Interactive differential expression analysis with volcano3D

I am pleased to present volcano3D, an R package which is now available on CRAN! The volcano3D package enables exploration of probes differentially expressed between three groups. Its main purpose is for the visualisation of differentially expressed genes in a three-dimensional volcano plot. These plots can be converted to interactive visualisations using plotly:

Gif showing interactive volcano3D plot

Here I will explore a case study from the PEAC rheumatoid arthritis trial (Pathobiology of Early Arthritis Cohort). The methodology has been published in Lewis, Myles J., et al. Molecular portraits of early rheumatoid arthritis identify clinical and treatment response phenotypes. Cell reports 28.9 (2019): 2455–2470. (DOI: 10.1016/j.celrep.2019.07.091) with an accompanying interactive website available at https://peac.hpc.qmul.ac.uk:

PEAC RNAseq Shiny website

PEAC RNAseq website hosted using R Shiny and featuring volcano3D plots

This tool acts as a searchable interface to examine relationships between individual synovial and blood gene transcript levels and histological, clinical, and radiographic parameters, and clinical response at 6 months. An interactive interface allows the gene module analysis to be explored for relationships between modules and clinical parameters. The PEAC interactive web tool was creating as an R Shiny app and deployed to the web using a server.


Getting Started

Prerequisites

Install from CRAN

install.packages("volcano3D")
library(volcano3D)

Install from Github

library(devtools)
install_github("KatrionaGoldmann/volcano3D")
library(volcano3D)

volcano3D data

The sample data can then also be installed either from source or using:

install_github("KatrionaGoldmann/volcano3Ddata")
library(volcano3Ddata)
data("syn_data")

Samples in this cohort fall into three pathotype groups:

table(syn_metadata$Pathotype)

╔═══════════╦═══════╗
║ Pathotype ║ Count ║ 
╠═══════════╬═══════╣
║ Fibroid   ║ 16    ║
║ Lymphoid  ║ 45    ║
║ Myeloid   ║ 20    ║
╚═══════════╩═══════╝

In this example we are interested in genes that are differentially expressed between each of these groups.

First we will set up a polar object, using the polar_coords function, which maps the expression and p-values to polar coordinates using:

For more information on how to create p-values data frames see the pvalue generator vignette.

syn_polar <- polar_coords(sampledata = syn_metadata,
                          contrast = "Pathotype",
                          pvalues = syn_pvalues,
                          expression = syn_rld,
                          p_col_suffix = "pvalue",
                          padj_col_suffix = "padj",
                          fc_col_suffix = "log2FoldChange",
                          multi_group_prefix = "LRT",
                          non_sig_name = "Not Significant",
                          significance_cutoff = 0.01,
                          label_column = NULL,
                          fc_cutoff = 0.1)

This creates a polar class object with slots for: sampledata, contrast, pvalues, multi_group_test, expression, polar and non_sig_name. The pvalues slot which should have a data frame with at least two statistics for each comparison — p-value and adjusted p-value — and an optional logarithmic fold change statistic.

If there is a fold change column previously provided, we can now investigate the comparisons between pathotypes using the volcano_trio function. This creates three ggplot outputs:

syn_plots <- 
     volcano_trio(
                  polar = syn_polar,
                  sig_names = c("not significant","significant",
                                "not significant","significant"),
                  colours = rep(c("grey60",  "slateblue1"), 2),
                  text_size = 9,
                  marker_size=1,
                  shared_legend_size = 0.9,
                  label_rows = c("SLAMF6", "PARP16", "ITM2C"),
                  fc_line = FALSE,
                  share_axes = FALSE)

syn_plots$All

volcano plots for each comparison

volcano plots showing differential expression for each comparison

#plotly #data-science #data analysis

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Interactive differential expression analysis with volcano3D
Tyrique  Littel

Tyrique Littel

1604008800

Static Code Analysis: What It Is? How to Use It?

Static code analysis refers to the technique of approximating the runtime behavior of a program. In other words, it is the process of predicting the output of a program without actually executing it.

Lately, however, the term “Static Code Analysis” is more commonly used to refer to one of the applications of this technique rather than the technique itself — program comprehension — understanding the program and detecting issues in it (anything from syntax errors to type mismatches, performance hogs likely bugs, security loopholes, etc.). This is the usage we’d be referring to throughout this post.

“The refinement of techniques for the prompt discovery of error serves as well as any other as a hallmark of what we mean by science.”

  • J. Robert Oppenheimer

Outline

We cover a lot of ground in this post. The aim is to build an understanding of static code analysis and to equip you with the basic theory, and the right tools so that you can write analyzers on your own.

We start our journey with laying down the essential parts of the pipeline which a compiler follows to understand what a piece of code does. We learn where to tap points in this pipeline to plug in our analyzers and extract meaningful information. In the latter half, we get our feet wet, and write four such static analyzers, completely from scratch, in Python.

Note that although the ideas here are discussed in light of Python, static code analyzers across all programming languages are carved out along similar lines. We chose Python because of the availability of an easy to use ast module, and wide adoption of the language itself.

How does it all work?

Before a computer can finally “understand” and execute a piece of code, it goes through a series of complicated transformations:

static analysis workflow

As you can see in the diagram (go ahead, zoom it!), the static analyzers feed on the output of these stages. To be able to better understand the static analysis techniques, let’s look at each of these steps in some more detail:

Scanning

The first thing that a compiler does when trying to understand a piece of code is to break it down into smaller chunks, also known as tokens. Tokens are akin to what words are in a language.

A token might consist of either a single character, like (, or literals (like integers, strings, e.g., 7Bob, etc.), or reserved keywords of that language (e.g, def in Python). Characters which do not contribute towards the semantics of a program, like trailing whitespace, comments, etc. are often discarded by the scanner.

Python provides the tokenize module in its standard library to let you play around with tokens:

Python

1

import io

2

import tokenize

3

4

code = b"color = input('Enter your favourite color: ')"

5

6

for token in tokenize.tokenize(io.BytesIO(code).readline):

7

    print(token)

Python

1

TokenInfo(type=62 (ENCODING),  string='utf-8')

2

TokenInfo(type=1  (NAME),      string='color')

3

TokenInfo(type=54 (OP),        string='=')

4

TokenInfo(type=1  (NAME),      string='input')

5

TokenInfo(type=54 (OP),        string='(')

6

TokenInfo(type=3  (STRING),    string="'Enter your favourite color: '")

7

TokenInfo(type=54 (OP),        string=')')

8

TokenInfo(type=4  (NEWLINE),   string='')

9

TokenInfo(type=0  (ENDMARKER), string='')

(Note that for the sake of readability, I’ve omitted a few columns from the result above — metadata like starting index, ending index, a copy of the line on which a token occurs, etc.)

#code quality #code review #static analysis #static code analysis #code analysis #static analysis tools #code review tips #static code analyzer #static code analysis tool #static analyzer

Ian  Robinson

Ian Robinson

1623856080

Streamline Your Data Analysis With Automated Business Analysis

Have you ever visited a restaurant or movie theatre, only to be asked to participate in a survey? What about providing your email address in exchange for coupons? Do you ever wonder why you get ads for something you just searched for online? It all comes down to data collection and analysis. Indeed, everywhere you look today, there’s some form of data to be collected and analyzed. As you navigate running your business, you’ll need to create a data analytics plan for yourself. Data helps you solve problems , find new customers, and re-assess your marketing strategies. Automated business analysis tools provide key insights into your data. Below are a few of the many valuable benefits of using such a system for your organization’s data analysis needs.

Workflow integration and AI capability

Pinpoint unexpected data changes

Understand customer behavior

Enhance marketing and ROI

#big data #latest news #data analysis #streamline your data analysis #automated business analysis #streamline your data analysis with automated business analysis

Interactive differential expression analysis with volcano3D

I am pleased to present volcano3D, an R package which is now available on CRAN! The volcano3D package enables exploration of probes differentially expressed between three groups. Its main purpose is for the visualisation of differentially expressed genes in a three-dimensional volcano plot. These plots can be converted to interactive visualisations using plotly:

Gif showing interactive volcano3D plot

Here I will explore a case study from the PEAC rheumatoid arthritis trial (Pathobiology of Early Arthritis Cohort). The methodology has been published in Lewis, Myles J., et al. Molecular portraits of early rheumatoid arthritis identify clinical and treatment response phenotypes. Cell reports 28.9 (2019): 2455–2470. (DOI: 10.1016/j.celrep.2019.07.091) with an accompanying interactive website available at https://peac.hpc.qmul.ac.uk:

PEAC RNAseq Shiny website

PEAC RNAseq website hosted using R Shiny and featuring volcano3D plots

This tool acts as a searchable interface to examine relationships between individual synovial and blood gene transcript levels and histological, clinical, and radiographic parameters, and clinical response at 6 months. An interactive interface allows the gene module analysis to be explored for relationships between modules and clinical parameters. The PEAC interactive web tool was creating as an R Shiny app and deployed to the web using a server.


Getting Started

Prerequisites

Install from CRAN

install.packages("volcano3D")
library(volcano3D)

Install from Github

library(devtools)
install_github("KatrionaGoldmann/volcano3D")
library(volcano3D)

volcano3D data

The sample data can then also be installed either from source or using:

install_github("KatrionaGoldmann/volcano3Ddata")
library(volcano3Ddata)
data("syn_data")

Samples in this cohort fall into three pathotype groups:

table(syn_metadata$Pathotype)

╔═══════════╦═══════╗
║ Pathotype ║ Count ║ 
╠═══════════╬═══════╣
║ Fibroid   ║ 16    ║
║ Lymphoid  ║ 45    ║
║ Myeloid   ║ 20    ║
╚═══════════╩═══════╝

In this example we are interested in genes that are differentially expressed between each of these groups.

First we will set up a polar object, using the polar_coords function, which maps the expression and p-values to polar coordinates using:

For more information on how to create p-values data frames see the pvalue generator vignette.

syn_polar <- polar_coords(sampledata = syn_metadata,
                          contrast = "Pathotype",
                          pvalues = syn_pvalues,
                          expression = syn_rld,
                          p_col_suffix = "pvalue",
                          padj_col_suffix = "padj",
                          fc_col_suffix = "log2FoldChange",
                          multi_group_prefix = "LRT",
                          non_sig_name = "Not Significant",
                          significance_cutoff = 0.01,
                          label_column = NULL,
                          fc_cutoff = 0.1)

This creates a polar class object with slots for: sampledata, contrast, pvalues, multi_group_test, expression, polar and non_sig_name. The pvalues slot which should have a data frame with at least two statistics for each comparison — p-value and adjusted p-value — and an optional logarithmic fold change statistic.

If there is a fold change column previously provided, we can now investigate the comparisons between pathotypes using the volcano_trio function. This creates three ggplot outputs:

syn_plots <- 
     volcano_trio(
                  polar = syn_polar,
                  sig_names = c("not significant","significant",
                                "not significant","significant"),
                  colours = rep(c("grey60",  "slateblue1"), 2),
                  text_size = 9,
                  marker_size=1,
                  shared_legend_size = 0.9,
                  label_rows = c("SLAMF6", "PARP16", "ITM2C"),
                  fc_line = FALSE,
                  share_axes = FALSE)

syn_plots$All

volcano plots for each comparison

volcano plots showing differential expression for each comparison

#plotly #data-science #data analysis

Ray  Patel

Ray Patel

1623292080

Getting started with Time Series using Pandas

An introductory guide on getting started with the Time Series Analysis in Python

Time series analysis is the backbone for many companies since most businesses work by analyzing their past data to predict their future decisions. Analyzing such data can be tricky but Python, as a programming language, can help to deal with such data. Python has both inbuilt tools and external libraries, making the whole analysis process both seamless and easy. Python’s Panda s library is frequently used to import, manage, and analyze datasets in various formats. However, in this article, we’ll use it to analyze stock prices and perform some basic time-series operations.

#data-analysis #time-series-analysis #exploratory-data-analysis #stock-market-analysis #financial-analysis #getting started with time series using pandas

Upload and Store Images in MySQL using Node.Js, Express, Express-FileUpload & Handlebars

Today we are going to explore the basic usage of Express-FileUpload. In addition to this, I will show you how you can save/update a user record with a profile image that you can upload.

Source Files:
https://raddy.co.uk/blog/upload-and-store-images-in-mysql-using-node-js-express-express-fileupload-express-handlebars/

Chapters:
0:00 Introduction:
1:16 NPM Project Setup
3:54 Creating Express Server
5:51 Setting up Layouts & Routes
9:46 Express Upload Form
21:50 User Card
33:40 Database
52:05 Ending

Credit:
Icons www.flaticon.com
Cat photo by Cédric VT on Unsplash
Upload Icon by Gregor Cresnar www.flaticon.com

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#node.js #express #express-fileupload #express-handlebars #mysql #upload and store images