关于生物信息分析的可视化方案

使用plotly或者dash进行数据可视化

Installation

 pip install plotly==4.14.3

import plotly.graph_objects as go
fig = go.Figure(data=go.Bar(y=[2, 3, 1]))
fig.write_html('first_figure.html', auto_open=True)

JupyterLab Support

pip install jupyterlab "ipywidgets>=7.5"

import plotly.graph_objects as go
fig = go.Figure(data=go.Bar(y=[2, 3, 1]))
fig.show()

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Static Image Export

pip install -U kaleido

usage dash

import plotly.graph_objects as go # or plotly.express as px
fig = go.Figure(data=go.Bar(y=[2, 3, 1])) # or any Plotly Express function e.g. px.bar(...)
# fig.add_trace( ... )
# fig.update_layout( ... )

import dash
import dash_core_components as dcc
import dash_html_components as html

app = dash.Dash()
app.layout = html.Div([
    dcc.Graph(figure=fig)
])

#app.run_server(debug=True, use_reloader=False,port=8080,)  # Turn off reloader if inside Jupyter
app.run_server(debug=True, use_reloader=False,port=8080,host="0.0.0.0") 

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see also plotly for R
more Examples
more Examples
dash-bio

使用shiny进行数据可视化

library(shiny)

# Define UI for application that draws a histogram
ui <- fluidPage(
  titlePanel("差异基因表达分析"),
  downloadButton('downloadData', 'Download'),

  fluidRow(
    DT::dataTableOutput("table")
  )
)
# Define server logic required to draw a histogram
server <- function(input, output) {
  lncRNA_deg_sig <- readRDS("/home/wangyang/workspace/bioinfo_analysis/Rscript/result/limma_mRNA_deg.rda")
  output$table <- DT::renderDataTable(DT::datatable({
    lncRNA_deg_sig
  }, rownames = T))

  output$downloadData <- downloadHandler(
    filename = 'file.csv',
    content = function(file) {
      write.csv(lncRNA_deg_sig, file)
    }
  )
}
# Run the application
#options(ui = ui, server = server,options=list(port=8080,host="0.0.0.0"))
shinyApp(ui = ui, server = server,uiPattern="/ESCA",options=list(port=8080,host="0.0.0.0"))

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