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
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
Related questions
Question
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.
![library(clustifyr)
# calculate correlation
res <- clustify(
input = pbmc_matrix_small,
metadata = pbmc_meta$classified,
ref_mat = cbmc_ref,
query_genes
pbmc_vargenes
%3D
# print assignments
cor_to_call(res)
#> # A tibble: 9 x 3
#> # Groups:
cluster [9]
#>
cluster
type
r
#>
<chr>
<chr>
<dbl>
#> 1 B
0.909
#> 2 CD14+ Mono
CD14+ Mono 0.915
#> 3 FCGR3A+ Mono CD16+ Mono 0.929
#> 4 Memory CD4 T CD4 T
0.861
#> 5 Naive CD4 T CD4 T
0.889
#> 6 DC
DC
0.849
#> 7 Platelet
Mk
0.732
#> 8 CD8 T
NK
0.826
#> 9 NK
NK
0.894
# plot assignments on a projection
plot_best_call(
cor_mat = res,
%3D
metadata = pbmc_meta,
cluster_col = "classified"
)](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F528734fa-2f56-42f1-ac26-0d1ff4e0efca%2F089bf488-506f-4db1-adce-19084d4f4977%2F4l6kqh9_processed.png&w=3840&q=75)
Transcribed Image Text:library(clustifyr)
# calculate correlation
res <- clustify(
input = pbmc_matrix_small,
metadata = pbmc_meta$classified,
ref_mat = cbmc_ref,
query_genes
pbmc_vargenes
%3D
# print assignments
cor_to_call(res)
#> # A tibble: 9 x 3
#> # Groups:
cluster [9]
#>
cluster
type
r
#>
<chr>
<chr>
<dbl>
#> 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,
%3D
metadata = pbmc_meta,
cluster_col = "classified"
)
![10-
5-
В
CD14+ Mono
CD16+ Mono
CD4 T
DC
Mk
NK
-5-
-10-
-15
-10
-5
UMAP_1
UMAP_2](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F528734fa-2f56-42f1-ac26-0d1ff4e0efca%2F089bf488-506f-4db1-adce-19084d4f4977%2F79xfn6o_processed.png&w=3840&q=75)
Transcribed Image Text:10-
5-
В
CD14+ Mono
CD16+ Mono
CD4 T
DC
Mk
NK
-5-
-10-
-15
-10
-5
UMAP_1
UMAP_2
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