https://vals.github.io/single-cell-studies/
Notebook with example analysis and code for generating all figures in the manuscript A curated database reveals trends in single cell transcriptomics.
To obtain the data programatically, see instructions for different platforms below:
In [1]: import pandas as pd
In [2]: data = pd.read_csv('http://nxn.se/single-cell-studies/data.tsv', sep='\t')
In [3]: data.head()
Out[3]:
Shorthand DOI Authors ... tSNE H5AD location Isolation
0 Tietjen et al Neuron 10.1016/S0896-6273(03)00229-0 Ian Tietjen, Jason M. Rihel, Yanxiang Cao, Geo... ... NaN NaN Manual, LCM
1 Kurimoto et al NAR 10.1093/nar/gkl050 K. Kurimoto ... NaN NaN NaN
2 Esumi et al NResearch 10.1016/j.neures.2007.12.011 Shigeyuki Esumi, Sheng-Xi Wu, Yuchio Yanagawa,... ... No NaN NaN
3 Subkhankulova et al BMCGenomics 10.1186/1471-2164-9-268 Tatiana Subkhankulova, Michael J Gilchrist, Fr... ... NaN NaN NaN
4 Tang et al NMeth 10.1038/NMETH.1315 Fuchou Tang, Catalin Barbacioru, Yangzhou Wang... ... No NaN Pipetting (Manual picking)
[5 rows x 25 columns]
> library(tidyverse)
> data <- readr::read_delim('http://www.nxn.se/single-cell-studies/data.tsv', delim = '\t')
Parsed with column specification:
cols(
.default = col_character(),
Date = col_double(),
`Reported cells total` = col_number(),
`Panel size` = col_number(),
`Number of reported cell types or clusters` = col_double()
)
See spec(...) for full column specifications.
> head(data)
# A tibble: 6 x 25
Shorthand DOI Authors Journal Title Date `bioRxiv DOI` `Reported cells… Technique `Data location`
<chr> <chr> <chr> <chr> <chr> <dbl> <chr> <dbl> <chr> <chr>
1 Tietjen … 10.1… Ian Ti… Neuron Sing… 2.00e7 - 37 PCR NA
2 Kurimoto… 10.1… K. Kur… Nuclei… An i… 2.01e7 - 20 aRNA amp… GSE4309
3 Esumi et… 10.1… Shigey… Neuros… Meth… 2.01e7 - 8 Super SM… NA
4 Subkhank… 10.1… Tatian… BMC Ge… Mode… 2.01e7 - 12 PCR NA
5 Tang et … 10.1… Fuchou… Nat Me… mRNA… 2.01e7 - 5 Tang GSE14605
6 Sul et a… 10.1… J.-Y. … Procee… Tran… 2.01e7 - 48 aRNA amp… NA
# … with 15 more variables: `Panel size` <dbl>, Measurement <chr>, Organism <chr>, Tissue <chr>, `Cell
# source` <chr>, Contrasts <chr>, `Developmental stage` <chr>, `Number of reported cell types or
# clusters` <dbl>, `Cell clustering` <chr>, Pseudotime <chr>, `RNA Velocity` <chr>, PCA <chr>,
# tSNE <chr>, `H5AD location` <chr>, Isolation <chr>
$ gsutil ls gs://single-cell-studies | tail
gs://single-cell-studies/data_snapshot_2019-08-06T08:00:00.tsv
gs://single-cell-studies/data_snapshot_2019-08-07T08:00:01.tsv
gs://single-cell-studies/data_snapshot_2019-08-10T08:00:04.tsv
gs://single-cell-studies/data_snapshot_2019-08-11T08:00:01.tsv
gs://single-cell-studies/data_snapshot_2019-08-12T08:00:06.tsv
gs://single-cell-studies/data_snapshot_2019-08-13T08:00:01.tsv
gs://single-cell-studies/data_snapshot_2019-08-14T08:00:06.tsv
gs://single-cell-studies/data_snapshot_2019-08-15T08:00:00.tsv
gs://single-cell-studies/data_snapshot_2019-08-16T08:00:01.tsv
gs://single-cell-studies/data_snapshot_2019-08-17T08:00:01.tsv
$ gsutil cp gs://single-cell-studies/data_snapshot_2019-08-17T08:00:01.tsv .
Copying gs://single-cell-studies/data_snapshot_2019-08-17T08:00:01.tsv...
/ [1 files][249.2 KiB/249.2 KiB]
Operation completed over 1 objects/249.2 KiB.
$ ls
data_snapshot_2019-08-17T08:00:01.tsv