Главная
Study mode:
on
1
Running Basic Statistical Analysis in R
2
How to plot a Heatmap in Rstudio, the easy way - Part 1/3
3
How to use ggplot2 in R | A Beginner's RStudio Tutorial
4
Recurrent Neural Network (RNN) in R | A Rstudio Tutorial on Keras and Tensorflow
5
Galaxy Tutorial 2: Basic RNA-Seq Pipeline
6
Gene Set Enrichment Analysis (+ R tutorial)
7
How to Export High Quality Image from R
8
RNASeq Analysis | Differential Expressed Genes (DEGs) from FastQ
9
RNASeq Analysis | Differential Expressed Genes (DEGs) from FastQ
10
Clustering and Markers Identification for ScRNA-Seq | Seurat Package Tutorial
11
R Markdown Tutorial for Beginners | RStudio Tutorials Part 1
12
How to analyze GEO data in R?
13
How to get Keras and Tensorflow running in Rstudio
14
Deep Learning in Bioinformatics | Recent Advancement
15
Log2 fold-change & DESeq2 model in a nutshell
16
Log2 fold-change & DESeq2 model in a nutshell
17
(Simplified) Linear Mixed Model in R with lme()
18
How to analyze 10X Single Cell RNA-seq data with R| Seurat Package Tutorial
19
Make a Web App in 5 min | R Shiny Tutorial
20
Survival Analysis on Cancer data | RStudio Tutorial
21
Survival Analysis on Cancer data | RStudio Tutorial
22
How to use gganimate in R | A RStudio Tutorial for Beginners
23
Googledrive and Googlesheet4 in R | A RStudio Tutorial
24
K Means Clustering and Sub-cluster Determination in Heatmap Part 2/3
25
Heatmap Generation and Exporting plots as hi-res PNG - Part 3/3
26
How to Plot a 3D graph | Plotly Tutorial in Rstudio
27
S3 and S4 Object in R | Object Oriented Programming and Bioconductor
28
You will need this for your meta analysis | Forest plot in RStudio
29
Text Mining with R - Part 1 | Importing PDF and Text Detection
30
How to check the frequencies of gene mutations in TCGA cancer database [R]
31
Creating a Heatmap in R | ComplexHeatMap tutorial p1
32
Word Embedding with Keras: A RStudio Tutorial
33
R Markdown Tutorial for Beginners | RStudio Tutorials Part 2
34
Subset Clusters in Seurat
35
Autoencoder in RStudio Tutorial
36
Comparing scRNA-Seq | Suerat Integration Analysis (Brief)
37
Summarized Experiment (se) Object from Bioconductor
38
Gene Set Enrichment Analysis| GSEA algorithm
39
Data Transformation with R using dplyr part 1/6
40
PyTorch is now on R
41
Build a Telegram Chat Bot in R | Simple RStudio Project Tutorial
42
Efficient Loopsing in R | Rstudio FOR Loop Tutorial
43
I Force AI to read research paper 100 times | Text mining in R
44
How to Build A Simple Neural Network in R
45
Annotations on Heatmaps | ComplexHeatMap tutorial
46
Cancer Somatic Mutation Analysis | MAFtools R Package
47
GFF3 File Format | Clearly Explained
48
TCGA Biomarkers Identification using Machine Learning | Complete Walkthrough
49
Make your Analysis 4x faster | Multi core processing with R
50
How to Make a Simple Blockchain | R Tutorial
51
DEG isolation using limma voom | A Rstudio Tutorial
52
Oncoprint Walkthrough (Essential)
53
How to create your own package in R?
54
Data manipulation in R (For Bioinformatics)| R tutorial | For beginner
55
Joining Multiple Heatmaps | ComplexHeatMap tutorial
56
It is better than Microsoft Word | Writing in R Markdown
57
Why R Programming is better (For me) ?
58
Visualize a Neural Network | ggplot2 & Keras in RStudio
59
R Data Project Best Practice
60
Why R uses so much memory ? Probably
61
How to know if two genes are similar? | Semantic Similarity Explained
62
How to do data Imputation with RStudio
63
16s rRNA Sequencing Analysis and Visualization
Description:
Explore a comprehensive 23-hour RStudio tutorial covering a wide range of topics in data analysis, visualization, and bioinformatics. Learn to perform basic statistical analyses, create heatmaps, and utilize ggplot2 for data visualization. Dive into advanced topics such as recurrent neural networks, RNA-Seq analysis, and single-cell RNA sequencing using the Seurat package. Master essential skills like R Markdown, survival analysis, and text mining. Discover how to work with various data formats, including GEO data and TCGA cancer databases. Gain proficiency in machine learning techniques, including K-means clustering, autoencoders, and word embedding. Explore cutting-edge applications like creating chatbots, building simple blockchains, and leveraging multi-core processing for faster analyses. Ideal for beginners and intermediate R users looking to enhance their skills in data manipulation, visualization, and bioinformatics applications.

RStudio Tutorial

Add to list
#Programming #Domain-Specific Languages (DSL) #R Programming #RStudio #Data Science #Bioinformatics #Data Visualization #Computer Science #Machine Learning #Deep Learning #Mathematics #Statistics & Probability #Statistical Analysis #ggplot2 #Heatmaps
0:00 / 0:00
1 of 63

Running Basic Statistical Analysis in R