transpose single-cell cell x gene dataframe to gene x cell

Download the data

We can use command line to download the count matrix at ftp: https://ftp.ncbi.nlm.nih.gov/geo/series/GSE154nnn/GSE154763/suppl/

wget https://ftp.ncbi.nlm.nih.gov/geo/series/GSE154nnn/GSE154763/suppl/GSE154763_ESCA_normalized_expression.csv.gz -O ~/blog_data/GSE154763_ESCA_normalized_expression.csv.gz

# decompress the file
gunzip GSE154763_ESCA_normalized_expression.csv.gz

# this GEO matrix is cell x gene
# take a look by https://www.visidata.org/
vd  GSE154763_ESCA_normalized_expression.csv

Note, most GEO data I downloaded before is gene x cell, this one is cell x gene.

The data is normalized by logNormalization. Let’s revert back to the raw counts using https://github.com/immunitastx/recover-counts. It uses binary search to find the total count to make the smallest non-zero count one. In other words, it assumes the smallest non-zero value is a count of 1.

wget https://raw.githubusercontent.com/immunitastx/recover-counts/main/recover_counts_from_log_normalized_data.py

chmod u+x recover_counts_from_log_normalized_data.py

mamba install h5py numpy pandas

# by default, the script assumes the input is cell x gene. to specify gene x cell, turn
# on -t
./recover_counts_from_log_normalized_data.py -m 10000 -d CSV GSE154763_ESCA_normalized_expression.csv -o GSE154763_ESCA_counts.csv

vd GSE154763_ESCA_counts.csv

Now the smallest counts are 0s and some are 1s; fewer are > 1.

transpose the dataframe

Read into R:

library(tidyverse)

mat_df<- read_csv("~/blog_data/GSE154763_ESCA_counts.csv")

mat_df[1:5, 1:5]

#> # A tibble: 5 × 5
#>   index              `RP11-34P13.7` FO538757.2 AP006222.2 `RP4-669L17.10`
#>   <chr>                       <dbl>      <dbl>      <dbl>           <dbl>
#> 1 AAACGGGAGATCCTGT-5              0          1          0               0
#> 2 AAACGGGTCGGCCGAT-5              0          1          0               0
#> 3 AAAGATGGTAATCGTC-5              0          1          0               0
#> 4 AAAGCAACAGCCAATT-5              0          0          0               0
#> 5 AAAGCAACATACTCTT-5              0          1          0               0

mat_transposed_df<- mat_df %>%
        tidyr::pivot_longer(cols = -1, names_to = "genes") %>% 
        tidyr::pivot_wider(names_from = index, values_from = value)

mat_transposed_df[1:5, 1:5]

#> # A tibble: 5 × 5
#>   genes      `AAACGGGAGATCC…` `AAACGGGTCGGCC…` `AAAGATGGTAATC…` `AAAGCAACAGCCA…`
#>   <chr>                 <dbl>            <dbl>            <dbl>            <dbl>
#> 1 RP11-34P1…                0                0                0                0
#> 2 FO538757.2                1                1                1                0
#> 3 AP006222.2                0                0                0                0
#> 4 RP4-669L1…                0                0                0                0
#> 5 RP11-206L…                0                0                0                0

Now, the dataframe is transposed to gene x cell.

One can also use t() to transpose, but it is used for matrix in R, you will have to make the dataframe to matrix first

cells<- mat_df$index
mat2<- as.matrix(mat_df[, -1])

rownames(mat2)<- cells

mat2_transpose<- t(mat2)

mat2[1:5, 1:5]

#>                    RP11-34P13.7 FO538757.2 AP006222.2 RP4-669L17.10
#> AAACGGGAGATCCTGT-5            0          1          0             0
#> AAACGGGTCGGCCGAT-5            0          1          0             0
#> AAAGATGGTAATCGTC-5            0          1          0             0
#> AAAGCAACAGCCAATT-5            0          0          0             0
#> AAAGCAACATACTCTT-5            0          1          0             0
#>                    RP11-206L10.9
#> AAACGGGAGATCCTGT-5             0
#> AAACGGGTCGGCCGAT-5             0
#> AAAGATGGTAATCGTC-5             0
#> AAAGCAACAGCCAATT-5             0
#> AAAGCAACATACTCTT-5             0

make a Seurat object:

genes<- mat_transposed_df$genes
mat<- mat_transposed_df[, -1] %>%
        as.matrix()

rownames(mat)<- genes

library(Seurat)
obj<- CreateSeuratObject(counts = mat)

You see most of the work happens before creating the Seurat object. Once you have a Seurat object, you can use scCustomize to make a lot of nice visualizations.