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library(clustifyr) # calculate correlation res <- clustify( input pbmc_matrix_small, metadata = pbmc_meta$classified, cbmc_ref, ref_mat query_genes pbmc_vargenes ) # print assignments cor_to_call(res) #> # A tibble: 9 x 3 #> # Groups: cluster [9] #> cluster type r #> #> 1 B 0.909 #> 2 CD14+ Mono CD14+ Mono 0.915 #> 3 FCGR3A+ Mono CD16+ Mono 0.929 #> 4 Memory CD4 T CD4 T 0.861 #> 5 Naive CD4 T CD4 T 0.889 #> 6 DC DC 0.849 #> 7 Platelet Mk 0.732 #> 8 CD8 T NK 0.826 #> 9 NK NK 0.894 # plot assignments on a projection plot_best_call( cor_mat = res, metadata = pbmc_meta, cluster_col = "classified"

library(clustifyr) # calculate correlation res <- clustify( input pbmc_matrix_small, metadata = pbmc_meta$classified, cbmc_ref, ref_mat query_genes pbmc_vargenes ) # print assignments cor_to_call(res) #> # A tibble: 9 x 3 #> # Groups: cluster [9] #> cluster type r #> #> 1 B 0.909 #> 2 CD14+ Mono CD14+ Mono 0.915 #> 3 FCGR3A+ Mono CD16+ Mono 0.929 #> 4 Memory CD4 T CD4 T 0.861 #> 5 Naive CD4 T CD4 T 0.889 #> 6 DC DC 0.849 #> 7 Platelet Mk 0.732 #> 8 CD8 T NK 0.826 #> 9 NK NK 0.894 # plot assignments on a projection plot_best_call( cor_mat = res, metadata = pbmc_meta, cluster_col = "classified"

Database System Concepts
Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
Problem 1PE
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Can you please explain what each code does in the picture, by that I mean elaborate on every code, function of it and importance. If you can explain the graph too would be awesome.

```r library(clustifyr) # calculate correlation res <- clustifyr( input = pbmc_matrix_small, metadata = pbmc_meta$classified, ref_mat = cbmc_ref, query_genes = pbmc_vargenes ) # print assignments cor_to_call(res) #> # A tibble: 9 x 3 #> # Groups: cluster [9] #> cluster type r #> <chr> <chr> <dbl> #> 1 B B 0.909 #> 2 CD14+ Mono CD14+ Mono 0.915 #> 3 FCGR3A+ Mono CD16+ Mono 0.929 #> 4 Memory CD4 T CD4 T 0.861 #> 5 Naive CD4 T CD4 T 0.889 #> 6 DC DC 0.849 #> 7 Platelet Mk 0.732 #> 8 CD8 T NK 0.826 #> 9 NK NK 0.894 # plot assignments on a projection plot_best_call( cor_mat = res, metadata = pbmc_meta, cluster_col = "classified" ) ``` **Explanation:** This script involves the use of the `clustifyr` package in R, which is designed for cell type classification in single-cell RNA sequencing data. 1. **Loading Library**: The `clustifyr` library is loaded. 2. **Correlation Calculation**: - The function `clustifyr` is used to calculate the correlation between query and reference datasets. - `input` specifies the matrix of single-cell RNA-seq data. - `metadata` holds classification metadata. - `ref_mat` is the reference matrix for comparison. - `query_genes` indicates the variable genes being queried. 3. **Assignment Printing**: - `cor_to_call(res)` prints out the assignments made by the correlation analysis. - Results show different clusters (e.g., B, CD14+ Mono) matched to similar types with correlation values (ranging from 0.732 to 0.929). 4. **Plotting Assignments**: - `plot_best
Transcribed Image Text:```r library(clustifyr) # calculate correlation res <- clustifyr( input = pbmc_matrix_small, metadata = pbmc_meta$classified, ref_mat = cbmc_ref, query_genes = pbmc_vargenes ) # print assignments cor_to_call(res) #> # A tibble: 9 x 3 #> # Groups: cluster [9] #> cluster type r #> <chr> <chr> <dbl> #> 1 B B 0.909 #> 2 CD14+ Mono CD14+ Mono 0.915 #> 3 FCGR3A+ Mono CD16+ Mono 0.929 #> 4 Memory CD4 T CD4 T 0.861 #> 5 Naive CD4 T CD4 T 0.889 #> 6 DC DC 0.849 #> 7 Platelet Mk 0.732 #> 8 CD8 T NK 0.826 #> 9 NK NK 0.894 # plot assignments on a projection plot_best_call( cor_mat = res, metadata = pbmc_meta, cluster_col = "classified" ) ``` **Explanation:** This script involves the use of the `clustifyr` package in R, which is designed for cell type classification in single-cell RNA sequencing data. 1. **Loading Library**: The `clustifyr` library is loaded. 2. **Correlation Calculation**: - The function `clustifyr` is used to calculate the correlation between query and reference datasets. - `input` specifies the matrix of single-cell RNA-seq data. - `metadata` holds classification metadata. - `ref_mat` is the reference matrix for comparison. - `query_genes` indicates the variable genes being queried. 3. **Assignment Printing**: - `cor_to_call(res)` prints out the assignments made by the correlation analysis. - Results show different clusters (e.g., B, CD14+ Mono) matched to similar types with correlation values (ranging from 0.732 to 0.929). 4. **Plotting Assignments**: - `plot_best
The image shows a UMAP (Uniform Manifold Approximation and Projection) plot used for visualizing high-dimensional data, such as single-cell RNA sequencing. Each point on the plot represents a cell, and they are color-coded based on cell types. **Legend:** - **B cells**: Light blue - **CD14+ Monocytes**: Dark blue - **CD16+ Monocytes**: Light green - **CD4 T cells**: Green - **DC (Dendritic Cells)**: Pink - **Mk (Megakaryocytes)**: Red - **NK (Natural Killer cells)**: Orange **Explanation of Plot:** - The plot is divided into two axes: UMAP_1 (x-axis) and UMAP_2 (y-axis). - Points are clustered in distinct groups, each corresponding to different cell types. - For example, the cluster on the right, predominantly green, represents CD4 T cells. - The light blue cluster located in the upper part of the plot represents B cells. - The orange cluster towards the bottom-right represents NK cells. - Cells of similar types are grouped together due to their similar gene expression profiles, which the UMAP algorithm highlights by placing them in proximal positions on the plot. This visualization aids in identifying and segregating different cell types in a high-dimensional dataset, allowing for a better understanding of cellular compositions and relationships.
Transcribed Image Text:The image shows a UMAP (Uniform Manifold Approximation and Projection) plot used for visualizing high-dimensional data, such as single-cell RNA sequencing. Each point on the plot represents a cell, and they are color-coded based on cell types. **Legend:** - **B cells**: Light blue - **CD14+ Monocytes**: Dark blue - **CD16+ Monocytes**: Light green - **CD4 T cells**: Green - **DC (Dendritic Cells)**: Pink - **Mk (Megakaryocytes)**: Red - **NK (Natural Killer cells)**: Orange **Explanation of Plot:** - The plot is divided into two axes: UMAP_1 (x-axis) and UMAP_2 (y-axis). - Points are clustered in distinct groups, each corresponding to different cell types. - For example, the cluster on the right, predominantly green, represents CD4 T cells. - The light blue cluster located in the upper part of the plot represents B cells. - The orange cluster towards the bottom-right represents NK cells. - Cells of similar types are grouped together due to their similar gene expression profiles, which the UMAP algorithm highlights by placing them in proximal positions on the plot. This visualization aids in identifying and segregating different cell types in a high-dimensional dataset, allowing for a better understanding of cellular compositions and relationships.
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