In cparsania/FungiExpresZ: FungiExpresZ: Fungal gene expression data analysis and visualizations

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knitr::opts_chunk$set(echo = TRUE)
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How to use SRA data

r params$app_name contains normalised gene expression values for more than r params$n_sra_data NCBI SRA data. In this video, we show that how to select any of these data for downstream analysis and visualization purpose.

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                       width = frame_width, height = frame_height,frameborder = "2px solid #000000",allowfullscreen = TRUE)

How to upload user data

Along with NCBI SRA data, user can also upload his own gene expression matrix for data analysis and visualisation. In this video, we show that how user can upload his own data on r params$app_name for further analysis and visualizations.

vembedr::embed_youtube(id = "4INn1AhEoO4",
                       width = frame_width, height = frame_height,frameborder = "2px solid #000000",allowfullscreen = TRUE)

How to upload sample groups and gene groups

To address the complexity of gene expression data, r params$app_name allows user to integrate gene group and sample group information. In this video, we show that how to upload gene groups and sample groups on the r params$app_name to further integrate them in the visualizations.

vembedr::embed_youtube(id = "H2gdBGUP9XY",
                       width = frame_width, height = frame_height,frameborder = "2px solid #000000",allowfullscreen = TRUE)

How to use predefined NCBI BioProjects as sample groups

NCBI BioProject{target="_blank"} groups single initiative, originating from a single organization or from a consortium SRA runs under single BioProject ID. Diverse data types generated under single study can be find under single BioProject ID. r params$app_name allows user to use BioProject ID as sample groups for selected SRA data. The group information can be used across several plots for comparisons between multiple groups. In this video, we show that how to use given NCBI BioProject IDs as a sample groups.

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                       width = frame_width, height = frame_height,frameborder = "2px solid #000000",allowfullscreen = TRUE)

How to merge user data with SRA data

Integrated analysis of user gene expression data and public SRA data is one of the ways to build data driven hypothesis. r params$app_name allows user seamlessly integrate his/her own gene expression data with selected SRA data. In this video, we show that how to integrate user gene expression data with selected SRA data.

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                       width = frame_width, height = frame_height,frameborder = "2px solid #000000",allowfullscreen = TRUE)

Generate plots

r params$app_name can generate r params$n_plots different exploratory plots and r params$n_go_plots different GO plots. Below several videos show that how to generate different exploratory plots once data uploaded on the r params$app_name.

Scatter plot

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                       width = frame_width, height = frame_height,frameborder = "2px solid #000000",allowfullscreen = TRUE)

Multi-scatter plot

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                       width = frame_width, height = frame_height,frameborder = "2px solid #000000",allowfullscreen = TRUE)

CorrHeat box

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                       width = frame_width, height = frame_height,frameborder = "2px solid #000000",allowfullscreen = TRUE)

Density plot

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                       width = frame_width, height = frame_height,frameborder = "2px solid #000000",allowfullscreen = TRUE)

Histogram

vembedr::embed_youtube(id = "O_YNFr0Tl5Y",
                       width = frame_width, height = frame_height,frameborder = "2px solid #000000",allowfullscreen = TRUE)

Joy plot

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                       width = frame_width, height = frame_height,frameborder = "2px solid #000000",allowfullscreen = TRUE)

Box plot

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                       width = frame_width, height = frame_height,frameborder = "2px solid #000000",allowfullscreen = TRUE)

Violin plot

vembedr::embed_youtube(id = "Rta2Nz1DKCw",
                       width = frame_width, height = frame_height,frameborder = "2px solid #000000",allowfullscreen = TRUE)

Bar plot

vembedr::embed_youtube(id = "8ekS2Y1oRZs",
                       width = frame_width, height = frame_height,frameborder = "2px solid #000000",allowfullscreen = TRUE)

PCA

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                       width = frame_width, height = frame_height,frameborder = "2px solid #000000",allowfullscreen = TRUE)

Line plot

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                       width = frame_width, height = frame_height,frameborder = "2px solid #000000",allowfullscreen = TRUE)

Heatmap

vembedr::embed_youtube(id = "yZH1ioPjmR0",
                       width = frame_width, height = frame_height,frameborder = "2px solid #000000",allowfullscreen = TRUE)

Gene Ontology (GO) analysis and gene annotations

Once the genes of similar expression pattern have been found, next step is to perform GO analysis to look for biological insights from gene expression data. r params$app_name allows user to select genes and gene cluster(s) of similar expression pattern directly from scatter plot and, line plot and heatmap for GO analysis and gene annotations. In these videos, we show that how to perfom GO analysis from scatter plot and heatmap on r params$app_name.

GO analysis from scatter plot

vembedr::embed_youtube(id = "KTIgFVhKPBY",
                       width = frame_width, height = frame_height,frameborder = "2px solid #000000",allowfullscreen = TRUE)

GO analysis from heatmap

vembedr::embed_youtube("Plsat-crwE0",
                       width = frame_width, height = frame_height,frameborder = "2px solid #000000",allowfullscreen = TRUE)

cparsania/FungiExpresZ documentation built on March 15, 2024, 5:48 p.m.