CellPhoneDB example

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Last updated: 2020-11-24

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Knit directory: interaction-tools/

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    Ignored:    data/celltalker/
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    Ignored:    output/15-talklr.Rmd/
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These are the previous versions of the repository in which changes were made to the R Markdown (analysis/11-CellPhoneDB.Rmd) and HTML (docs/11-CellPhoneDB.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File	Version	Author	Date	Message
html	3feea4c	Luke Zappia	2020-05-27	Add chunk timing to documents
html	9e20a9a	Luke Zappia	2020-05-08	Update cellphonedb to v2.1.2
html	7bd35b2	Luke Zappia	2020-05-06	Rebuild cellphonedb
Rmd	54358bc	Luke Zappia	2020-05-06	Fix links to output figures
html	19026df	Luke Zappia	2020-01-22	Rebuild
Rmd	eb24441	Luke Zappia	2020-01-22	Add output links
Rmd	dbeb3cd	Luke Zappia	2020-01-22	Add plot to CellPhoneDB
Rmd	2335199	Luke Zappia	2020-01-22	Add input and output to CellPhoneDB
Rmd	1ba0683	Luke Zappia	2019-12-13	Set up CellPhoneDB test

---

# Setup document
source(here::here("code", "setup.R"))

# Function dependencies
invisible(drake::readd(download_link))

Introduction

In this document we are going to run through the example analysis for the CellPhoneDB tool and have a look at the output it produces. More information about CellPhoneDB can be found at https://www.cellphonedb.org/.

1 Input

CellPhoneDB takes two input files, a table of metadata for each cell and a counts matrix.

1.1 test_meta.txt

This file contains cell type assignments for each cell in the dataset.

meta <- read_tsv(
    fs::path(PATHS$cellphonedb_in, "test_meta.txt"),
    col_types = cols(
        Cell      = col_character(),
        cell_type = col_character()
    )
)

skim(meta)

Data summary

Name	meta
Number of rows	10
Number of columns	2
_______________________
Column type frequency:
character	2
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
Cell	0	1	22	22	0	10	0
cell_type	0	1	6	9	0	4	0

Chunk time: 0.1 secs

1.2 test_counts.txt

This file contains the count expression matrix.

counts <- read_tsv(
    fs::path(PATHS$cellphonedb_in, "test_counts.txt"),
    col_types = cols(
        .default = col_double(),
        Gene     = col_character()
    )
)

skim(counts)

Data summary

Name	counts
Number of rows	17198
Number of columns	11
_______________________
Column type frequency:
character	1
numeric	10
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
Gene	0	1	15	15	0	17198	0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
d-pos_AAACCTGAGCAGGTCA	0	1	0.14	0.50	0	0	0	0	6.31	▇▁▁▁▁
d-pos_AAACCTGGTACCGAGA	0	1	0.17	0.49	0	0	0	0	5.86	▇▁▁▁▁
d-pos_AAACCTGTCGCCATAA	0	1	0.15	0.50	0	0	0	0	6.09	▇▁▁▁▁
d-pos_AAACGGGTCAGTTGAC	0	1	0.15	0.50	0	0	0	0	6.07	▇▁▁▁▁
d-pos_AAAGATGCATTGAGCT	0	1	0.19	0.46	0	0	0	0	6.01	▇▁▁▁▁
d-pos_AAAGATGTCCAAAGTC	0	1	0.16	0.50	0	0	0	0	6.27	▇▁▁▁▁
d-pos_AAAGCAAAGAGGACGG	0	1	0.14	0.46	0	0	0	0	7.16	▇▁▁▁▁
d-pos_AAAGCAACACATTCGA	0	1	0.15	0.50	0	0	0	0	6.62	▇▁▁▁▁
d-pos_AAAGTAGAGAGCCCAA	0	1	0.17	0.49	0	0	0	0	5.86	▇▁▁▁▁
d-pos_AAAGTAGCAAGCTGAG	0	1	0.17	0.49	0	0	0	0	6.17	▇▁▁▁▁

Chunk time: 0.25 secs

The small test dataset contains 17198 genes and 17198 cells.

2 Analysis

CellPhoneDB is designed to be a command line tool so we will run the following commands in a BASH shell. Here we run it with the full statistical analysis.

eval "$(conda shell.bash hook)"
conda activate $CONDA_ENV

cellphonedb method statistical_analysis \
    --output-path output/11-cellphonedb.Rmd \
    --threads 1 \
    data/cellphonedb/test_meta.txt \
    data/cellphonedb/test_counts.txt

[ ][APP][24/11/20-12:21:19][WARNING] Latest local available version is `v2.0.0`, using it
[ ][APP][24/11/20-12:21:19][WARNING] User selected downloaded database `v2.0.0` is available, using it
[ ][CORE][24/11/20-12:21:19][INFO] Initializing SqlAlchemy CellPhoneDB Core
[ ][CORE][24/11/20-12:21:19][INFO] Using custom database at /Users/luke.zappia/.cpdb/releases/v2.0.0/cellphone.db
[ ][APP][24/11/20-12:21:19][INFO] Launching Method cpdb_statistical_analysis_local_method_launcher
[ ][APP][24/11/20-12:21:19][INFO] Launching Method _set_paths
[ ][APP][24/11/20-12:21:19][WARNING] Output directory (output/11-cellphonedb.Rmd) exist and is not empty. Result can overwrite old results
[ ][APP][24/11/20-12:21:19][INFO] Launching Method _load_meta_counts
[ ][CORE][24/11/20-12:21:19][INFO] Launching Method cpdb_statistical_analysis_launcher
[ ][CORE][24/11/20-12:21:19][INFO] Launching Method _counts_validations
[ ][CORE][24/11/20-12:21:20][INFO] [Cluster Statistical Analysis Simple] Threshold:0.1 Iterations:1000 Debug-seed:-1 Threads:1 Precision:3
[ ][CORE][24/11/20-12:21:20][INFO] Running Simple Prefilters
[ ][CORE][24/11/20-12:21:20][INFO] Running Real Simple Analysis
[ ][CORE][24/11/20-12:21:20][INFO] Running Statistical Analysis
[ ][CORE][24/11/20-12:22:47][INFO] Building Pvalues result
[ ][CORE][24/11/20-12:22:49][INFO] Building Simple results
[ ][CORE][24/11/20-12:22:50][INFO] [Cluster Statistical Analysis Complex] Threshold:0.1 Iterations:1000 Debug-seed:-1 Threads:1 Precision:3
[ ][CORE][24/11/20-12:22:50][INFO] Running Complex Prefilters
[ ][CORE][24/11/20-12:22:51][INFO] Running Real Complex Analysis
[ ][CORE][24/11/20-12:22:51][INFO] Running Statistical Analysis
[ ][CORE][24/11/20-12:24:41][INFO] Building Pvalues result
[ ][CORE][24/11/20-12:24:43][INFO] Building Complex results

Chunk time: 3.47 mins

3 Output

CellPhoneDB produces four output files. Let’s have a look at each of these and see what they contain:

3.1 deconvoluted.txt

According to the CellPhoneDB documentation this file provides additional information about the interacting pairs. Specifically it descibes relationships between genes, complexes and expression in cell types.

deconvoluted <- read_tsv(
    fs::path(OUT_DIR, "deconvoluted.txt"),
    col_types = cols(
        gene_name         = col_character(),
        uniprot           = col_character(),
        is_complex        = col_logical(),
        protein_name      = col_character(),
        complex_name      = col_character(),
        id_cp_interaction = col_character(),
        Myeloid           = col_double(),
        NKcells_0         = col_double(),
        NKcells_1         = col_double(),
        Tcells            = col_double()
    )
)

skim(deconvoluted)

Data summary

Name	deconvoluted
Number of rows	483
Number of columns	10
_______________________
Column type frequency:
character	5
logical	1
numeric	4
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
gene_name	0	1.00	2	9	0	183	0
uniprot	0	1.00	4	7	0	183	0
protein_name	0	1.00	4	11	0	183	0
complex_name	277	0.43	10	17	0	17	0
id_cp_interaction	0	1.00	15	15	0	190	0

Variable type: logical

skim_variable	n_missing	complete_rate	mean	count
is_complex	0	1	0.43	FAL: 277, TRU: 206

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
Myeloid	0	1	0.57	0.90	0	0.00	0.00	1.13	4.01	▇▂▁▁▁
NKcells_0	0	1	0.78	0.86	0	0.09	0.35	1.41	4.22	▇▂▂▁▁
NKcells_1	0	1	0.63	0.82	0	0.00	0.36	1.16	4.61	▇▂▁▁▁
Tcells	0	1	0.71	1.02	0	0.00	0.00	1.95	4.26	▇▁▂▁▁

Chunk time: 0.12 secs

3.2 means.txt

This file contains mean values for each ligand-receptor interaction.

means <- read_tsv(
    fs::path(OUT_DIR, "means.txt"),
    col_types = cols(
        .default            = col_double(),
        id_cp_interaction   = col_character(),
        interacting_pair    = col_character(),
        partner_a           = col_character(),
        partner_b           = col_character(),
        gene_a              = col_character(),
        gene_b              = col_character(),
        secreted            = col_logical(),
        receptor_a          = col_logical(),
        receptor_b          = col_logical(),
        annotation_strategy = col_character(),
        is_integrin         = col_logical()
    )
)

skim(means)

Data summary

Name	means
Number of rows	190
Number of columns	27
_______________________
Column type frequency:
character	7
logical	4
numeric	16
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
id_cp_interaction	0	1.00	15	15	0	190	0
interacting_pair	0	1.00	7	24	0	190	0
partner_a	0	1.00	11	25	0	116	0
partner_b	0	1.00	13	21	0	81	0
gene_a	9	0.95	15	15	0	109	0
gene_b	94	0.51	15	15	0	71	0
annotation_strategy	0	1.00	3	28	0	13	0

Variable type: logical

skim_variable	n_missing	complete_rate	mean	count
secreted	0	1	0.73	TRU: 138, FAL: 52
receptor_a	0	1	0.33	FAL: 127, TRU: 63
receptor_b	0	1	0.29	FAL: 135, TRU: 55
is_integrin	0	1	0.49	FAL: 96, TRU: 94

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
Myeloid|Myeloid	0	1	0.23	0.60	0	0	0	0.00	2.70	▇▁▁▁▁
Myeloid|NKcells_0	0	1	0.40	0.70	0	0	0	0.72	2.70	▇▂▁▁▁
Myeloid|NKcells_1	0	1	0.30	0.64	0	0	0	0.00	2.69	▇▁▁▁▁
Myeloid|Tcells	0	1	0.20	0.62	0	0	0	0.00	2.94	▇▁▁▁▁
NKcells_0|Myeloid	0	1	0.22	0.52	0	0	0	0.00	2.39	▇▁▁▁▁
NKcells_0|NKcells_0	0	1	0.34	0.59	0	0	0	0.51	2.73	▇▂▁▁▁
NKcells_0|NKcells_1	0	1	0.29	0.60	0	0	0	0.29	2.80	▇▁▁▁▁
NKcells_0|Tcells	0	1	0.18	0.50	0	0	0	0.00	2.65	▇▁▁▁▁
NKcells_1|Myeloid	0	1	0.13	0.45	0	0	0	0.00	2.36	▇▁▁▁▁
NKcells_1|NKcells_0	0	1	0.25	0.59	0	0	0	0.00	2.86	▇▁▁▁▁
NKcells_1|NKcells_1	0	1	0.21	0.58	0	0	0	0.00	2.93	▇▁▁▁▁
NKcells_1|Tcells	0	1	0.13	0.47	0	0	0	0.00	2.61	▇▁▁▁▁
Tcells|Myeloid	0	1	0.13	0.47	0	0	0	0.00	2.56	▇▁▁▁▁
Tcells|NKcells_0	0	1	0.21	0.56	0	0	0	0.00	2.88	▇▁▁▁▁
Tcells|NKcells_1	0	1	0.18	0.56	0	0	0	0.00	2.95	▇▁▁▁▁
Tcells|Tcells	0	1	0.07	0.36	0	0	0	0.00	2.33	▇▁▁▁▁

Chunk time: 0.21 secs

It includes information about each ligand-receptor pair as well as scores for each pair of cell types.

3.3 pvalues.txt

This file is similar to means.txt but contains p-values from the statistical test instead of scores.

pvalues <- read_tsv(
    fs::path(OUT_DIR, "pvalues.txt"),
    col_types = cols(
        .default = col_double(),
        id_cp_interaction   = col_character(),
        interacting_pair    = col_character(),
        partner_a           = col_character(),
        partner_b           = col_character(),
        gene_a              = col_character(),
        gene_b              = col_character(),
        secreted            = col_logical(),
        receptor_a          = col_logical(),
        receptor_b          = col_logical(),
        annotation_strategy = col_character(),
        is_integrin         = col_logical()
    )
)

skim(pvalues)

Data summary

Name	pvalues
Number of rows	190
Number of columns	27
_______________________
Column type frequency:
character	7
logical	4
numeric	16
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
id_cp_interaction	0	1.00	15	15	0	190	0
interacting_pair	0	1.00	7	24	0	190	0
partner_a	0	1.00	11	25	0	116	0
partner_b	0	1.00	13	21	0	81	0
gene_a	9	0.95	15	15	0	109	0
gene_b	94	0.51	15	15	0	71	0
annotation_strategy	0	1.00	3	28	0	13	0

Variable type: logical

skim_variable	n_missing	complete_rate	mean	count
secreted	0	1	0.73	TRU: 138, FAL: 52
receptor_a	0	1	0.33	FAL: 127, TRU: 63
receptor_b	0	1	0.29	FAL: 135, TRU: 55
is_integrin	0	1	0.49	FAL: 96, TRU: 94

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
Myeloid|Myeloid	0	1	0.87	0.33	0.00	1.00	1	1	1	▁▁▁▁▇
Myeloid|NKcells_0	0	1	0.77	0.39	0.00	0.56	1	1	1	▂▁▁▁▇
Myeloid|NKcells_1	0	1	0.83	0.34	0.00	1.00	1	1	1	▁▁▁▁▇
Myeloid|Tcells	0	1	0.91	0.28	0.00	1.00	1	1	1	▁▁▁▁▇
NKcells_0|Myeloid	0	1	0.85	0.33	0.00	1.00	1	1	1	▁▁▁▁▇
NKcells_0|NKcells_0	0	1	0.71	0.38	0.00	0.34	1	1	1	▂▂▁▁▇
NKcells_0|NKcells_1	0	1	0.81	0.34	0.00	0.70	1	1	1	▁▁▁▁▇
NKcells_0|Tcells	0	1	0.89	0.29	0.00	1.00	1	1	1	▁▁▁▁▇
NKcells_1|Myeloid	0	1	0.94	0.22	0.03	1.00	1	1	1	▁▁▁▁▇
NKcells_1|NKcells_0	0	1	0.87	0.29	0.00	1.00	1	1	1	▁▁▁▁▇
NKcells_1|NKcells_1	0	1	0.89	0.27	0.02	1.00	1	1	1	▁▁▁▁▇
NKcells_1|Tcells	0	1	0.94	0.21	0.00	1.00	1	1	1	▁▁▁▁▇
Tcells|Myeloid	0	1	0.93	0.25	0.00	1.00	1	1	1	▁▁▁▁▇
Tcells|NKcells_0	0	1	0.89	0.27	0.00	1.00	1	1	1	▁▁▁▁▇
Tcells|NKcells_1	0	1	0.91	0.26	0.00	1.00	1	1	1	▁▁▁▁▇
Tcells|Tcells	0	1	0.97	0.15	0.00	1.00	1	1	1	▁▁▁▁▇

Chunk time: 0.21 secs

3.4 significant_means.txt

This file contains mean values for signitificant ligand-receptor interactions.

sig_means <- read_tsv(
    fs::path(OUT_DIR, "significant_means.txt"),
    col_types = cols(
        .default            = col_double(),
        id_cp_interaction   = col_character(),
        interacting_pair    = col_character(),
        partner_a           = col_character(),
        partner_b           = col_character(),
        gene_a              = col_character(),
        gene_b              = col_character(),
        secreted            = col_logical(),
        receptor_a          = col_logical(),
        receptor_b          = col_logical(),
        annotation_strategy = col_character(),
        is_integrin         = col_logical()
    )
)

skim(sig_means)

Data summary

Name	sig_means
Number of rows	190
Number of columns	28
_______________________
Column type frequency:
character	7
logical	4
numeric	17
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
id_cp_interaction	0	1.00	15	15	0	190	0
interacting_pair	0	1.00	7	24	0	190	0
partner_a	0	1.00	11	25	0	116	0
partner_b	0	1.00	13	21	0	81	0
gene_a	9	0.95	15	15	0	109	0
gene_b	94	0.51	15	15	0	71	0
annotation_strategy	0	1.00	3	28	0	13	0

Variable type: logical

skim_variable	n_missing	complete_rate	mean	count
secreted	0	1	0.73	TRU: 138, FAL: 52
receptor_a	0	1	0.33	FAL: 127, TRU: 63
receptor_b	0	1	0.29	FAL: 135, TRU: 55
is_integrin	0	1	0.49	FAL: 96, TRU: 94

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
rank	0	1.00	0.64	0.55	0.06	0.06	0.69	1.19	1.19	▇▁▁▁▇
Myeloid|Myeloid	169	0.11	1.65	0.49	1.13	1.39	1.55	1.94	2.70	▇▂▃▂▂
Myeloid|NKcells_0	164	0.14	1.43	0.66	0.61	0.87	1.25	2.05	2.70	▇▃▂▆▂
Myeloid|NKcells_1	178	0.06	1.04	0.25	0.74	0.85	1.03	1.08	1.60	▆▇▂▂▂
Myeloid|Tcells	180	0.05	1.95	0.75	1.11	1.37	1.77	2.71	2.94	▇▁▂▂▅
NKcells_0|Myeloid	179	0.06	1.18	0.43	0.61	0.92	1.11	1.43	2.00	▃▇▂▂▃
NKcells_0|NKcells_0	179	0.06	0.51	0.40	0.12	0.22	0.25	0.73	1.19	▇▁▂▁▂
NKcells_0|NKcells_1	184	0.03	1.39	0.88	0.22	0.98	1.19	1.98	2.63	▃▇▃▁▇
NKcells_0|Tcells	182	0.04	1.12	0.56	0.59	0.81	0.96	1.27	2.34	▇▃▂▁▂
NKcells_1|Myeloid	187	0.02	1.45	0.44	0.95	1.28	1.60	1.69	1.79	▇▁▁▇▇
NKcells_1|NKcells_0	182	0.04	1.20	1.00	0.22	0.30	0.98	1.81	2.86	▇▅▂▁▅
NKcells_1|NKcells_1	185	0.03	1.49	0.95	0.39	0.87	1.25	2.48	2.48	▃▃▃▁▇
NKcells_1|Tcells	184	0.03	1.62	0.61	0.76	1.37	1.52	1.96	2.50	▃▃▇▃▃
Tcells|Myeloid	181	0.05	1.72	0.52	1.11	1.37	1.67	2.04	2.56	▇▇▁▂▅
Tcells|NKcells_0	186	0.02	0.97	0.31	0.62	0.79	0.94	1.12	1.36	▇▇▇▁▇
Tcells|NKcells_1	187	0.02	2.04	0.53	1.46	1.81	2.17	2.33	2.50	▇▁▁▇▇
Tcells|Tcells	189	0.01	1.10	NA	1.10	1.10	1.10	1.10	1.10	▁▁▇▁▁

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4 Plotting

CellPhoneDB also has some plotting functions.

4.1 Dotplot

eval "$(conda shell.bash hook)"
conda activate $CONDA_ENV

cellphonedb plot dot_plot \
    --means-path output/11-CellPhoneDB.Rmd/means.txt \
    --pvalues-path output/11-CellPhoneDB.Rmd/pvalues.txt \
    --output-path output/11-CellPhoneDB.Rmd/ \
    --output-name dotplot.png

Chunk time: 8.94 secs

This is a dotplot of the mean expression of the ligand-receptor pair in each pair of cell types.

fig_dir <- here("docs", "figure", DOCNAME)
fs::dir_create(fig_dir)
fs::file_copy(
    fs::path(OUT_DIR, "dotplot.png"),
    fs::path(fig_dir, "dotplot.png"),
    overwrite = TRUE
)

include_graphics(fs::path("figure", DOCNAME, "dotplot.png"), error = FALSE)

Past versions of dotplot.png

Version	Author	Date
3feea4c	Luke Zappia	2020-05-27
9e20a9a	Luke Zappia	2020-05-08
7bd35b2	Luke Zappia	2020-05-06
54358bc	Luke Zappia	2020-05-06
19026df	Luke Zappia	2020-01-22

Chunk time: 0.05 secs

4.2 Heatmap

CellPhoneDB also has a heatmap plotting function. This function also produces some additional output files.

eval "$(conda shell.bash hook)"
conda activate $CONDA_ENV

cellphonedb plot heatmap_plot \
    --pvalues-path output/11-CellPhoneDB.Rmd/pvalues.txt \
    --output-path output/11-CellPhoneDB.Rmd/ \
    --count-name heatmap_counts.png \
    --log-name heatmap_logcounts.png \
    --count-network-name count_network.txt \
    --interaction-count-name interactions_count.txt \
    data/cellphonedb/test_meta.txt

Chunk time: 4.88 secs

4.2.1 Counts

This is a heatmap of the count of interactions between cell types.

fig_dir <- here("docs", "figure", DOCNAME)
fs::dir_create(fig_dir)
fs::file_copy(
    fs::path(OUT_DIR, "heatmap_counts.png"),
    fs::path(fig_dir, "heatmap_counts.png"),
    overwrite = TRUE
)

include_graphics(fs::path("figure", DOCNAME, "heatmap_counts.png"),
                 error = FALSE)

Past versions of heatmap_counts.png

Version	Author	Date
3feea4c	Luke Zappia	2020-05-27
9e20a9a	Luke Zappia	2020-05-08
7bd35b2	Luke Zappia	2020-05-06
54358bc	Luke Zappia	2020-05-06
19026df	Luke Zappia	2020-01-22

Chunk time: 0.04 secs

4.2.2 Log-counts

This is a heatmap of the log-count of interactions between cell types.

fig_dir <- here("docs", "figure", DOCNAME)
fs::dir_create(fig_dir)
fs::file_copy(
    fs::path(OUT_DIR, "heatmap_logcounts.png"),
    fs::path(fig_dir, "heatmap_logcounts.png"),
    overwrite = TRUE
)

include_graphics(fs::path("figure", DOCNAME, "heatmap_logcounts.png"),
                 error = FALSE)

Past versions of heatmap_logcounts.png

Version	Author	Date
3feea4c	Luke Zappia	2020-05-27
9e20a9a	Luke Zappia	2020-05-08
7bd35b2	Luke Zappia	2020-05-06
54358bc	Luke Zappia	2020-05-06
19026df	Luke Zappia	2020-01-22

Chunk time: 0.04 secs

4.2.3 count_network.txt

This file contains a count of the directional interactions between different cell types used to create the heatmaps.

network <- read_tsv(
    fs::path(OUT_DIR, "count_network.txt"),
    col_types = cols(
        SOURCE = col_character(),
        TARGET = col_character(),
        count  = col_double()
    )
)

skim(network)

Data summary

Name	network
Number of rows	16
Number of columns	3
_______________________
Column type frequency:
character	2
numeric	1
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
SOURCE	0	1	6	9	0	4	0
TARGET	0	1	6	9	0	4	0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
count	0	1	15.62	9.78	1	10.5	14	19	37	▂▇▂▁▂

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4.2.4 interactions_count.txt

This file contains a count of total number of interactions for each cell type.

interactions <- read_tsv(
    fs::path(OUT_DIR, "interactions_count.txt"),
    col_types = cols(
        X1 = col_character(),
        all_sum = col_double()
    )
)

skim(interactions)

Data summary

Name	interactions
Number of rows	4
Number of columns	2
_______________________
Column type frequency:
character	1
numeric	1
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
X1	0	1	6	9	0	4	0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
all_sum	0	1	62.5	24.8	41	42.5	58.5	78.5	92	▇▁▁▃▃

Chunk time: 0.06 secs

Summary

Parameters

This table describes parameters used and set in this document.

params <- list(
    
)
params <- toJSON(params, pretty = TRUE)
kable(fromJSON(params))

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Output files

This table describes the output files produced by this document. Right click and Save Link As… to download the results.

kable(data.frame(
    File = c(
        download_link("parameters.json", OUT_DIR),
        download_link("deconvoluted.txt", OUT_DIR),
        download_link("means.txt", OUT_DIR),
        download_link("pvalues.txt", OUT_DIR),
        download_link("significant_means.txt", OUT_DIR),
        download_link("dotplot.png", OUT_DIR),
        download_link("heatmap_counts.png", OUT_DIR),
        download_link("heatmap_logcounts.png", OUT_DIR),
        download_link("count_network.txt", OUT_DIR),
        download_link("interactions_count.txt", OUT_DIR)
    ),
    Description = c(
        "Parameters set and used in this analysis",
        "Deconvoluted output from CellPhoneDB",
        "Means output from CellPhoneDB",
        "P-values output from CellPhoneDB",
        "Significant means output from CellPhoneDB",
        "Dotplot plot from CellPhoneDB",
        "Counts heatmap plot from CellPhoneDB",
        "Log-counts heatmap plot from CellPhoneDB",
        "Count network output from CellPhoneDB",
        "Interactions count output from CellPhoneDB"
    )
))

File	Description
parameters.json	Parameters set and used in this analysis
deconvoluted.txt	Deconvoluted output from CellPhoneDB
means.txt	Means output from CellPhoneDB
pvalues.txt	P-values output from CellPhoneDB
significant_means.txt	Significant means output from CellPhoneDB
dotplot.png	Dotplot plot from CellPhoneDB
heatmap_counts.png	Counts heatmap plot from CellPhoneDB
heatmap_logcounts.png	Log-counts heatmap plot from CellPhoneDB
count_network.txt	Count network output from CellPhoneDB
interactions_count.txt	Interactions count output from CellPhoneDB

Chunk time: 0.08 secs

Session information

Session information

sessioninfo::session_info()

─ Session info ───────────────────────────────────────────────────────────────
 setting  value                       
 version  R version 4.0.0 (2020-04-24)
 os       macOS Catalina 10.15.7      
 system   x86_64, darwin17.0          
 ui       X11                         
 language (EN)                        
 collate  en_US.UTF-8                 
 ctype    en_US.UTF-8                 
 tz       Europe/Berlin               
 date     2020-11-24                  

─ Packages ───────────────────────────────────────────────────────────────────
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[1] /Users/luke.zappia/Documents/Projects/interaction-tools/renv/library/R-4.0/x86_64-apple-darwin17.0
[2] /private/var/folders/rj/60lhr791617422kqvh0r4vy40000gn/T/RtmpOqMPdA/renv-system-library
[3] /private/var/folders/rj/60lhr791617422kqvh0r4vy40000gn/T/RtmpqYMqtc/renv-system-library
[4] /private/var/folders/rj/60lhr791617422kqvh0r4vy40000gn/T/RtmpHPTw6W/renv-system-library

 P ── Loaded and on-disk path mismatch.

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