Blue nodes on the graph represent statistically significant LFCs which are greater than a given value than a user-defined LFC parameter. Green nodes indicate statistically significant LFCs which are less than the user-defined LFC parameter. Gray nodes are data points that are not statistically significant. Numerical values in parantheses for each legend color indicate the number of transcripts that meet the prior conditions. Left and right brackets (< and >) represent values that exceed the viewing area of the graph. Node size changes represent the magnitude of the LFC values (i.e. larger shapes reflect larger LFC values). Vertical and horizontal lines indicate user-defined LFC and adjusted *p*-values, respectively.” width=”100%”></p> <p>Figure 30: <strong>A volcano plot example using the <code>vsVolcano()</code> function with</strong><br><code>edgeR</code> data. In this visualization, comparisons are made between the −log10−log10 <em>p</em>-value versus the log2log2 fold change (LFC) between two treatments. Blue nodes on the graph represent statistically significant LFCs which are greater than a given value than a user-defined LFC parameter. Green nodes indicate statistically significant LFCs which are less than the user-defined LFC parameter. Gray nodes are data points that are not statistically significant. Numerical values in parantheses for each legend color indicate the number of transcripts that meet the prior conditions. Left and right brackets (< and >) represent values that exceed the viewing area of the graph. Node size changes represent the magnitude of the LFC values (i.e. larger shapes reflect larger LFC values). Vertical and horizontal lines indicate user-defined LFC and adjusted <em>p</em>-values, respectively.</p> <h1>Example S9: Creating volcano plot matrices</h1> <p>Similar to the prior matrix functions, <code>vsVolcanoMatrix()</code> will produce visualizations for all comparisons within your data set. For more information on how the aesthetics are plotted in these visualizations, please refer to the figure caption and Method S1.</p> <h2>With Cuffdiff</h2> <pre class=$ vsVolcanoMatrix( data = df.cuff, d.factor = NULL, type = 'cuffdiff', padj = 0.05, x.lim = NULL, lfc = NULL, title = TRUE, legend = TRUE, grid = TRUE, counts = TRUE )

$A volcano plot matrix using the `vsVolcanoMatrix()` function with `Cuffdiff` data. Similar to the `vsVolcano()` function, `vsVolcanoMatrix()` will generate a matrix of volcano plots for all comparisons within an experiment. Comparisons are made between the $-log_{10}$ *p*-value versus the $log_2$ fold change (LFC) between two treatments. Blue nodes on the graph represent statistically significant LFCs which are greater than a given value than a user-defined LFC parameter. Green nodes indicate statistically significant LFCs which are less than the user-defined LFC parameter. Gray nodes are data points that are not statistically significant. The blue and green numbers in each facet represent the number of transcripts that meet the criteria for blue and green nodes in each comparison. Left and right brackets (< and >) represent values that exceed the viewing area of the graph. Node size changes represent the magnitude of the LFC values (i.e. larger shapes reflect larger LFC values). Vertical and horizontal lines indicate user-defined LFC and adjusted *p*-values, respectively.” width=”100%”></p> <p>Figure 31: <strong>A volcano plot matrix using the <code>vsVolcanoMatrix()</code> function with</strong><br><code>Cuffdiff</code> data. Similar to the <code>vsVolcano()</code> function, <code>vsVolcanoMatrix()</code> will generate a matrix of volcano plots for all comparisons within an experiment. Comparisons are made between the −log10−log10 <em>p</em>-value versus the log2log2 fold change (LFC) between two treatments. Blue nodes on the graph represent statistically significant LFCs which are greater than a given value than a user-defined LFC parameter. Green nodes indicate statistically significant LFCs which are less than the user-defined LFC parameter. Gray nodes are data points that are not statistically significant. The blue and green numbers in each facet represent the number of transcripts that meet the criteria for blue and green nodes in each comparison. Left and right brackets (< and >) represent values that exceed the viewing area of the graph. Node size changes represent the magnitude of the LFC values (i.e. larger shapes reflect larger LFC values). Vertical and horizontal lines indicate user-defined LFC and adjusted <em>p</em>-values, respectively.</p> <h2>With DESeq2</h2> <pre class=$ vsVolcanoMatrix( data = df.deseq, d.factor = 'condition', type = 'deseq', padj = 0.05, x.lim = NULL, lfc = NULL, title = TRUE, legend = TRUE, grid = TRUE, counts = TRUE )

$A volcano plot matrix using the `vsVolcanoMatrix()` function with `DESeq2` data. Similar to the `vsVolcano()` function, `vsVolcanoMatrix()` will generate a matrix of volcano plots for all comparisons within an experiment. Comparisons are made between the $-log_{10}$ *p*-value versus the $log_2$ fold change (LFC) between two treatments. Blue nodes on the graph represent statistically significant LFCs which are greater than a given value than a user-defined LFC parameter. Green nodes indicate statistically significant LFCs which are less than the user-defined LFC parameter. Gray nodes are data points that are not statistically significant. The blue and green numbers in each facet represent the number of transcripts that meet the criteria for blue and green nodes in each comparison. Left and right brackets (< and >) represent values that exceed the viewing area of the graph. Node size changes represent the magnitude of the LFC values (i.e. larger shapes reflect larger LFC values). Vertical and horizontal lines indicate user-defined LFC and adjusted *p*-values, respectively.” width=”100%”></p> <p>Figure 32: <strong>A volcano plot matrix using the <code>vsVolcanoMatrix()</code> function with</strong><br><code>DESeq2</code> data. Similar to the <code>vsVolcano()</code> function, <code>vsVolcanoMatrix()</code> will generate a matrix of volcano plots for all comparisons within an experiment. Comparisons are made between the −log10−log10 <em>p</em>-value versus the log2log2 fold change (LFC) between two treatments. Blue nodes on the graph represent statistically significant LFCs which are greater than a given value than a user-defined LFC parameter. Green nodes indicate statistically significant LFCs which are less than the user-defined LFC parameter. Gray nodes are data points that are not statistically significant. The blue and green numbers in each facet represent the number of transcripts that meet the criteria for blue and green nodes in each comparison. Left and right brackets (< and >) represent values that exceed the viewing area of the graph. Node size changes represent the magnitude of the LFC values (i.e. larger shapes reflect larger LFC values). Vertical and horizontal lines indicate user-defined LFC and adjusted <em>p</em>-values, respectively.</p> <h2>With edgeR</h2> <pre class=$ vsVolcanoMatrix( data = df.edger, d.factor = NULL, type = 'edger', padj = 0.05, x.lim = NULL, lfc = NULL, title = TRUE, legend = TRUE, grid = TRUE, counts = TRUE )

$A volcano plot matrix using the `vsVolcanoMatrix()` function with `edgeR` data. Similar to the `vsVolcano()` function, `vsVolcanoMatrix()` will generate a matrix of volcano plots for all comparisons within an experiment. Comparisons are made between the $-log_{10}$ *p*-value versus the $log_2$ fold change (LFC) between two treatments. Blue nodes on the graph represent statistically significant LFCs which are greater than a given value than a user-defined LFC parameter. Green nodes indicate statistically significant LFCs which are less than the user-defined LFC parameter. Gray nodes are data points that are not statistically significant. The blue and green numbers in each facet represent the number of transcripts that meet the criteria for blue and green nodes in each comparison. Left and right brackets (< and >) represent values that exceed the viewing area of the graph. Node size changes represent the magnitude of the LFC values (i.e. larger shapes reflect larger LFC values). Vertical and horizontal lines indicate user-defined LFC and adjusted *p*-values, respectively.” width=”100%”></p> <p>Figure 33: <strong>A volcano plot matrix using the <code>vsVolcanoMatrix()</code> function with</strong><br><code>edgeR</code> data. Similar to the <code>vsVolcano()</code> function, <code>vsVolcanoMatrix()</code> will generate a matrix of volcano plots for all comparisons within an experiment. Comparisons are made between the −log10−log10 <em>p</em>-value versus the log2log2 fold change (LFC) between two treatments. Blue nodes on the graph represent statistically significant LFCs which are greater than a given value than a user-defined LFC parameter. Green nodes indicate statistically significant LFCs which are less than the user-defined LFC parameter. Gray nodes are data points that are not statistically significant. The blue and green numbers in each facet represent the number of transcripts that meet the criteria for blue and green nodes in each comparison. Left and right brackets (< and >) represent values that exceed the viewing area of the graph. Node size changes represent the magnitude of the LFC values (i.e. larger shapes reflect larger LFC values). Vertical and horizontal lines indicate user-defined LFC and adjusted <em>p</em>-values, respectively.</p> <h1>Example S10: Creating four way plots</h1> <p>To create four-way plots, the function, <code>vsFourWay()</code> is used. This plot compares the log2log2 fold changes between two samples and a ‘control’. For more information on how each of the aesthetics are plotted, please refer to the figure captions and Method S1.</p> <h2>With Cuffdiff</h2> <pre class=$ vsFourWay( x = 'iPS', y = 'hESC', control = 'Fibroblasts', data = df.cuff, d.factor = NULL, type = 'cuffdiff', padj = 0.05, x.lim = NULL, y.lim = NULL, lfc = NULL, legend = TRUE, title = TRUE, grid = TRUE )

Figure 34: A four way plot visualization using the vsFourWay() function with
Cuffdiff data. In this example, LFCs comparisons between two treatments and a control are made. Blue nodes indicate statistically significant LFCs which are greater than a given user-defined value for both x and y-axes. Green nodes reflect statistically significant LFCs which are less than a user-defined value for treatment y and greater than said value for treatment x. Similar to green nodes, red nodes reflect statistically significant LFCs which are greater than a user-defined vlaue treatment y and less than said value for treatment x. Gray nodes are data points that are not statistically significant for both x and y-axes. Triangular shapes indicate values which exceed the viewing are for the graph. Size change reflects the magnitude of LFC values ( i.e. larger shapes reflect larger LFC values). Vertical and horizontal dashed lines indicate user-defined LFC values.

With DESeq2

vsFourWay(
    x = 'treated_paired.end', y = 'untreated_single.read', 
    control = 'untreated_paired.end', data = df.deseq, 
    d.factor = 'condition', type = 'deseq', padj = 0.05, x.lim = NULL, 
    y.lim = NULL, lfc = NULL, legend = TRUE, title = TRUE, grid = TRUE
)

Figure 35: A four way plot visualization using the vsFourWay() function with
DESeq2 data. In this example, LFCs comparisons between two treatments and a control are made. Blue nodes indicate statistically significant LFCs which are greater than a given user-defined value for both x and y-axes. Green nodes reflect statistically significant LFCs which are less than a user-defined value for treatment y and greater than said value for treatment x. Similar to green nodes, red nodes reflect statistically significant LFCs which are greater than a user-defined vlaue treatment y and less than said value for treatment x. Gray nodes are data points that are not statistically significant for both x and y-axes. Triangular shapes indicate values which exceed the viewing are for the graph. Size change reflects the magnitude of LFC values ( i.e. larger shapes reflect larger LFC values). Vertical and horizontal dashed lines indicate user-defined LFC values.

With edgeR

vsFourWay(
    x = 'WW', y = 'WM', control = 'MM', data = df.edger,
    d.factor = NULL, type = 'edger', padj = 0.05, x.lim = NULL,
    y.lim = NULL, lfc = NULL, legend = TRUE, title = TRUE, grid = TRUE
)

Figure 36: A four way plot visualization using the vsFourWay() function with
DESeq2 data. In this example, LFCs comparisons between two treatments and a control are made. Blue nodes indicate statistically significant LFCs which are greater than a given user-defined value for both x and y-axes. Green nodes reflect statistically significant LFCs which are less than a user-defined value for treatment y and greater than said value for treatment x. Similar to green nodes, red nodes reflect statistically significant LFCs which are greater than a user-defined vlaue treatment y and less than said value for treatment x. Gray nodes are data points that are not statistically significant for both x and y-axes. Triangular shapes indicate values which exceed the viewing are for the graph. Size change reflects the magnitude of LFC values ( i.e. larger shapes reflect larger LFC values). Vertical and horizontal dashed lines indicate user-defined LFC values.

Example S11: Highlighting data points

Overview

For point-based plots, users can highlight IDs of interest (i.e. genes, transcripts, etc.). Currently, this functionality is implemented in the following functions:

vsScatterPlot()
vsMAPlot()
vsVolcano()
vsFourWay()

To use this feature, simply provide a vector of specified IDs to the highlight parameter found in the prior functions. An example of a typical vector would be as follows:

important_ids <- c(
  "ID_001",
  "ID_002",
  "ID_003",
  "ID_004",
  "ID_005"
)
important_ids

## [1] "ID_001" "ID_002" "ID_003" "ID_004" "ID_005"

For specific examples using the toy data set, please see the proceeding 4 sub-sections.

Highlighting with `vsScatterPlot()`

data("df.cuff")
hl <- c(
  "XLOC_000033",
  "XLOC_000099",
  "XLOC_001414",
  "XLOC_001409"
)
vsScatterPlot(
    x = "hESC", y = "iPS", data = df.cuff, d.factor = NULL,
    type = "cuffdiff", title = TRUE, grid = TRUE, highlight = hl
)

Figure 37: Highlighting with vsScatterPlot()
IDs of interest can be identified within basic scatter plots. When highlighted, non-important points will turn grey while highlighted points will turn blue. Text tags will try to optimize their location within the graph without trying to overlap each other.

Highlighting with `vsMAPlot()`

hl <- c(
  "FBgn0022201",
  "FBgn0003042",
  "FBgn0031957",
  "FBgn0033853",
  "FBgn0003371"
)
vsMAPlot(
    x = "treated_paired.end", y = "untreated_paired.end",
    data = df.deseq, d.factor = "condition", type = "deseq",
    padj = 0.05, y.lim = NULL, lfc = NULL, title = TRUE,
    legend = TRUE, grid = TRUE, data.return = FALSE, highlight = hl
)

Figure 38: Highlighting with vsMAPlot()
IDs of interest can be identified within MA plots. When highlighted, non-important points will decrease in transparency (i.e. lower alpha values) while highlighted points will turn red. Text tags will try to optimize their location within the graph without trying to overlap each other.

Highlighting with `vsVolcano()`

hl <- c(
  "FBgn0036248",
  "FBgn0026573",
  "FBgn0259742",
  "FBgn0038961",
  "FBgn0038928"
)
vsVolcano(
    x = "treated_paired.end", y = "untreated_paired.end",
    data = df.deseq, d.factor = "condition",
    type = "deseq", padj = 0.05, x.lim = NULL, lfc = NULL,
    title = TRUE, grid = TRUE, data.return = FALSE, highlight = hl
)

Figure 39: Highlighting with vsVolcano()
IDs of interest can be identified within volcano plots. When highlighted, non-important points will decrease in transparency (i.e. lower alpha values) while highlighted points will turn red. Text tags will try to optimize their location within the graph without trying to overlap each other.

Highlighting with `vsFourWay()`

data("df.edger")
hl <- c(
    "ID_639",
    "ID_518",
    "ID_602",
    "ID_449",
    "ID_076"
)
vsFourWay(
    x = "WM", y = "WW", control = "MM", data = df.edger,
    d.factor = NULL, type = "edger", padj = 0.05, x.lim = NULL,
    y.lim = NULL, lfc = 2, title = TRUE, grid = TRUE,
    data.return = FALSE, highlight = hl
)

Figure 40: Highlighting with vsFourWay()
IDs of interest can be identified within four-way plots. When highlighted, non-important points will decrease in transparency (i.e. lower alpha values) while highlighted points will turn dark grey. Text tags will try to optimize their location within the graph without trying to overlap each other.

Example S12: Extracting datasets from plots

Overview

For all plots, users can extract datasets used for the visualizations. You may want to pursue this option if you want to use a highly customized plot script or you would like to perform some unmentioned analysis, for example.

To use this this feature, set the data.return parameter in the function you are using to TRUE. You will also need to assign the function to an object. See the following example for further details.

The data extraction process

In this example, we will use the toy data set df.cuff, a cuffdiff output on the function vsScatterPlot(). Take note that we are assigning the function to an object tmp:

# Extract data frame from visualization
data("df.cuff")
tmp <- vsScatterPlot(
   x = "hESC", y = "iPS", data = df.cuff, d.factor = NULL,
   type = "cuffdiff", title = TRUE, grid = TRUE, data.return = TRUE
)

The object we have created is a list with two elements: data and plot. To extract the data, we can call the first element of the list using the subset method (<object>[[1]]) or by invoking its element name (<object>$data):

df_scatter <- tmp[[1]] ## or use tmp$data
head(df_scatter)

##            id           x         y
## 1 XLOC_000001 3.47386e-01  20.21750
## 2 XLOC_000002 0.00000e+00   0.00000
## 3 XLOC_000003 0.00000e+00   0.00000
## 4 XLOC_000004 6.97259e+05   0.00000
## 5 XLOC_000005 6.96704e+02 355.82300
## 6 XLOC_000006 0.00000e+00   1.51396

Return the plot

By assigning each of these functions to a list, we can also store the plot as another element. To extract the plot, we can call the second element of the list using the aformentioned procedures:

my_plot <- tmp[[2]] ## or use tmp$plot
my_plot

Example S13: Changing text sizes

Overview

For all functions, users can modify the font size of multiple portions of the plot. These portions primarily revolve around these components:

Axis text and titles
Plot title
Legend text and titles
Facet titles

To manipulate these components, users can modify the default values of the following parameters:

xaxis.text.size
yaxis.text.size
xaxis.title.size
yaxis.title.size
main.title.size
legend.text.size
legend.title.size
facet.title.size

What exactly can you manipulate?

Each of parameters mentioned in the prior section refer to numerical values. These values correlate to font size in typographic points. To illustrate what exactly these parameters modify, please refer to the following figure:

Figure 41: A visual guide to text size parameters
Users can modify these components which are highlighted by their respective parameter.

The facet.title.size parameter refers to the facets which are allocated in the matrix functions (vsScatterMatrix(), vsMAMatrix(), vsVolcanoMatrix()). This is illustrated in the following figure:

Figure 42: Location of facet titles
Facet title sizes can be modified using the facet.title.size parameter.

Since not all functions are equal in their parameters and component layout, some functions will either have or lack some of the prior parameters. To get an idea of which have functions have which, please refer to the following figure:

Figure 43: An overview of text size parameters for each function
Cells highlighted in red refer to parameters (columns) which are found in their respective functions (rows). Cells which are grey indicate parameters which are not found in each of the functions.

Method S1: Determining data point shape and size changes

The shape and size of each data point will also change depending on several conditions. To maximize the viewing area while retaining high resolution, some data points will not be present within the viewing area. If they exceed the viewing area, they will change shape from a circle to a triangular orientation.

The extent (i.e. fold change) to how far these points exceed the viewing area are based on the following criteria:

SUB – values that fall within the viewing area of the plot.
T-1 – values that are greater than the maximum viewing area and are less than the 25th percentile of values that exceed the viewing area.
T-2 – Similar to T-1; values fall between the 25th and 50th percentile.
T-3 – Similar to T-2; values fall between the 50th and 75th percentile.
T-4 – Similar to T-3; values fall between the 75th and 100th percentile.

To further clarify theses conditions, please refer to the following figure:

Figure 44: An illustration detailing the principles behind the node size for
the differntial gene expression functions. In this figure, the data points increase in size depending on which quartile they reside as the absolute LFC increases (top bar). Data points that fall within the viewing area classified as SUB while data points that exceed this area are classified as T-1 through T-4.

Method S2: Determining function performance

Function efficiencies were determined by calculating system times by using the microbenchmark R package. Each function was ran 100 times with the prior code used in the documentation. All benchmarks were determined on a machine running a 64-bit Windows 10 operating system, 8 GB of RAM, and an Intel Core i5-6400 processor running at 2.7 GHz.

Scatterplots

Figure 45: Benchmarks for the vsScatterPlot() function
Time (ms) distributions were generated for this function using 100 trials for each of the three RNAseq data objects. Cuffdiff, DESeq2, and edgeR example data sets contained 1200, 724, and 29391 transcripts, respectively.

Scatterplot matrices

Figure 46: Benchmarks for the vsScatterMatrix() function
Time (ms) distributions were generated for this function using 100 trials for each of the three RNAseq data objects. Cuffdiff, DESeq2, and edgeR example data sets contained 1200, 724, and 29391 transcripts, respectively.

Box plots

Figure 47: Benchmarks for the vsBoxPlot() function
Time (ms) distributions were generated for this function using 100 trials for each of the three RNAseq data objects. Cuffdiff, DESeq2, and edgeR example data sets contained 1200, 724, and 29391 transcripts, respectively.

Differential gene expression matrices

Figure 48: Benchmarks for the vsDEGMatrix() function
Time (ms) distributions were generated for this function using 100 trials for each of the three RNAseq data objects. Cuffdiff, DESeq2, and edgeR example data sets contained 1200, 724, and 29391 transcripts, respectively.

Volcano plots

Figure 49: Benchmarks for the vsVolcano() function
Time (ms) distributions were generated for this function using 100 trials for each of the three RNAseq data objects. Cuffdiff, DESeq2, and edgeR example data sets contained 1200, 724, and 29391 transcripts, respectively.

Volcano plot matrices

Figure 50: Benchmarks for the vsVolcanoMatrix() function
Time (ms) distributions were generated for this function using 100 trials for each of the three RNAseq data objects. Cuffdiff, DESeq2, and edgeR example data sets contained 1200, 724, and 29391 transcripts, respectively.

MA plots

Figure 51: Benchmarks for the vsMAPlot() function
Time (ms) distributions were generated for this function using 100 trials for each of the three RNAseq data objects. Cuffdiff, DESeq2, and edgeR example data sets contained 1200, 724, and 29391 transcripts, respectively.

MA matrices

Figure 52: Benchmarks for the vsMAMatrix() function
Time (s) distributions were generated for this function using 100 trials for each of the three RNAseq data objects. Cuffdiff, DESeq2, and edgeR example data sets contained 1200, 724, and 29391 transcripts, respectively.

Four way plots

Figure 53: Benchmarks for the vsFourWay() function
Time (ms) distributions were generated for this function using 100 trials for each of the three RNAseq data objects. Cuffdiff, DESeq2, and edgeR example data sets contained 1200, 724, and 29391 transcripts, respectively.

Session info

## R version 4.1.0 (2021-05-18)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.2 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.13-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.13-bioc/R/lib/libRlapack.so
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_GB              LC_COLLATE=C              
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] parallel  stats4    stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
##  [1] edgeR_3.34.0                limma_3.48.0               
##  [3] DESeq2_1.32.0               SummarizedExperiment_1.22.0
##  [5] Biobase_2.52.0              MatrixGenerics_1.4.0       
##  [7] matrixStats_0.58.0          GenomicRanges_1.44.0       
##  [9] GenomeInfoDb_1.28.0         IRanges_2.26.0             
## [11] S4Vectors_0.30.0            BiocGenerics_0.38.0        
## [13] vidger_1.12.0               BiocStyle_2.20.0           
## 
## loaded via a namespace (and not attached):
##  [1] bitops_1.0-7           bit64_4.0.5            RColorBrewer_1.1-2    
##  [4] httr_1.4.2             tools_4.1.0            bslib_0.2.5.1         
##  [7] utf8_1.2.1             R6_2.5.0               DBI_1.1.1             
## [10] colorspace_2.0-1       withr_2.4.2            tidyselect_1.1.1      
## [13] GGally_2.1.1           bit_4.0.4              compiler_4.1.0        
## [16] DelayedArray_0.18.0    labeling_0.4.2         bookdown_0.22         
## [19] sass_0.4.0             scales_1.1.1           genefilter_1.74.0     
## [22] stringr_1.4.0          digest_0.6.27          rmarkdown_2.8         
## [25] XVector_0.32.0         pkgconfig_2.0.3        htmltools_0.5.1.1     
## [28] highr_0.9              fastmap_1.1.0          rlang_0.4.11          
## [31] RSQLite_2.2.7          farver_2.1.0           jquerylib_0.1.4       
## [34] generics_0.1.0         jsonlite_1.7.2         BiocParallel_1.26.0   
## [37] dplyr_1.0.6            RCurl_1.98-1.3         magrittr_2.0.1        
## [40] GenomeInfoDbData_1.2.6 Matrix_1.3-3           Rcpp_1.0.6            
## [43] munsell_0.5.0          fansi_0.4.2            lifecycle_1.0.0       
## [46] stringi_1.6.2          yaml_2.2.1             zlibbioc_1.38.0       
## [49] plyr_1.8.6             grid_4.1.0             blob_1.2.1            
## [52] ggrepel_0.9.1          crayon_1.4.1           lattice_0.20-44       
## [55] Biostrings_2.60.0      splines_4.1.0          annotate_1.70.0       
## [58] KEGGREST_1.32.0        magick_2.7.2           locfit_1.5-9.4        
## [61] knitr_1.33             pillar_1.6.1           geneplotter_1.70.0    
## [64] XML_3.99-0.6           glue_1.4.2             evaluate_0.14         
## [67] BiocManager_1.30.15    png_0.1-7              vctrs_0.3.8           
## [70] tidyr_1.1.3            gtable_0.3.0           purrr_0.3.4           
## [73] reshape_0.8.8          assertthat_0.2.1       cachem_1.0.5          
## [76] ggplot2_3.3.3          xfun_0.23              xtable_1.8-4          
## [79] survival_3.2-11        tibble_3.1.2           AnnotationDbi_1.54.0  
## [82] memoise_2.0.0          ellipsis_0.3.2

R语言入群

R语言群，不知怎么的入群的人很少啊，请大家支持呐--

请关注“恒诺新知”微信公众号，感谢“R语言“，”数据那些事儿“，”老俊俊的生信笔记“，”冷🈚️思“，“珞珈R”，“生信星球”的支持！

关于课程

关于课程

TCGA数据挖掘

摘要

R语言入群

示例 S1：安装和数据示例

所用数据概览

示例 S2：创建箱线图

使用Cuffdiff

使用DESeq2

使用edgeR

箱形图的美学优化

box variant

violin variant

boxdot variant

viodot variant

viosumm variant

notch variant

箱形图的调色板变体

颜色变体示例 1

Color variant example 2

Color variant example 3

Example S3: Creating scatter plots

With Cuffdiff

With DESeq2

With edgeR

Example S4: Creating scatter plot matrices

With Cuffdiff

With DESeq2

With edgeR

Example S5: Creating differential gene expression matrices

With Cuffdiff

With DESeq2

With edgeR

Grey-scale DEG matrices

Example S6: Creating MA plots

With Cuffdiff

With DESeq2

With edgeR

Example S7: Creating MA plot matrices

With Cuffdiff

With DESeq2

With edgeR

Example S8: Creating volcano plots

With Cuffdiff

With DESeq2

With edgeR

Example S11: Highlighting data points

Overview

Highlighting with vsScatterPlot()

Highlighting with vsMAPlot()

Highlighting with vsVolcano()

Highlighting with vsFourWay()

Example S12: Extracting datasets from plots

Overview

The data extraction process

Return the plot

Example S13: Changing text sizes

Overview

What exactly can you manipulate?

Method S1: Determining data point shape and size changes

Method S2: Determining function performance

Scatterplots

Scatterplot matrices

Box plots

Differential gene expression matrices

Volcano plots

Volcano plot matrices

MA plots

MA matrices

Four way plots

Session info

R语言入群

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`box` variant

`violin` variant

`boxdot` variant

`viodot` variant

`viosumm` variant

`notch` variant

Highlighting with `vsScatterPlot()`

Highlighting with `vsMAPlot()`

Highlighting with `vsVolcano()`

Highlighting with `vsFourWay()`

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